Performance Enhancement of WLAN by Buffer Size Optimization using

National Conference on Advanced Computing and Communication Technology | ACCT-10|

Performance Enhancement of WLAN by Buffer Size Optimization using OPNET Rajan Vohra Dept. of Electronics and Communication Engineering Guru Nanak Dev University Amritsar, Punjab, India e-mail: [email protected]

R.S. Sawhney Lecturer, Dept. of Electronics and Communication Engineering Guru Nanak Dev University Amritsar, Punjab, India

Manju Sharma Lecturer, Dept. of Information Technology DAV Institute of Engineering and Technology Jalandhar, Punjab, India e-mail: [email protected]

Abstract: Wireless local area networks (WLANs) provide mobility and convenience to users, Communications may be cut off when mobile stations travel between cells. Wireless data connections have high bit error rates, low bandwidth and long delays. Therefore, it is very important to improve their loss performance. In this paper, our main contribution is to analyze and evaluate the throughput performance of WLANs. We describe OPNET implementations for fine- tuning the IEEE 802.11 layer related parameters. Through simulation we demonstrate that WLAN performance can be improved by tuning parameters such as buffer size, fragmentation threshold and request to send (RTS) thresholds. In this paper we emphasize on buffer size parameter. Customizing this parameter opposes to using the values specified in the standards will reduce delays, increase throughput and reduce load on nodes. Then finally the results are compiled to improve the performance of wireless local area networks.

business customers. Many wireless network standards have appeared up to now. The most known standards belong to the IEEE 802.11 family, which includes the popular 802.11b, the 802.11a and the 802.11g. Other standards, such as HIPERLAN and HIPERLAN/2, also had some importance but they didn’t find as much acceptance in market as the others. [1] This paper is focused on the studies of wireless Local Area networks in a simulated environment using OPNETTM IT Guru Academic IT Guru Academic Edition (2007). [2] In this paper the work has been further extended and reported Performance enhancement of WLAN. by customizing IEEE 802.11 layer related parameters has been presented. We demonstrate that WLAN performance can be improved by tuning parameters such as fragmentation threshold ,request to send (RTS) thresholds and buffer size. Customizing buffer size parameter oppose to using the values specified in the standards will reduce delays, and increase throughput reduce load on nodes.[3][4]

Keywords- WLAN, OPNET, Delay, Throughput, Load

I.

INTRODUCTION II.

In Wireless networks, computers communicate with one another through wireless media i.e. air instead of wired media, using short-range frequencies. Over the last few years, WLANs have gained strong popularity in a number of vertical markets, including health-care, retail, manufacturing, and warehousing, and academic areas. These industries have profited from the productivity gains of using hand-held terminals and notebook computers to transmit real time information to centralized hosts for data processing. Today WLANs are becoming more widely recognized as a generalpurpose connectivity alternative for a broad range of

OUR APPROACH

OPNET is a tool used to simulate the way networks run. In this paper, we have carried out a comparative study on delay, throughput and load in wireless LAN configured for video conferencing . We have chosen simulative toolOPNET for our research because of the several benefits it offers over the other contemporary tools available. OPNET provides the set of complete tools and a complete user interface for topology design and development. Another advantage of using OPNET is that it is being extensively 495

National Conference on Advanced Computing and Communication Technology | ACCT-10| used and there is wide confidence in the validity of the results it produces. OPNET enables realistic analysis of performance measures and the effectiveness of wireless network design techniques.

TABLE I.SIMULATION SCENARIO PARAMETERS Parameter(s)

III.

Address

SCENARIOS AND SETTINGS

Scenario 1

Scenario 2

Auto Assigned

Auto Assigned

11 Mbps

2 Mbps

WLAN bandwidth (bps) PHY Characteristics

In this section, we consider the case of two scenarios in which two independent wireless LANs workstations are connected. Here, these workstations are configured for the video application only as in figure 1. In scenario 1 workstations are operated at 11 Mbps data rate and in 2 scenario workstations are operated at 2 Mbps data rate. Two different scenarios and settings have been considered to optimize the network.

Direct Sequence

Slot time (s)

5.0 E-05

Packet ReceptionPower Threshold (W) Short Retry Limit (Attempts) Long Retry Limit (Attempts) Buffer size(bits)

7.33 E-14

7 4

Application supported profile Max Receive Lifetime (secs)

256000

64000

Vdo_pro

Vdo_pro 0.5

TABLE II. WIRELESS LAN TRAFFIC GENERATION PARAMETERS Duration

Attribute

Value End of simulation

Repeatability

Once at Start Time

Operation Mode

Serial (ordered)

Start Time (seconds)

uniform (100,110)

Inter-repetition (seconds) Number of Figure 1. WLAN Network video application configured for 11 Mbps and 2 Mbps

Time Repetitions

Repetition Pattern

Table I shows the parameters, which are used in different scenarios for simulation. Parameters shows that we use PHY characteristics as Direct sequence, buffer size(bits) 256 k and 64 k for scenario 1 and 2. Bandwidth used for the WLAN for scenario 1 and scenario 2 is 11Mbps and 2 Mbps.

IV.

Table II shows the wireless Lan traffic generation parameters for both the scenario. Operation Mode used in the scenario is serial(ordered) , start time (seconds) are uniform (100,110) ,repetition pattern in the scenarios is serial ,numbers of repitition is constant and rest of the parameters used are listed in table below:

constant (300) constant (30)

Serial

SIMULATION EVALUATION

A simulation model was developed using OPNET IT Guru Academic Edition (2007). OPNET 802.11b PHY module was used as a standard with maximum data rate up to 11 Mbps. IEEE 802.11b direct sequence was used. The packet size is default. In normal case when workstations are operated at 11 Mbps in scenario 1 and at 2 Mbps in scenario 2 respectively. Figure 2 shows the throughput at five mins is around 900 Kbps in case of 11 Mbps and around 575 Kbps in case of 2 Mbps.

496

National Conference on Advanced Computing and Communication Technology | ACCT-10|

Figure 2 WLAN throughput (bits/sec) for normal 11 and 2 Mbps scenarios Figure 4 WLAN delay(sec) for normal 11 Mbps and 2 Mbps scenarios

In figure (3-4) WLAN load(bits/sec) and WLAN delay(sec) is considered for both the scenarios.In case of figure 3 WLAN load(bits/sec) is 900 Kbps in scenario 1 and 575 Kbps in scenario 2. For WLAN delay (sec) we consider results at 2 mins in case of 11 Mbps scenario delay is 0.030 sec and 0.250 sec for 2 Mbps scenario after that delay remain constant for rest of time. We observe the result with the customized paramters of WLAN and compare with the previous scenarios, where customization is not done. Figure 5 shows the throughput when Wlan parameters of mob n(workstation) in scenario 2 is customized we have seen that there is drastic change in the throughput it is around 775 Kbps at 5 mins which is 200 Kbps more than previous results. Figure 6 shows that load on mob n(workstation) is decreased with the customized scenario. In the previous case load is 575 Kbps but with the customized scenario load is around 360 Kbps.

Figure 3 WLAN load(bits/sec) for normal 11 and 2 Mbps scenarios

497

National Conference on Advanced Computing and Communication Technology | ACCT-10|

Figure 5 WLAN throughput (bits/sec) for customized scenarios

Figure 7 WLAN delay(sec) for customized scenarios

In case of delay(sec) here also we observe the result at 2 mins. Delay for the customized scenario as comapre to previous result is decreased as shown in figure 7 value of the delay is 0.163sec which is 0.087sec less than previous scenario result. V.

CONCLUSIONS

This paper investigates the delay , throughput and load for normal and customized WLAN networks and their comparison thereof. Low throughput in case of normal WLAN network of around 575 Kbps in 2 Mbps scenario have been reported, while it is high in case of customized WLAN network of around 775 Kbps in 2 Mbps scenario. A high delay in case of normal WLAN network of the order of 0.250 sec have been reported, while it is lowest in case of customized WLAN network measuring 0.163sec respectively. Further, the results demonstrate measurements of load on mob n(workstation) for both the normal & customized networks. It is investigated that in case of normal network load on workstation (mob n) in 2 Mbps scenario is high around 575 Kbps in case of customized network in comparison to normal network it is low around 360 Kbps. In conclusion, it is reported that the customized network offer less load , delay and high throughput in comparison to normal network. Overall performance of the WLAN network with customized WLAN parameters can be enhanced.

Figure 6 WLAN load (bits/sec) for customized scenarios

498

National Conference on Advanced Computing and Communication Technology | ACCT-10| VI.

REFERENCES

Soliman A. Al-Wabie (2002), “The New Wireless Local Area Networks (WLAN’s) Standard”, University of Maryland. [2] IT Guru Academic Edition (2007), OPNET Technologies from http://www.opnet.com/university_program/itguru_acad emic_edition. [3] Wlan_lab_script_1_2 from http:// www.comnets.unibremen.de/~mms/wlan_lab_script_1_2.pdf [4] Walid Hneiti(2006),“Performance Enhancement of Wireless Local Area Networks” Amman Arab University for Graduate Studies, Jordan. [1]

499

An Approach of Service Discovery For AODV In MANET’s NehaSharma Guru Jambheshwar University of Science & Technology, Hisar, India Email: [email protected]

Miss Sunila Godara Asst. Professor Guru Jambheshwer University of Science & Technology, Hisar, India E-mail: [email protected]

Abstract

dynamically form a network to exchange

A mobile wireless ad hoc network is an

information without utilizing any pre-existing

autonomous collection of routers that have the

infrastructure. In these networks each mobile

ability to dynamically and rapidly form networks

node may act as a client, a server and a router.

without the use of any centralized network

MANET’s have been proposed for disaster relief

infrastructure using wireless communication

operations, police and military applications, and

technologies. Service discovery is a process

other situations where there is no deployed

allowing networked entities to advertise their

Communication infrastructure or the existing

services, query about services provided by other

infrastructure is not available.[5] We expect that

entities, select the most appropriately matched

one of the main uses of ad hoc networks will be

services and invoke the services. Service

for accessing services. In addition, MANET’s

discovery is an integral part of the ad hoc

are characterized by their highly dynamic, multi

networking to achieve stand alone and self

hop and infrastructure less nature. The dynamic

configurable communication network. The Ad

nature arises from the facts that (1) nodes are

Hoc

Vector AODV)

free to move, (2) the adverse channel conditions

routing protocol is one of the well-known and

of the wireless medium (e.g. multi path fading,

efficient on-demand MANET protocols. In this

shadowing, interference, collision, etc.) may be

paper we develop a simulation tool to achieve

effected, (3) nodes failure may occur because of

high level view showing the Routing Layer

the limited energy of the battery charged devices

working of AODV for mobile service Discovery.

and (4) nodes may frequently join or leave the

And we will discuss how to implement the service

network at will [15].

discovery in MANET’s using this simulation tool.

Service discovery in mobile ad hoc network is an

Keywords: Mobile Ad hoc Network, Service

important part of realization towards service

Discovery, Ad hoc on Demand vector protocol

access anytime, anywhere. The basic criteria for

On-Demand Distance

1.

evaluating a service discovery scheme is

INTRODUCTION

A mobile ad-hoc network (MANET’s) is a

effectiveness with which (1) service clients are

autonomous system of mobile nodes, a kind of a

able to find the service provider and (2) the

wireless network where the mobile nodes

service provider will successfully deliver the

500

requested service. Service discovery is defined as

each node parameters: at least should see how

a process allowing networked entities to:

many

nodes

bidirectional.

and

if

Then

their the

connection

application

is fills

automatically the nodes power, IP and name.

•

Advertise their services.

•

Query about services provided by other entities.

•

Select the most appropriately matched services.

•

Invoke the services.

In recent years, some protocols have been presented

to

support

service

discovery

specifically targeted at MANET’s environments. These ad hoc discovery specifically can be divided into two categories: centralized directory based protocols and distributed directory less protocols[11]. Fig:1

2. RELATED WORK There has been recent attention on service discovery in ad hoc network. In this paper we develop the simulator which achieve high level the routing layer of the AODV protocol for the service discovery and select the service that is matched to their requirement. Simulation can be defined as the process of designing a model of a real system and conducting experiments with this

The fig 1 shows how the Ad Hoc model can extend

an

infrastructure

wireless

network.

Without Ad Hoc, only station A could access the internet using the access point. But, if each station is able to forward the packets to the Access Point, then, B can access the internet, as well as C and the final user. 3. SERVICE DISCOVERY WITH AODV

model for the purpose of understanding the behavior of the system and/or evaluating various strategies for its control. A number of logical and practical steps have to be followed in any simulation study. In particular, three major phases of the process of building and using a simulation model can be identified: (i) definition of the model of the real system, (ii) collection of data from the real system, and (iii) definition of the experimental design and of the actual run of the

simulation

using

the

defined

model.

Automatic parameter setter: user can specify

This section describes our proposed scheme, AODV(Ad

hoc

On Demand

vector)

that

efficiently integrates the service and route discovery. The AODV is inspired from the Bellman Ford algorithm like DSDV. The principal changes is on demand. The ad hoc on demand vector (AODV) protocol [2] is routing protocol for mobile ad hoc network. Its author state that it offers “quick adaptation to dynamic link conditions, low processing and overhead,

low

network

memory

utilization

and

determines unicast routes to destinations within

its control. The simulator is one of the important

the ad hoc network”.

tool for the service discovery. We identified

AODV is one of the prominent on demand

OPNET,

routing protocols for MANET’s and based on

OMNeT++[4] as the best possible candidates to

some ideas sketched by Koodli and Perkins I an

run simulation studies in MANETs. These

Internet draft, we have extended the AODV

simulator as representative of the state-of-the-art

protocol to perform service discovery[6]; we call

in the field of simulators for mobile ad hoc

this extension AODV-SD.

networks. But we design the simulation tool for

In order to perform service discovery we have

the AODV protocol for the service discovery.

the RREQ and RREP messages. A service table

That is not only for the AODV protocol. That is

contains the information about services

for the other protocols in the MANET’s. But we

the

GloMoSimQualNet,

NS-2

and

current node provides as well as information

study only for AODV protocol.

about service provided by other

nodes;

With this in mind, we identify several specific

whenever a node require a service , it perform a

questions that simulator design should considers

lookup in this table. Each row in a service table

and strive to answer to ensure that their results

contains the service identifier, its IP address, a

are meaningful as possible:

lifetime and a list of attributes that varies

•

What simulator parameter used. How

according to the type of service. When one node

sensitive are the results to changes in

needs to send a message to another node that is

the parameters.

not its neighbor it broadcast a Route (RREQ)

•

What is the range of the node.

message. The route request contains several key

•

What is the delay time.

bits of information: the source, the destination,

•

What is the size of the packets,

the lifespan of the message and sequence number

•

What is the status of the receiver.

which serve a unique id. When one node has a

In that we make the many modules. That uses the

route to another nod and replies to the RREQ by

Interactive user interface (user can change nodes

sending out a RREP.

arrangement during the simulation) Drag & Drop

4. SIMUATOR DESIGN

Nodes creation with parameters to specify IP,

Simulation is the research tool of choice for a

name and signal strength. And searching nodes

majority of the

ad hoc network

by their name. That have the three minimum

(MANET) community [13]. However, while the

nodes and give the status of the service discovery

use of simulation has increased, the credibility of

received data. How much of data we send, how

the simulation results has decreased. The

much of packet we send and how much of the

simulator can be defined as the process of

data we received. In that we give the range to the

designing a model of a real system and

mobile nodes.

mobile

conducting experiments with this model for the

5. SIMULATOR ENVIRONMENT

purpose of understanding the behavior of the

Simulation environments are an important tool

system and/or evaluating various strategies for

for the evaluation of new concepts in networking.

The study of mobile ad hoc networks depends on

communication overhead and improved service

understanding protocols from simulations, before

finding.

these protocols are implemented in a real-world

Table1. Experimental Perameters

setting. All mobile nodes in the network are

Duration

2000s

configured to run Ad hoc On Demand distance

Perameters

3000 * 3000

vector protocol (AODV) or Temporary Ordered

Min Neighbors

3

Routing Algorithm (TORA) or Optimized Link

No. of Nodes

Many no of Nodes

State Routing (OLSR) protocols and multiple

Transmission Range

1000m

FTP sessions.

Node Placement

Random

Mobility

Random Waypoint

6. SIMULATION RESULT In the first set of experiment, we try to capture the effect of the number of servers when the number of user is kept constant. Control message overhead of On demand anycast protocols are found to be very sensitive to the number of server. In multicasting, all the server receive the request and then all of them have to reply. End to end delay for successfully service discovery improve with the increasing number of servers. In this paper we have added the hierarchical service discovery. The hierarchical service advertisement is started when a service is added to a machine located within the ad hoc environment and the cluster are aware of the exact details of the service.[9] Hierarchical service discovery means that client

7. CONCLUSION MANET’s have attracted extraordinary attention from the research community in recent years, yet civilian, mass applications remains elusive. Efficient service discovery is of the key issues that need to be resolved for the acceptance of MANET’s. In this paper we develop the simulator for the AODV protocol that is used on the network layer. The service selection strategy into an AODV protocol is based on service discovery protocol for MANET’s. The result sows that to find the best path for the service discovery. That uses the hierarchical service discovery. That also give the status of the services and received data.

first searches for the required service within the same cluster. If the queried service is shown to be suitable for waiting task is selected and discovery process is terminated. But if suitable service could not be found, search of the other

ACKNOLEDGEMENT This work was supported by Miss Sunila Godara. I would like to thanks to her for the continual encouragement, help and guidance throughout the work.

clusters using hierarchical service discovery will

REFERENCES

performed .in addition, searching service in all adjacent clusters is done parallel and the number

[1]. Carlos Henrique Pereira Augusto and Jose Ferreira de Rezende “An Adaptive Approach to

of moving packet between clusters in a few then

Service Discovery in Ad Hoc Networks” Augusto,

this approach also is between clusters are a few

C.H.P.,

then this approach also is expected to decrease

International

de Rezende,

J.F.,

Federation

2006, for

in

IFIP

Information

Processing, Volume 211, ed. PujoUe, G., Mobile

and Wireless Communication Networks, (Boston:

[11]. Sccot L Rosen, Jon A. Stin “A MANET’s

Springer), pp. 61-75

simulation tool to study algorithm for generating

[2]. E. M. Royer and C-K Toh, “A Review of Current

propagation maps” proceding of the 2006 winter simulation conference IEEE pp 2219-2224

Routing Protocols for Ad-Hoc Mobile Wireless Networks,” IEEE Person. Commun., Vol. 6, No.

[12]. Sotirios E. Athanaileas, Christopher N. Ververidis

2, Apr. 1999.

and George C. Polyzos “Optimized Service

[3]. Francoise Sailhan, Valerie Issarny “Scalable Service

Discovery

for

Selection for MANETs using an AODV-based

MANET”

Service

Discovery

Protocol”

sixth

annul

processing of the 3rd IEEE Int’l Conf. on

Mediterranean Ad Hoc Networking Workshop,

Pervasive

Corfu, Greece, June 12-15,2007, 9-16

Computing

and

communication

(PerCom 2005) 8-12 March pp 235-244

[13]. Stuart Kurkowski, Tracy cap, Michael Calagrosso

[4]. Gianni A. Di Caro “Analysis of simulation environment

for

mobile

ad

hoc

“Manet Simulation Studies: The Incredibles”

network”

conference on MobileComputing and Review,

Technical report 2003

[5]. Jerry

volume 9, number 4, pp 50-61 “A

[14]. Ulas C. Kozat and Leandros Tassiuls “Network

Comprehensive Service Discovery Solution for

Layer Support For Service Discovery in Mobile

Mobile Ad Hoc Networks” Mobile Network and

ad Hoc Network” INFOCOM 2003. Twenty-

Application vol. 10, 423-434,2005

second Annual Joint Conference on the IEEE

Tyan,

Q

Usay

H.

Mahmoud

Computer and Communications Societies. IEEE

[6]. J. Antonio Garcia-Macias, Dante Arias Torres “Service Discovery in Mobile Ad-Hoc Networks:

vol. 3, 30 March-3 April 2003 pp 1965-1975

Better at the Network Layer?” Proceedings of the

[15]. U.C. Kozat, L. Tassiulas “Service discovery in

Parallel

mobile ad hoc networks: an overall perspective on

Processing Workshops (ICPPW’05) 2005 IEEE

2005

International

Conference

on

architectural choices and network layer support

14-17 June pp 452-457

issues” Ad Hoc Networks vol. 2 Number 1, Jan (2004) 23–44

[7]. Kavita Taneja , R.B.Patel “Mobile Ad hoc

[16]. Vincent lenders, Martin May , Bernhard Platters

Networks: Challenges and Future” Proceedings of National

Conference

on

Challenges

&

“ Service Discovery in Mobile Ad Hoc Network :

Opportunities in Information Technology (COIT-

A Field Theoretic Approach” IEEE Vol. 1, Issue

2007) RIMT-IET, Mandi Gobindgarh. March 23,

3, Sep. 2005, pp 120-130

2007,133-135

[17]. Zhuoyao Zhang, Weiwei Sun,Weiyu Chen, Bo

[8]. N.Ververdis, C. Polyzos “Service Discovery for

Peng “An Integrated Approach to Service

Mobile Ad Hoc Network: A Survey of Issues and

Selection in Mobile Ad Hoc Networks” Wirless

Techniques”

Communications,

Communication

Surveys

&

Networking

and th

Mobile

Tutorials, IEEE Vol. 10, Issue 3, Third Quarter

Computing, 2008. WiCOM ’08. 4 International

2008 pp 30-45

Conference On 12-14 Oct 2008 pp 1-4

[9]. S. Murthy and J. J. Garcia-Luna-Aceves, “An Efficient

Routing

Protocol

for

Wireless

Networks,” ACM Mobile Networks and App. J., Special

Issue

on

Routing

in

Mobile

Communication Networks, Oct. 1996, pp. 183–97

[10]. Seyed Amin Hosseini Seno, Rahmat Budiarto, Tat-Chee Wan “Survey and new Approach in Service Discovery and Advertisement for Mobile Ad hoc Networks” IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.2, February 2007, 275-284

504

National Conference on Advanced Computing and Communication Technology

ROUTING PROTOCOLS IN WIRELESS SENSOR NETWORKS: CLASSIFICATION AND COMPARISON *Shivani Garg, **Rajni Meelu *CSE/ IT Department, NCCE Israna, Panipat **ECE Department, NCCE Israna, Panipat nodes, which are spatially distributed in order to perform an application-oriented global task. These nodes form a network by communicating with each other either directly or through other nodes. One or more nodes among them will serve as sink(s) that are capable of communicating with the user either directly or through the existing wired networks. The primary component of the network is the sensor, essential for monitoring real world physical conditions such as sound, temperature, humidity, intensity, vibration, pressure, motion, pollutants etc. at different locations. The tiny sensor nodes, which consist of sensing, on board processor for data processing, and communicating components, leverage the idea of sensor networks based on collaborative effort of a large number of nodes [22]. Figure 1 shows the structural view of a sensor network in which sensor nodes are shown as small circles. Each node typically consists of the four components: sensor unit, central processing unit (CPU), power unit, and communication unit. They are assigned with different tasks. The sensor unit consists of sensor and ADC (Analog to Digital Converter). The sensor unit is responsible for collecting information as the ADC requests, and returning the analog data it sensed. ADC is a translator that tells the CPU what the sensor unit has sensed, and also informs the sensor unit what to do. Communication unit is tasked to receive command or query from and transmit the data from CPU to the outside world. CPU is the most complex unit. It interprets the command or query to ADC, monitors and controls power if necessary, processes received data, computes the next hop to the sink, etc. Power unit supplies power to sensor unit, processing unit and communication unit. Each node may also consist of the two optional components namely Location finding system and Mobilizer. If the user requires the knowledge of location with high accuracy then the node should pusses Location finding system and Mobilizer may be needed to move sensor nodes when it is required to carry out the assigned tasks. Instead of sending the raw data to the nodes responsible for the fusion, sensor nodes use their processing abilities to locally carry out simple computations and transmit only the required and partially processed data. The

ABSTRACT The recent advances and the convergence of micro electro-mechanical systems technology, integrated circuit technologies, microprocessor hardware and nano technology, wireless communications, Ad-hoc networking routing protocols, distributed signal processing, and embedded systems have made the concept of Wireless Sensor Networks (WSNs). Sensor network nodes are limited with respect to energy supply, restricted computational capacity and communication bandwidth. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. To prolong the lifetime of the sensor nodes, designing efficient routing protocols is critical. Even though sensor networks are primarily designed for monitoring and reporting events, since they are application dependent, a single routing protocol cannot be efficient for sensor networks across all applications. In this paper, we analyze the design issues of sensor networks and present a classification and comparison of routing protocols. This comparison reveals the important features that need to be taken into consideration while designing and evaluating new routing protocols for sensor networks. Keywords: Sensor networks, Design issues, Routing protocols, Applications. 1 INTRODUCTION Sensor networks have emerged as a promising tool for monitoring (and possibly actuating) the physical world, utilizing self organizing networks of battery-powered wireless sensors that can sense, process and communicate. In sensor networks, energy is a critical resource, while applications exhibit a limited set of characteristics. Thus, there is both a need and an opportunity to optimize the network architecture for the applications in order to minimize resource consumed. The requirements and limitations of sensor networks make their architecture and protocols both challenging and divergent from the needs of traditional Internet architecture. A sensor network [1][4] is a network of many tiny disposable low power devices, called

505

National Conference on Advanced Computing and Communication Technology sensor nodes not only collect useful information such as sound, temperature, light etc., they also play a role of the router by communicating through wireless channels under battery-constraints [1].

In the recent past, wireless sensor networks have found their way into a wide variety of applications and systems with vastly varying requirements and characteristics [6][8]. The sensor networks can be used in Disaster Relief, Emergency Rescue operation, Military, Habitat Monitoring, Health Care, Environmental monitoring, Home networks, detecting chemical, biological, radiological, nuclear, and explosive material etc. as summarized in table 1. Table 1: Some applications for different areas.

Figure 1: Structural view of sensor network Sensor network nodes are limited with respect to energy supply, restricted computational capacity and communication bandwidth. The ideal wireless sensor is networked and scaleable, fault tolerance, consume very little power, smart and software programmable, efficient, capable of fast data acquisition, reliable and accurate over long term, cost little to purchase and required no real maintenance. The basic goals of a WSN are to: (i) determine the value of physical variables at a given location, (ii) detect the occurrence of events of interest, and estimate parameters of the detected event or events, (iii) classify a detected object, and (iv) track an object. Thus, the important requirements of a WSN are: (i) use of a large number of sensors, (ii) attachment of stationary sensors, (iii) low energy consumption, (iv) self organization capability, (v) collaborative signal processing, and (vi) querying ability. The remainder of this paper is organized as follows. Section 2 contains applications of sensor networks, section 3 contains classification of routing protocols, section 4 contains design issues of routing protocols, section 5 conations comparison of routing protocols, and finally section 6 conations conclusion.

3CLASSIFICATION OF ROUTING PROTOCOLS The design space for routing algorithms for WSNs is quite large and we can classify the routing algorithms for WSNs in many different ways. Routing protocols are classified as node centric, datacentric, or location-aware (geo-centric) and QoS based routing protocols. Most Ad-hoc network routing protocols are node-centric protocols where

2 APPLICATIONS OF SENSOR NETWORKS

506

National Conference on Advanced Computing and Communication Technology destinations are specified based on the numerical addresses (or identifiers) of nodes. In WSNs, nodecentric communication is not a commonly expected communication type. Therefore, routing protocols designed for WSNs are more data-centric or geocentric. In data-centric routing, the sink sends queries to certain regions and waits for data from the sensors located in the selected regions. Since data is being requested through queries, attribute based naming is necessary to specify the properties of data. Here data is usually transmitted from every sensor node within the deployment region with significant redundancy. In location aware routing nodes know where they are in a geographical region. Location information can be used to improve the performance of routing and to provide new types of services.In QoS based routing protocols data delivery ratio, latency and energy consumption are mainly considered. To get a good QoS (Quality of Service),the rooting protocols must posses more data delivery ratio, less latency and less energy consumption. Routing protocols can also be classified based on whether they are reactive or proactive. A proactive protocol sets up routing paths and states before there is a demand for routing traffic. Paths are maintained even there is no traffic flow at that time. In reactive routing protocol, routing actions are triggered when there is data to be sent and disseminated to other nodes. Here paths are setup on demand when queries are initiated. Routing protocols are also classified based on whether they are destination-initiated (Dst-initiated) or source-initiated (Src-initiated). A source-initiated protocol sets up the routing paths upon the demand of the source node, and starting from the source node. Here source advertises the data when available and initiates the data delivery. A destination initiated protocol, on the other hand, initiates path setup from a destination node. Routing protocols are also classified based sensor network architecture. Some WSNs consist of homogenous nodes, whereas some consist of heterogeneous nodes. Based on this concept we can classify the protocols whether they are operating on a flat topology or on a hierarchical topology. In Flat routing protocols all nodes in the network are treated equally. When node needs to send data, it may find a route consisting of several hops to the sink. A hierarchical routing protocol is a natural approach to take for heterogeneous networks where some of the nodes are more powerful than the other ones. The hierarchy does not always depend on the power of nodes. In Hierarchical (Clustering) protocols different nodes are grouped to form clusters and data from nodes belonging to a single cluster can be

combined (aggregated).The clustering protocols have several advantages like scalable, energy efficient in finding routes and easy to manage. 4 DESIGN ISSUES OF ROUTING PROTOCOLS Initially WSNs was mainly motivated by military applications. Later on the civilian application domain of wireless sensor networks have been considered, such as environmental and species monitoring, production and healthcare, smart home etc. These WSNs may consist of heterogeneous and mobile sensor nodes, the network topology may be as simple as a star topology; the scale and density of a network varies depending on the application. To meet this general trend towards diversification, the following important design issues [8] of the sensor network have to be considered. 4.1 Fault Tolerance Some sensor nodes may fail or be blocked due to lack of power, have physical damage or environmental interference. The failure of sensor nodes should not affect the overall task of the sensor network. This is the reliability or fault tolerance issue. Fault tolerance is the ability to sustain sensor network functionalities without any interruption due to sensor node failures. 4.2 Scalability The number of sensor nodes deployed in the sensing area may be in the order of hundreds, thousands or more and routing schemes must be scalable enough to respond to events. 4.3 Production Costs Since the sensor networks consist of a large number of sensor nodes, the cost of a single node is very important to justify the overall cost of the networks and hence the cost of each sensor node has to be kept low. 4.4 Operating Environment We can set up sensor network in the interior of large machinery, at the bottom of an ocean, in a biologically or chemically contaminated field, in a battle field beyond the enemy lines, in a home or a large building, in a large warehouse, attached to animals, attached to fast moving vehicles, in forest area for habitat monitoring etc. 4.5 Power Consumption Since the transmission power of a wireless radio is proportional to distance squared or even higher order in the presence of obstacles, multi-hop routing will consume less energy than direct

507

National Conference on Advanced Computing and Communication Technology communication. However, multi-hop routing introduces significant overhead for topology management and medium access control. Direct routing would perform well enough if all the nodes were very close to the sink [12]. Sensor nodes are equipped with limited power source ( Encrypt -> Cipher  Decrypt -- > Plain Figure 1. An Encryption/Decryption Pattern

III. TWO WAYS OF ENCRYPTION PROCESS The two ways of going about this process are conventional (or symmetric) encryption and public key (or asymmetric) encryption. Private Key encryption also referred to as conventional, single-key or symmetric encryption. Each system uses a key which is shared among the sender and the recipient. This key has the ability to encrypt and decrypt the data. That key is known as secret key . AES, DES, RC5, RC6 and Blowfish use symmetric key algorithms. Public key encryption is referred to as asymmetric encryption.. Each encryption/decryption process requires at least one public key and one private key. The public key can be known by anyone and used to encrypt data that will be sent to the owner. Once the message is encrypted, it can only be decrypted by the owner of the private key. RSA use asymmetric key algorithm.

•

•

•

IV.CHARACTERSTICS OF ENCRYPTION 1) Confidentiality - the information cannot be understood by anyone for whom it was unintended 2) Integrity - the information cannot be altered in storage or transit between sender and intended receiver without the alteration being detected

Message Encryption. This is the traditional use of cryptography. Blocks of text are encrypted as units. Digital Signatures. Authenticating who sent a message is often useful. In the public key scheme, the secret decryption key can be used to encrypt, allowing the non-secret encryption key to be used to decrypt. Since only the secret key holder is presumed to have the secret key, only he could have encrypted/signed the message. Stream Encryption. Some encryption schemes increase security by varying the key for separate packets of a long message. Often, the key is computed from previous packets. As long as all packets ultimately arrive, this works, but if packets are lost, subsequent packages are not decryptable. Various synchronizations can be used to minimize the loss. File Encryption. Various encryption algorithms have been applied to files and databases. The main issue here is one of packaging the encryption naturally into normal file access and managing keys when a key may need to be used for a long time after it was originally used to encrypt. Electronic Cash. Cryptography is used to create unforgeable "electronic cash" tokens. Tokens include a serial number that can be decrypted (and saved) by the bank accepting the token. Reuse (illegitimate) of the token allows the user to be identified because the serial number will have already been seen in a previous transaction. VI. SURVEY OF SYMMETRIC KEY ENCRYPTION ALGORITHMS

3) Non-repudiation - the creator/sender of the information cannot deny at a later stage his or her intentions in the creation or transmission of the information

In secret key encryption, or symmetric Key Algorithm the sender and receiver must have a single shared key set up in advance and kept secret from all other parties. The sender and the receiver use the same key for encryption and decryption. An overview of symmetric key encryption is illustrated in Figure2. The Visual Works security library includes two types of secret key ciphers: block and stream. Block ciphers encrypt one full, fixed-size block at a time, and stream ciphers encrypt one byte at a time.

4) Authentication -the sender and receiver can confirm each others identity and the origin/destination of the information V.USE OF ENCRYPTION Encryption can be used in several different ways as summarized below. For example, if encryption is used

515

National Conference on Advanced Computing and Communication Technology The data transformation process for Pocket Brief uses the Blowfish Algorithm for Encryption and Decryption, respectively. Blowfish is a symmetric block cipher that can be effectively used for encryption and safe guarding of data. It takes a variable-length key, from 32 bits to 448 bits, making it ideal for securing data. Blowfish was designed in 1993 by Bruce Schneier as a fast, free alternative to existing encryption algorithms. Blowfish is unpatented and license-free, and is available free for all uses. Blowfish is a variable-length key block cipher. It is suitable for applications where the key does not change often, like a communications link or an automatic file encryptor. It is significantly faster than most encryption algorithms when implemented on 32bit microprocessors with large data caches .Blowfish was designed to work as a drop-in replacement for DES encryption. Blowfish encrypts 8-byte blocks, and it takes a variable-length key from 32 bits to 448 bits.

Secret Key Encryption

Block

DES

AES

Stream

Blowfish

RC5

Figure 2. An overview of Symmetric Key

• DES Data Encryption Standard DES originated at IBM in 1977 and was adopted by the National Bureau of Standards (now NIST) and the U.S. Department of Defense. It was a widely-used method of data encryption using a private (secret) 56-bit key that was judged so difficult to break by the U.S. government that it was restricted for exportation to other countries. For each given message, the key is chosen at random from among this enormous number of keys. Like other private key cryptographic methods, both the sender and the receiver must know and use the same private key. DES was built so strong that it has never been successfully “algorithmically broken”, however what is called a “brute force attack” or “key exhaustion” can eventually compromise a given encrypted message. A brute force attack attempts to decrypt an encrypted message by starting with key “0” and increasing to 2 to the 56th . Each key is used to decrypted the message with the results being compared to ASCII data

• Stream cipher implementations Stream ciphers operate on a single byte at a time. Using a key as seed, a random number generator (RNG) creates a key stream, which is used to encrypt the data. The same key stream is generated to decrypt the stream. Visual Works provides implementations of the popular RC5 stream cipher. • RC5 RC5 uses a variable-length key up to 256 bytes. RC5 is a parameterized symmetric encryption algorithm. RC5 stands for “Rivest Cipher”, or alternatively, “Ron’s Code” RC5 encryption algorithm is a fast symmetric block cipher suitable for hardware or software implementations. A novel feature of RC5 is the heavy use of data dependent rotations.RC5 has a variable word size, a variable number of rounds and a variable length secret key. The encryption and decryption algorithms are simple. RC5 is word oriented. Beyond that, additional bytes are ignored. The algorithm generates a key stream from the key, which is then used for encryption and decryption.

• AES Advanced Encryption Standard Advanced Encryption Standard (AES) is the National Institute of Standards (NIST) cipher based on the Rijndael algorithm. It is intended as a replacement for the DES standard . AES encrypts 16-byte blocks. It is an encryption algorithm securing sensitive but unclassified material by U.S. Government agencies and, as a likely consequence, may eventually become the de facto encryption standard for commercial transactions in the private sector. The specification called for a symmetric algorithm (same key for encryption and decryption) using block encryption of 128 bits in size, supporting key sizes of 128, 192 and 256 bits.It is better then DES. •

VII. CONCLUSION Encryption is to make the data illegible for everyone else except those specified. Secure encryption over the network is the key to confidence for people wanting to protect their privacy, or doing business online. Encryption is the process of obscuring information to make it unreadable without special knowledge. It is also referred a scrambling. Various symmetric encryption algorithms have been studied .After study of all symmetric key algorithms we conclude that RC5 encryption scheme is faster than any other symmetric keys. It is suitable for hardware or software implementations. The encryption and decryption

Blowfish

516

National Conference on Advanced Computing and Communication Technology algorithms are quite simple. RC5 is word oriented. RC5 has a variable word size, a variable number of rounds and a variable length secret key. RC5 algorithm is quite easy to implement in different languages like ‘C’, ‘Java’, and ‘C++’. It used low circuit size, low power, low memory and high performance designs. REFERENCES [1] [2] [3]

en.wikipedia.org/wiki/RC5 http://people.csail.mit.edu/rivest/Rivest-rc5rev.pdf http://people.csail.mit.edu/rivest/RivestTheRC5EncryptionAlgo rithm [4] http://klabs.org/richcontent/MAPLDCon03/papers/p/p12_chital wala_p.doc [5] http://www.slideshare.net/serngawy/rc4rc5 [6] http://www.networksorcery.com/enp/data/rc5.htm [7] http://bwrc.eecs.berkeley.edu/classes/cs252/Projects/Reports/yu _olson.pdf [8] http://en.wikipedia.org/wiki/Cellular_Message_Encryption_Alg orithm [9] http://www.drdobbs.com/security/184409480 [10] http://www.mycrypto.net/encryption/crypto_algorithms.html [11] http://msdn.microsoft.com/enus/library/bb416357.aspx

517

National Conference on Advanced Computing and Communication Technology

Performance Evaluation of MANET with Variable Node Mobility and Number of Nodes Gursimranjit Singh

Ravinder Singh Sawhney

Dept. of Electronics Technology, Guru Nanak Dev University Amritsar, Punjab, India email: [email protected]

Sr. Lecturer, Dept. of Electronics Technology, Guru Nanak Dev University Amritsar, Punjab, India email: [email protected]

Abstract – MANET (Mobile Ad hoc Network) is the collection of autonomous mobile nodes (users) that work in decentralized manner. The nodes are mobile therefore the topology of the network keeps on changing. The network making, breaking and message execution all are done by the mobile nodes themselves. To perform these operations efficiently, protocols are made. This paper evaluates the performance of the various metrics like throughput and routing overhead of MANET protocols like AODV, DSR, OLSR and TORA. The different scenarios are made and simulated on OPNET to show the performance of the protocols. Customizing the different parameters of the mobile nodes gave different sort of performances.

that gives huge variety of parameters for the evaluation. The OPNET even gives the options for customizing the single node at the modular level. A user can change the working of node at the hardware level of the node.

II.

The OPNET tool used in this work performs the comparative study of the four protocols in the metrics routing overhead and throughput. These metrics are studied while varying the mobile node speed and number of traffic nodes. For each of the metric, a scenario is made for each varying parameter. We have used the OPNET simulation tool for having great deal in user interface. This tool provides the parallelism in the simulation, which is the very important tool while simulating wireless networks [5]. The very impressive tool that this simulator uses is the ability to monitor and execute several scenarios at concurrent time [4]. OPNET enables realistic analysis of performance measures and the effectiveness of wireless network design techniques. It is fastest discrete event simulation engine [5].

Keywords – MANET, AODV, DSR, TORA, OLSR and OPNET

I.

SIMULATION APPROACH

INTRODUCTION

There have been many recent performances and advancements in computer and wireless communications technologies and with the help of these recent performances, advanced mobile wireless computing is expected to see increasingly widespread its use and application [1]. A mobile ad-hoc network, also called MANET is a sort of wireless ad-hoc network. It is a self-configuring network of mobile routers which is connected by wireless links. In a MANET, decentralized environment is there to make the system autonomous [2]. Each mobile node is the mobile router for the next node. In today’s busy world, MANET seeks great future, as there is no need of fixed infrastructure needed for these networks [1]. Various protocols are developed for the proper and efficient functioning of the network. In this paper, the performance of these protocols is evaluated based on different metrics and parameters.

III.

SIMULATION SCENARIOS

The campus network of 1500*1500 meters is made for the network setup. The scenarios made for the simulation are shown up in the table 1. TABLE 1 SCENARIOS FOR SIMULATION

The main purpose of this paper is to evaluate the performance of MANET protocols for different mobile node speeds, different mobile node numbers. The simulation tool used is the OPNET version 14.0

519

Scenario

No. of nodes

Speed(m/s)

1

5

10

2

5

25

National Conference on Advanced Computing and Communication Technology 3

20

10

4

20

25

5

50

10

6

50

25

The above scenarios are applied on each of the four protocols and after all this, there are 24 scenarios which are made to compare.

Fig 2 Routing Overhead of DSR in bits/sec

A. Routing Overhead In the case of routing overhead, the DSR outperforms all the other protocols having lowest stray traffic as in figure 2. The highest routing overhead is used by OLSR as shown in figure 3.

Fig. 1 The 5 node scenario for AODV protocol.

The mobile nodes and WLAN server both are made to transmit at 11 mbps and power of 0.005 watts. The pause time used for mobile nodes is 100 seconds. IV.

RESULT EVALUATION

With the above said parameters, the simulation was done for each of the scenarios. The evaluation metrics are studied one by one to give detailed effective results

Fig 3 Routing overhead of OLSR in bits/sec

B. Throughput As the throughput is the ratio of the total amount of data that reaches the receiver to the time it takes, theoretically throughput is concerned with traffic received by the receiver. The simulation results goes right with theoretical results as shown in figure 4

520

National Conference on Advanced Computing and Communication Technology circumstances like mobility speed and traffic sources. Concluding the performance of the protocols, OLSR performs best in the case of Throughput whereas the DSR performs best in the case of Routing overhead.

VI.

REFERENCES

[1] - C. K. Toh, “Ad Hoc Mobile Wireless Networks”, Prentice Hall Publishers, 2002 [2] – Xlang Yang Li, “Wireless Ad Hoc and Sensor Networks”, Cambridge University Press, 2008 [3] – Miguel

Garcia, Hugo Coll, Diana Bri, Jaime Lloret “Using MANET protocols in

Fig 4 Throughput of 5 node network (in bits/sec)

Also in case of larger number of nodes, the network shows same results. The throughput of 20 node network is shown in figure 5.

Wireless Sensor and Actor Networks” The Second International Conference on Sensor Technologies and Applications, IEEE 2008. [4] - Luc Hogie, Pascal Bouvry, “An overview of MANET simulation”, 2005 [5] - http://www.opnet.com/solutions/network_rd/ modeler.html

Fig 5 Throughput of 20 node network (in bits/sec)

In the case of 50 node network, the result remains same except the TORA giving the count to infinity problem giving a hang up to the system.

V.

CONCLUSION

In this paper, the evaluation of four mobile ad hoc protocols is done which all belongs to the four different categories of protocols. Two of them were reactive protocols, one of them was proactive and one was of hybrid protocol. The metrics used in evaluating i.e. Throughput tells the reliability of the protocol, whereas the Routing Overhead tells the efficient use of network resources by the protocol. All the protocols perform differently under different

521

National Conference on Advanced Computing and Communication Technology

Performance Analysis of VoIP over WLAN(802.11b) using different Voice Encoding Schemes Gurinder Singh Dhaliwal Dept. of Electronics Technology, Guru Nanak Dev University Amritsar, Punjab, India email: [email protected]

Ravinder Singh Sawhney Sr. Lecturer, Dept. of Electronics Technology, Guru Nanak Dev University Amritsar, Punjab, India email: [email protected]

telephony services over an IP network. This means that instead of using the traditional PSTN, an IP network is used to carry digitized voice in discrete packets based on the H.323 specification. This specification for transmitting multimedia (voice, video, and data) across a packet-based network does not provide quality of service (QoS). Ultimately, VoIP strives to provide the efficiency of a packetswitching network while rivaling the voice quality of a circuit-switched network[2].

Abstract – VoIP is one of the fastest growing Internet applications now days. Although IP was originally designed for data networking, its success has led to its adaptation to voice networking. This paper evaluates the performance of the various metrics like throughput, voice packet end-to-end delay and MAC delay of a WiFi based VoIP network. Results are obtained by simulating these metrics for different Encoding Schemes (G.711,G.726,G.728,G.729, G.723). The different scenarios are made and simulated on OPNET to show the performance of the Encoding Schemes. Customizing the different parameters of the mobile and fix nodes gave different sort of performances.

The main purpose of this paper is to evaluate the performance of the various metrics like throughput, voice packet end-to-end delay and MAC delay of a Wi-Fi based VoIP network. Results are obtained by simulating these metrics for different Encoding Schemes (G.711,G.726,G.728,G.729, G.723). The different scenarios are made and simulated on OPNET to show the performance of the Encoding Schemes. Customizing the different parameters of the mobile and fix nodes gave different sort of performances.

Keywords – VoIP ,WLAN, Wi-Fi, Encoding Schemes, MAC, and OPNET

I.

INTRODUCTION

The 802.11b standard was created in 1999 for WLAN operations. This is also known as Wi-Fi. 802.11b is the slowest and least expensive standard. Therefore its cost made it popular, but now it's becoming less common as faster standards become less expensive. 802.11b transmits in the 2.4 GHz frequency band of the radio spectrum. It can handle up to 11 megabits of data per second, and uses complimentary code keying (CCK) coding. The main advantages of 802.11b are lower cost, good signal range and are not easily obstructed[1]. VoIP (Voice over Internet Protocol) is a collection of technologies, protocols, and devices that provide

II.

SIMULATION APPROACH

The VoIP based Wi-Fi network has been developed on OPNET and Figure 1 shows the infrastructure mode of VoIP Wi-Fi system. The wireless domain consists of access point and three wireless stations and this domain is connected to the subnet through IP cloud. The IP router is the inter connection of various

522

National Conference on Advanced Computing and Communication Technology Table 1 Simulation Parameters Used Wi-Fi Parameters

Value

Data Rate

11Mbps

Bandwidth

2.4 GHz

Access Technology

Direct Sequence

Transmit Power

7.33 E-14 W

Short Retry Limit

7

Traffic Generation Parameters

Value

Simulation Time

15 mins

Inter-Repetition Time

Constant(120)

Duration of VoIP call

Constant(120)

Operation Mode

Simultaneous

Number of Repetitions

Unlimited

Figure 1 Simulation Model.

routers used to connect Wide Area Network or two or more networks at a distance. The access point runs standard IEEE 802.11b protocol to all clients and act as bridge between the wired and wireless routers and gateways. Through this access point the wireless stations are communicating with wired stations in the subnet and vice versa. The subnet is the wired LAN part consisting of three Ethernet stations. The switch in the subnet is used to connect Ethernet stations to the access point through IP Cloud. Figure 2 shows the view of subnet. The client address of mobile station 1 is the destination address of Ethernet station 1 in subnet and in a similar way the destination and client addresses are assigned to all mobile and Ethernet stations[3].

Scenario 3: G.728 with coding rate 16 kbps and frame size 10ms is the scheme used for voice communication. Scenario 4: To encode and decode analog signals G.729 with 8 kbps data rate and 30ms frame size is used as the voice encoder scheme. Scenario 5: For VoIP G.723.1 is used with data rate 5.3 kbps and 30ms frame size. IV.

RESULTS

With the above said parameters, the simulation was done for each of the scenarios. The evaluation metrics are studied one by one to give detailed effective results. A. Throughput As the throughput is the ratio of the total amount of data that reaches the receiver to the time it takes, theoretically throughput is concerned with traffic received by the receiver. Figure 3 indicates the results for throughput for voice encoder G.711, G.726, G.728, G.729, and G.723 schemes respectively. There is decrease in throughput of 80kbps for G.726, 105kbps for G.728, 160kbps for G.729kbps and 170kbps for G.723 respectively for the VoIP Wi-Fi network. The throughput of G.711 is maximum because it transmits many packets without doing any compression to the packets and the time interval is also very low(4ms). G.723 and G.729 generate fewer packets than G.711 because they need

Figure 2 The Subnet.

III.

SIMULATION SCENARIOS

Scenario 1: In this scenario the G.711 voice encoder scheme with 64 kbps coding rate and 4ms of frame size is used. Scenario 2: Stations are configured for G.726 codec with 32 kbps coding rate and 10ms of frame size.

523

National Conference on Advanced Computing and Communication Technology to compress the packets. But due to compression, vice quality at the receiver end decreases which is the main disadvantage of use of compression.

small algorithm delay. G.723 and G.729 have less delay because they have small number of packets.

Fig 5 Voice packet end-to-end delay Fig 3 Throughput for different Encoding Schemes.

B.

V.

MAC Delay

CONCLUSION

Results reveal that the capacity of VoIP Wi-Fi system increases for voice encoder schemes other than G.711. This establishes that the encoder schemes except G.711 can support more VoIP simultaneous calls for Wi-Fi system. From all above results it is observed that low bit rates coding schemes offer lower delay which results in better voice quality and capacity but on other hand lead to important decrease in network load utilization to G.711. Therefore, to support more VoIP calls G.729 and G.723 may be referred. But between these two G.729 would be the best choice because it has more MOS than G.723 i.e. better voice quality.

The results in the figure 4.8 are for media access delay for different voice encoding schemes. The media access delay for G.711 encoder scheme is maximum. Media access delay is minimum for G.729. So the results reveals that the encoder schemes with negligible media access delay may be preferred over G.711 encoder scheme.

VI.

REFERENCES

[1] - Joseph Ghetie, “Fixed-Mobile Wireless Networks Convergence”, Cambridge University Press, 2008. [2] - Olivier Hersent, Jean-Pierre Petit, “Beyond VoIP Protocols”, John Wiley & Sons Ltd,2005. Fig 4 MAC Delay

[3] J. Theunis, B. Van den Broeck, “OPNET in Advanced Networking Education”, Katholieke Universiteit Leuven,2005.

C. Voice Packet End-to-End Delay

[4] - K. Salah, A. Alkhoraidly, “An OPNET-based Simulation Approach for Deploying VoIP”, King Fahd University,2006.

Figure 5 shows comparison of Voice packet end to end delay for different voice encoding schemes. The difference in voice packet end to end delay for G.711 and G.729, G.723 encoder schemes is 0.290 secs. G.711 delay due to the multiple packet generation which are to be handled otherwise G.711 has very

524

Performance Comparisons of Routing Protocols and TCP in MANETS Neha Arora, Sapna Arora

([email protected],

[email protected])

Institute of Technology and Management, ECE Dept, Maharishi Dayanand University Gurgaon (Haryana), India Abstract— Ad hoc networks are characterized by a lack of infrastructure, and by a random and quickly changing network topology; thus the need for a robust dynamic routing protocol that can accommodate such an environment. So, it is significant to bring out a comparison among various routing protocols in different scenarios with TCP variants, for their better understanding and implementation. A comprehensive performance evaluation of various routing protocols and the TCP is presented to understand the nature of the TCP performance in different scenarios with variable number of nodes. Three different routing protocols (AODV, TORA and OLSR) have been evaluated with four different TCP variants (Tahoe, Reno, New Reno and SACK) in two different scenarios having 8 and 12 nodes. The performance parameters on the basis of which routing protocols are graded are throughput, delay and congestion window. Conclusions are drawn based on the simulation results and the comparison results are graphically depicted and explained. Keywords: MANET, Wireless networks, routing, AODV, Throughput, OPNET

I. INTRODUCTION TCP is designed for wired network, but with the technology emerging towards wireless medium, the need to implement TCP in ad hoc networks is of great importance but it faces many problems. TCP has poor performance in MANET due to dynamic topology, shared medium, high error ratio; channel connotation and multi hop architecture. In order for the ad hoc networks to operate as efficiently as possible, appropriate on-demand routing protocols have to be incorporated, to find efficient routes from a source to a destination, taking node mobility into consideration. The Mobility influences ongoing transmissions, since a mobile node that receives and forwards packets may move out of range. As a result, links fail over time. In such cases a new route must be established. Thus, a quick route recovery procedure should be one of the main characteristics of a routing protocol. In an ad hoc network, mobile nodes communicate with each other using multi-hop wireless links. There is no stationary infrastructure such as base stations in ad hoc networks. Each node in the network also acts as a router; forwarding data packets for other nodes, which in such a network move arbitrarily, thus network topology changes frequently and unpredictably. Such problems are associated with the MANET performance and therefore evaluation and optimization techniques are necessary to opt and adhere for the better execution of the transmission medium. To measure the performance of

525531

different TCP variants, simulation study has been conducted in practice. MANET utilizes TCP and UDP for data transmission and our study focuses on different variants of the TCP i.e. particularly Tahoe, Reno, New Reno and SACK explicitly using AODV, TORA and OLSR protocols. II. PROTOCOLS In this section, we give a brief overview of the routing protocols used in our performance analysis. We also discuss the variations of the TCP protocol that were considered. A. ROUTING PROTOCOLS AODV and TORA are on-demand algorithms. Unlike proactive protocols such as OLSR, on-demand protocols do not maintain routes between all the nodes in an ad hoc network. Rather, routes are established when needed through a route discovery process in which a route request is broadcast. A route reply is returned either by the destination or by an intermediate node with an available route. Route error messages are used to invalidate routing table entries when link failures are detected. The Ad Hoc On-Demand Distance Vector routing protocol (AODV) is an improvement of the Destination-Sequenced Distance Vector routing protocol (DSDV). It is based on distance vector and also uses the destination sequence numbers to determine the freshness of the routes. AODV requires hosts to maintain only active routes. The advantage of AODV is that it tries to minimize the number of required broadcasts. It creates the routes on an ondemand basis, as opposed to maintain a complete list of routes for each destination. Therefore, the literature on AODV, classifies it as a pure on-demand route acquisition system. TORA is a reactive routing algorithm based on the concept of link reversal and used in MANETs to improve the scalability. Highly dynamic mobile ad hoc networks can be used by TORA. It is an adaptive routing protocol used in multi-hop networks. It makes scaled routes between source and destination. There are three basic functions in TORA: 

Route Creation



Route Maintenance



Route Erasure

OLSR is a proactive link-state routing protocol, which uses Hello and Topology Control (TC) messages to discover and then disseminate link state information throughout the mobile ad-hoc network. Individual nodes use this topology information to compute next hop destinations for all nodes in the network using shortest hop forwarding paths. B. TRANSPORT PROTOCOLS After the introduction of first version of TCP, several variants are introduced, here we are discussing the most famous implementation of TCP called Tahoe, Reno, New Reno and SACK. TAHOE In the first version of TCP there was no congestion control mechanism. So after observing the congestion, Jacobson introduced several Congestion Control algorithms and this version is called TCP-Tahoe. The congestion control algorithms introduced in this version are: a) Slow Start b) Congestion Avoidance c) Fast Retransmit RENO TCP Reno is the most widely adopted Internet TCP protocol. It employs four transmission phases: a) Slow Start b) Congestion Avoidance c) Fast Retransmit d) Fast Recovery. NEW RENO TCP New Reno is a modification of TCP Reno. It improves retransmission process during the fast recovery phase of TCP Reno. TCP New Reno can detect multiple packet losses and does not exit the fast recovery phase until all unacknowledged segments at the time of fast recovery are acknowledged. Thus, as in TCP Reno, it overcomes reducing the congestion window size multiple times in case of multiple packet losses. The remaining three phases (slow start, congestion avoidance, and fast retransmit) are similar to TCP Reno. SACK SACK algorithm allows a TCP receiver to acknowledge out-of-order segments selectively rather than cumulatively by acknowledging the last correctly in order received segment. The receiver acknowledges packets received out of order and the sender then retransmits only the missing data segments instead of sending all unacknowledged segments. III. SIMULATION SETUP The general evaluation of the various TCP agents in the following section is conducted with the OPNET 11.5 tool.

The simulation is conducted in two different scenarios with varying number of nodes. The simulation area chosen is 1000mx1000 m where nodes are placed randomly. The mobility model used is the Random Waypoint .In the first scenario, the network we simulated consisted of 5 nodes randomly placed on a 1000m x 1000m field and 12 nodes in the second scenario. We utilized a mobility pattern based on the random waypoint model. Speed of mobile nodes is constant at 10m/s, and only zero-length pause times are considered. In both the scenarios, all the three routing protocols are evaluated based on the three performances metric which are Throughput, Delay and the Congestion Window. The simulation environment for these scenarios is:      

Various numbers of nodes which are 5 &12 nodes File size is set to 10 Mbytes Area size is set to 1000m x 1000m Node Speed is fixed to 10 m/s Random Way Point mobility model is used Network Protocol is IPv4

Network traffic is created by using FTP application between the server and the mobile nodes. The simulation duration is 150 seconds. Experiments are run for different protocols. A. Five Nodes Scenario: For five nodes scenario, the details of different protocols are as shown in Table 1. There are fives nodes working as clients to establish connection with a fixed node working as source, and to transfer a file of the same size over each connection. Table1: Details of TCP Variants and Routing Protocol for Five Nodes THROUGHPUT/ DELAY/ CONGESTION WINDOW Stages TCP Protocol Number Speed of MEASUREMENT A A A A B B B B C C C C

Variants Tahoe Reno New SACK Reno Tahoe Reno New SACK Reno Tahoe Reno New SACK Reno Variants

AODV AODV AODV AODV TORA TORA TORA TORA OLSR OLSR OLSR OLSR

of Nodes 5 5 5 5 5 5 5 5 5 5 5 5

Nodes (m/s) 10 10 10 10 10 10 10 10 10 10 10 10

IV. SIMULATION RESULTS A. Throughput From figure 1, by looking at the throughput performance between AODV and TORA at approximately 60 seconds, we can say AODV has better throughput performance over TORA (except for tahoe). Comparing AODV with OLSR at the approximation of 60 sec, AODV provides better throughput performance, in all the graphs. Total time consumed by each protocol to send the data through SACK, New Reno, Reno and Tahoe is relatively smaller for AODV followed by OLSR and than TORA. It means that TORA has worst throughput performance in each of the 4 cases for MANET 5 nodes. The reason behind TORA having less performance is that TORA works for route recreation, maintenance and erasure, if dropping of the route occurs, it requires more time and has bad impact in the data performance. FIGURE 1: Throughput Comparisons in 5 Nodes Scenario

12 Nodes Scenario From figure 2, we observe that graph behavior remains almost same as for 5 Nodes Scenario.

Figure 2: Throughput Comparisons in 12 nodes scenario

B. Delay Figure 3 holds the simulation results for each and every TCP variant with respect to different routing protocols all together. With 5 node scenario, TORA has the highest delay as compared to OLSR and AODV which validate our simulation results. With 12 nodes scenario, TORA and OLSR have approximately same delay, which is not considered as a good perception in wireless networks, as we encounter numerous losses and delays due to SNR, reflection, diffraction and inter symbol interference, so the delay measure is considered highly sensitive.

Figure 3: Delay Comparisons in Different Scenarios

C. Congestion window Figure 4 holds graphs defined earlier for the 5 node simulated scenario; there is much of dissimilarity within each set of variants and routing protocols. Congestion window of AODV reaches to the maximum of 7,000 bytes and remains there which depicts least of delay and better performance. TORA shows different results in all cases with CW size varying from around 2000 to 5500 bytes. So it’s likely to know that TORA congestion window is quite uncertain and its use in the MANET network will have severe results. Figure 4: Congestion Window Dynamics

V. CONCLUSIONS We adhere to the simulation results as evidence that TCP variants have minor affect on the overall results except in few cases, but the major dependence lies on MANET routing protocols. Simulation observation based on AODV, OLSR and TORA clearly describes about the performance evaluation by measuring throughput, delay and congestion window indicating that the best routing protocol for MANET is AODV.

VI. OPEN PROBLEM Presently numerous MANET routing protocols of interest have been selected by simulation in OPNET tool. Another possibility can be of doing the same work through another tool like NS-2 and check if we get better or different results. Also, selection of other routing protocols can be used for the performance evaluation or other parameters of performance could be considered for simulation such as single packet loss and multiple packet losses. References: [1] K. Leung and Victor O.K. Li, “Transmission Control Protocol (TCP) in wireless Networks: issues, approaches and challenges,” IEEE Communications Survey, Vol. 8 No. 4, pp. 64-79, 4th October 2006 [2] IEEE Standard 802.11, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, June 2007 [3] A. A. Hanbali, E. Altman, and P. Nain, “A Survey of TCP over Ad Hoc Networks,” IEEE Communication. Surveys and Tutorials, Vol. 7, No. 3, 3rd Quarter 2005, [4] V. Jacobson, “Congestion avoidance and control,” in: Proceedings of ACM SIG COMM, pp. 314-29, 1988. [5] Changling Liu, Jörg Kaiser, A Survey of Mobile Ad Hoc network Routing Protocols* [email protected] [6] A. Gurtov and S. Floyd, “Modeling wireless links for transport protocols,”ACM SIGCOMM Comput. Commun Rev., vol. 34, no. 2, pp. 85–96, Apr. 2004. [7] G. Holland and N. Vaidya, “Analysis of TCP performance over mobile ad hoc networks,” in Proc. ACM/IEEE Int. Conf. on Mobile Computing, Seattle, WA, USA, Sept. 1999, pp. 219–230. [8] W. G. Zeng and Lj. Trajkovic, “TCP packet control for wireless networks,” in Proc. IEEE Int. Conf. on Wireless and Mobile Computing, Networking and Communications (WiMob 2005), Montreal, Canada, Aug. 2005, vol. 2, pp. 196–203.

[9] H. Balakrishnan et al., “A Comparison of Mechanisms for Improving TCP Performance over Wireless Links,” IEEE Trans.Net., vol. 5, no. 6, Dec. 1997, pp. 756–69. [10] H. Balakrishnan et al., “Improving TCP/IP Performance over Wireless Networks,” Proc. ACM MOBIHOC, Berkeley, CA, USA, 1995, pp. 2–11. [11] J. Liu and S. Singh, “ATCP: TCP for Mobile Ad Hoc Net-works,” IEEE JSAC, vol. 19, no. 7, pp. 1300-1315, July 2001. [12] F. Wang and Y. Zhang, “Improving TCP Performance over Mobile Ad Hoc Networks with Out-of-order Detection and Response,” Proc. ACM MOBIHOC, Lausanne, Switzerland, June 2002, pp. 217–25. [13] K. Chin et al., “Implementation Experience with MANET Routing Protocols,” ACM SIGCOMM Comp. Commun. Rev., vol. 32,no. 5, Nov. 2002, pp. 49–59. [14] C. Jones et al., “A Survey of Energy Efficient Network Protocols for Wireless and Mobile Networks,” ACM Wireless Net, vol. 7, no. 4, 2001, pp. 343–58. [15] V. Anantharaman et al., “TCP Performance over Mobile Ad Hoc Networks: A Quantitative Study,” J. Wireless Commun. and Mobile Computing, vol. 4, no. 2, Mar. 2004, pp. 203–22. [16] T. Dyer and R. Boppana, “A Comparison of TCP Performance over Three Routing Protocols for Mobile Ad Hoc Networks,”Proc. ACM MOBIHOC, Long Beach, CA, USA, 2001, pp. 56–66 [17] H. Lim, K. Xu, and M. Gerla, “TCP Performance over Multipath Routing in Mobile Ad Hoc Networks,” Proc. IEEE ICC, Anchorage, Alaska, May 2003. [18] K. Xu, S. Bae, S. Lee, and M. Gerla, “TCP Behavior across Multihop Wireless Networks and the Wired Internet,” Proc.ACM Wksp. Wireless Mobile Multimedia, Atlanta, GA, USA, Sep. 2002, pp. 41–48.

Comparative Analysis of Urban Propagation Models Neha Arora, Sapna Arora ([email protected], [email protected]) ITM, ECE Dept, Maharishi Dayanand University Gurgaon (Haryana), India Abstract: Simulation tools are frequently used to analyze the performance of wired and wireless networks. The radio wave propagation model has a strong impact on the results of the simulation run. The development of various prediction tools requires the comparison of radio measurements with prediction methods. For a country like India where diverse terrain conditions exist, same prediction methods might not hold well in all regions. In this paper, a comparative analysis of urban propagation models is presented. To identify the methods suitable to urban zones an attempt is made to compare the measured results with classical two-ray, Hata and other models. The comparison has been evaluated in terms of parameters like throughput, delay etc. The performance of these propagation models is evaluated through simulation in QUALNET 5.0

All these models predict mean path loss as a function of various parameters, for example distance, antenna heights etc. A.

Importance of Propagation Prediction

Before implementing designs and confirming planning

of wireless communication systems, accurate propagation characteristics of the environment should be known. Propagation prediction usually provides two types of parameters corresponding to the large-scale path loss and small-scale fading statistics. The path-loss information is essential for the determination of coverage of a base-station (BS) placement and in optimizing it. The smallscale parameters usually provide statistical information on local field variations and this, in turn, leads to the calculation of important parameters that help to improve receiver (Rx) designs and combat the multipath fading. Without propagation predictions, these parameter estimations can only be obtained by field measurements which are time consuming and expensive. The following subsection provides a brief description of challenges faced for the development of accurate and sufficiently general propagation prediction models.

Keywords: Urban Environment, propagation models, simulation. I. INTRODUCTION Propagation models are used extensively in network planning, particularly for conducting feasibility studies and during initial deployment. They are also very useful for performing interference studies as the deployment proceeds. These models can be broadly categorized into three types; empirical, deterministic and stochastic. Empirical models are those based on observations and measurements alone. These models are mainly used to predict the path loss, but models that predict rain-fade and multipath have also been proposed. The deterministic models make use of the laws governing electromagnetic wave propagation to determine the received signal power at a particular location. Deterministic models often require a complete 3-D map of the propagation environment. An example of a deterministic model is a ray-tracing model. Stochastic models, on the other hand, model the environment as a series of random variables. These models are the least accurate but require the least information about the environment and use much less processing power to

B. Challenges to the Propagation Modeling Wireless communication channels are inherently frequency dispersive, time varying, and space selective, although only one or two of these dependencies will appear in some cases. The fast evolution of wireless communications has lead to the use of higher frequency bands, smaller cell sizes, and smart antenna systems, making the propagation prediction issues more challenging. In macrocells, since the transmitting antenna is usually located on a high tower, simple empirical and statistical models are widely used with satisfactory accuracy. As for the microcells and especially for picocells, the height of the transmitting antenna may be lower than the average height of the buildings in the regions involved, the geometry of the buildings and terrains will greatly affect the propagation of the radio waves, causing wide shadow regions. The outdoor radio wave propagates through reflections from vertical walls and ground, diffractions from vertical and horizontal edges of buildings, and scattering from non smooth surfaces, and all possible combinations. There is no general empirical and statistical model that can be used for prediction of

generate predictions. Empirical models can be split into two subcategories namely, time dispersive and non-time dispersive. The former type is designed to provide information relating to the time dispersive characteristics of the channel i.e., the multipath delay spread of the channel. An example of this type is the Stanford University Interim (SUI)

channel models developed under the Institute of Electrical and Electronic Engineers (IEEE) 802.16 working group. Examples of non-time dispersive empirical models are ITU-R, Hata and the COST231 Hata model. 532

these complicated propagation environments. To deal with the new complex propagation environments,

site-specific models have been developed based on ray-tracing techniques. In a basic ray-tracing algorithm, the main task is to determine the trajectory of a ray launched from a transmitting antenna. This procedure involves the calculation of the intersection of a ray with a surface (in threedimensional (3-D) cases) or a ray with an edge segment (in two-dimensional (2-D) cases). The computation time might be huge or even beyond the capability of present computers if the propagation environment is large and/or complex. The computation efficiency is then the biggest obstacle against the application of ray-tracing methods. An efficient ray-tracing procedure is also important for improving the prediction accuracy since more types of rays—such as reflected transmitted, diffracted and scattered rays and their combinations—can be taken into account. The accuracy of propagation prediction involves many

aspects. These include the accuracy of locations and sizes of buildings and accurate knowledge of the electric parameters of walls and other objects involved. Trees, large posts, traffic, and pedestrians in outdoor cases and furniture in indoor cases can also influence the results and make a difference. Recently, accurate characterization of complex wall structures including metal-framed windows is receiving attention due to the requirement of a more accurate prediction of the indoor/outdoor propagation mechanism. To meet these challenges, existing prediction methods should be modified and improved, and new procedures and techniques have to be developed. II.

PROPAGATION MODELS

There is an explosive growth in the market of wireless communications services in urban areas. New regulatory environments as well as competition in the communications industry require that these systems be deployed quickly and at low cost. Computer-based radio propagation prediction tools are strong candidates for this goal. Moreover, radio channels are much more complicated to analyze than wired channels. Their characteristics may change rapidly and randomly. There are large differences between simple paths with line of sight (LOS) and those which have obstacles like buildings or elevations between the sender and the receiver (Non Line of Sight (NLOS)). To implement a channel model generally two cases are considered: large-scale and small-scale propagation models. Large scale propagation models account for the fact that a radio wave has to cover a growing area when the distance to the sender is increasing. Small scale models (fading models) calculate the signal strength depending on small movements or small time frames.

Due to multi path propagation of radio waves, small movements of the receiver can have large effects on the received signal strength. Below, four frequently used urban propagation models for QualNet network simulator are described in more detail. COST 231 HATA MODEL The Hata model is introduced as a mathematical expression to mitigate the best fit of the graphical data provided by the classical Okumura model. Hata model is used for the frequency range of 150 MHz to 1500 MHz to predict the median path loss for the distance d from transmitter to receiver antenna up to 20 km, and transmitter antenna height is considered 30 m to 200 m and receiver antenna height is 1 m to 10 m. To predict the path loss in the frequency range 500 MHz to 2000 MHz. COST 231 Hata model is initiated as an extension of Hata model. It is used to calculate path loss in three different environments like urban, suburban and rural (flat).This model provides simple and easy ways to calculate the path loss. COST 231 WALFISH-IKEGAMI (W-I) MODEL This model is a combination of J.Walfish and F.Ikegami model and is most suitable for flat suburban and urban areas that have uniform building height. Among other models like the Hata model, COST 231 W-I model gives a more precise path loss. This is as a result of the additional parameters introduced which characterize different environments. It distinguishes different terrain with different proposed parameters. III. DESCRIPTION OF URBAN AREA SCENARIO To evaluate the comparison of urban propagation models, the throughput and delay of multiple constant bit rate (CBR) streams is taken as an indicator. Network traffic is created by starting CBR connections between randomly selected nodes. The simulation duration is 300 sec. Figure 1 shows the scenario considered for the urban area environment over a simulation area of 500m × 500m and figure2 shows the running model. In our simulation, we consider a network of 8 nodes that are placed randomly within a 500m X 500m area and operating over 300 seconds. Experiments are run for different models. The number of nodes in a scenario is eight. The number of CBR connections is set to two; the offered load per connection is 1024 Bytes/s.

Figure 3: Average Jitter

Figure 1: Simulation Model

Figure 4: Average End-to-End Delay

Figure 2: Running scenario Data Base The propagation tool requires as inputs the locations, orientations and antenna patterns of all transmitter sites and locations of all receivers. It also requires the transmitted power levels of all the transmitters. We study the most popular propagation models, COST-231Hata model, Two Ray Ground model, and COST-231 Walfish Ikegami model. Our evaluations are based on the simulation using Qualnet 5.0 and we extract the useful data using analyzer tool and graphs are generated.

Figure 5: Total Bytes Received

Figure 6: Signals Transmitted

V. COMPARATIVE ANALYSIS OF THE PROPAGATION MODELS The analysis of simulation results is performed based on the standard metrics of number of sent bytes, signals received, throughput, average jitter, end to end delay between different radio propagation models. Figure 3 indicates that Average Jitter is maximum for COST-231 Hata model and least for Two Ray model while figure 4 is indicative of average end to end delay which is maximum for COST-231 Walfish Ikegami model and least for Hata model. Figure 5 shows that Hata model and Two Ray model perform better than WI model when total bytes received are considered as metric. In contrast WI model transmits more signals as in figure 6, although the results indicate that its throughput is lower as compared to other propagation models (figure 7).

Figure 7: Throughput VI. CONCLUSION This work presented how these propagation models perform in an urban environment. We provide the parameters used in the creation of scenario in an urban environment. We incorporate the radio propagation models for wave propagation through Qualnet. A comparative analysis of various models has been carried out the statistical results obtained through QualNet simulation shows successful implementation of Two Ray Model and Cost 231 W-I Model showing the least.

References [1] V.S. Abhayawardhana, I.J. Wassel, D. Crosby, M.P. Sellers, M.G. Brown, “Comparison of empirical propagation path loss models for fixed th IEEE Technology wireless access systems,”61 Conference, Stockholm, pp. 73-77, 2005. [2] Josip Milanovic, Rimac-Drlje S, Bejuk K, “Comparison of propagation model accuracy for th WiMAX on 3.5GHz,” 14 IEEE International conference on electronic circuits and systems,Morocco, pp. 111-114. 2007. [3] M. Hata, “Empirical formula for propagation loss in land mobile radio services,” IEEE Transactions on Vehicular Technology, vol. VT29, pp. 317-325, September 1981. [4] T.S Rappaport, Wireless Comunications: Principles andPractice, 2n ed. New delhi Prentice Hall, 2005 pp. 151-152. [5] Well known propagation model, [Online]. Available: http://en.wikipedia.org/wiki/Radio_propagation_m odel [Accessed: April 11, 2009] [6]Empirical Models, [Online] Available: http://en.wikipedia.org/wiki/Empirical_model [Accessed April 18, 2009] [7] J. H. Whitteker, “Measurements of path loss at 910 MHz for proposed microcell urban mobile systems,”IEEE Trans. Veh. Technol., vol. 37,no. 3, 1988.

[8] R. A. Valenzuela, “A ray tracing approach to predicting wireless transmission,” in Proc. 43rd IEEE Veh. Technol. Conf., 1993, pp. 214–218 [9]Gahleitner, R. and Bonek, E., “Radio wave penetration into urban buildings in small cells and microcells”, th 44 IEEE Vehicular Technology Conference, vol. 2, pp. 887 –891, 1994. [10]Jong de, Y.L.C. and Herben, M.H.A.J., “Experimental verification of ray-tracing based propagation prediction models for urban microcellular environments”, in Proceedings of IEEE Vehicular Technology Conference, vol. 3, pp. 1434-1438, Sept. 1999. [11] Seidel, S.Y. and Rappaport, T.S., “Sitespecific propagation prediction for wireless inbuilding personal communication system design”, IEEE Transactions on Vehicular Technology, vol. 43, no.4, pp. 879-891, Nov. 1994. [12] Toledo de, A.F., Turkmani, A.M.D. and Parsons, J.D., “Estimating coverage of radio transmission into and within buildings at 900, 1800 and 2300 MHz”, IEEE Personal Communications, vol. 5, iss.2, pp. 40-47, April 1998.

535

National Conference on Advanced Computing and Communication Technology

Optimization of Performance Evaluation Parameter Using Formal Verification of Routing Protocols Deepak Gupta, Asstt. Prof., VCE Rohtak email- [email protected]

Vinay Goyal, Prof., JIET ,Jind [email protected]

Mobile Ad Hoc Networking (MANET) has become an exciting and important technology in recent years because of the rapid proliferation of wireless devices. MANET in fig 1 is an infrastructure less network consisting of mobile terminals with the capability to communicate with each other. Every mobile node acts as a router and forwards traffic originated by other nodes. Each node is able to dynamically discover and maintains routes to other nodes in the network. Established routes should be loop-free and route changes should converge quickly even in large network.

Abstract Crucial to the performance of MANETs is route-discovery; unfortunately there is a trade off between the amount of information that should be disseminated by protocols for route-discovery and the quality of the resulting link decisions. Typically the outcome for the user is poor performance of routing protocols. Performance is easily ignored during the protocol design process. Neither simulations nor testbed implementations can ensure the performance of these protocols. As an alternative to these methods, formal verification is a technique that optimizes the performance evaluation parameter of routing protocols. In this paper, we briefly present most established routing protocols and argue that simulation-based performance evaluation of ad hoc routing protocols should be complemented with formal verification using SPIN. Keywords: SPIN, Performance evaluation, optimization, Formal verification

1. Introduction

Fig 1: Ad hoc network

Ad hoc networks are a new paradigm of wireless communication for mobile nodes. Since the inception of MANETs and sensor networks, a number of routing protocols have been proposed to efficiently discover and maintain paths in an ad hoc network [1]. Due to lack of infrastructure and resources required to set up an ad hoc network, most of the routing protocols are evaluated and compared using network simulators. While simulations offer the flexibility to code and evaluate complex algorithmic logic, recent studies have shown that many ad hoc simulations report inconsistent, unrepeatable, or incomplete simulation results [2]. Furthermore, scalability experiments even with thousands of nodes quickly become infeasible due to extremely time-consuming nature of network simulations. Therefore, we argue that simulation-based performance evaluation should be complemented with mathematical model of key performance parameters of ad hoc routing protocols. Such an approach will allow unbiased and provable performance evaluation of the routing protocol even on very large scale networks. Moreover, an analytical approach

will allow researchers to analyze the strengths and weaknesses of a protocol at an early design stage. A complementary technique to simulation and testing is to prove that a system operates correctly. The term for this mathematical demonstration of the correctness of a system is Formal Verification [3]. In model checking [4], algorithms executed by computer tools are used in order to verify the correctness of systems. The user gives a description of the system (the possible behaviour) and defines the requirements (the desirable behaviour). Knowing these parameters, the machine can perform a verification of the model. If no errors are found, the user can refine its model description and can restart the verification process until the model specifications coverage to the real system. The rest of this paper is organized as follows. Section 2 summarizes the previous work in this area. Section 3 outlines overview of formal verification and SPIN model checker. Section

536

National Conference on Advanced Computing and Communication Technology 4 presents the review of routing protocols whose performance evaluation parameter is optimize using formal verification by SPIN model checker. We summarize key conclusions of this work in Section 5.

their relationships are modeled as a graph where nodes become the vertices and the links become the edges of the graph. With this, the vertex degree shows the number of neighbors of the node. This structure is then translated into CPN. To perform the verification this work uses five nodes, but, again, there is no explanation about why to use such number. The verification also is, partially, bounded by the computational power available to the user, i.e., if there are more resources, it is possible to add more nodes. Das and Dill [9] propose a way to discover quantified predicates automatically from the model. They use this technique to prove the absence of loops in a simplified version of AODV. The initial predicate set is formulated in a manual step where conditions on nextnode pointers, hop counters, and existence of routes are constructed. The method successfully discovers all required predicates for the version of AODV considered. Unfortunately, for the general case, the problem of finding predicates to an unbounded system is intractable. However, the authors claim that the presented technique, Predicate Abstraction, is an efficient way of reducing infinite state systems into more tractable finite state systems. As a manual verification, we can refer to the work of Ogier [10], which proves the correctness of the Topology Dissemination Based on Reverse-Path Forwarding (TBRPF) routing protocol. Since TBRPF consists of two modules, the routing module and the neighbour discovery module, the work presents the correctness proof for both modules separately. Even though this kind of proof is not easy, its results and procedures stand for TBRPF and only for it. Another point to observe is that when verifying a protocol, all cases must be considered and doing so manually it can be even harder for other protocols.

2. Literature Review This section will present some important proposals on formal verification for routing in wireless networks, giving special attention to their strong and weak points. In and Obradovic et. Al [5] show how to use the theorem prover HOL and the model checker SPIN to prove key properties of distance vector routing protocols. The technique focus mainly on distance vector algorithms. The main disadvantage of this work is the intense user interaction. HOL is a semi automatic theorem prover that needs the user to guide it. Another problem is the complexity in defining the theorems and lemmas to perform the real proof. In, Wibling et al. [6] use model checking to verify the Lightweight Underlay Network Adhoc Routing (LUNAR) Protocol. They use SPIN to verify the data and control aspects of the LUNAR protocol and the UPPAAL tool to verify the protocol timing properties. A possible drawback is that the authors only verify LUNAR, which was designed by the same group. Furthermore, the work is also based on some strong assumptions: only bidirectional links are allowed, messages must be delivered in order, and each node in the network can only receive and handle one message at a time. Such assumptions, in some cases, may even prevent the whole protocol verification, if it is based on any of these points. Renesse and Aghvami [7] present a technique to use SPIN to formally verify routing algorithms for ad hoc networks. In their work, Renesse and Aghvami argue that the super trace mode of SPIN is more suitable for large models. The super trace mode of a SPIN validation can be performed in much smaller amount of memory, and still present reasonable coverage. They present simple examples in PROMELA of how to implement timers, mobility, and other needful procedures. They apply their technique to the Wireless Adaptive Routing Protocol (WARP) using a five-node network. No strong justification or proof is given for using this number of nodes. Xiong et al. [8] propose a timed model for AODV protocol, based on the idea of topology approximation mechanism. This mechanism describes the aggregate behavior of nodes where their long-term average behaviors are of interest. With this technique the nodes and

3. Formal Verification A complementary technique to simulation and testing is to prove that a system operates correctly. The term for this mathematical demonstration of the correctness of a system is Formal Verification [3]. There are basically three kinds of automated formal verification techniques, namely model checking, theorem proving and equivalence checking. Model checking is a method to verify if a formally model system satisfies a given property [11]. Theorem proving technique uses mathematical methods, such as axioms and rules, to prove the correctness of a system. Equivalence checking formally checks

537

National Conference on Advanced Computing and Communication Technology if two models, at different abstraction levels, are equivalent.

single node SPIN simulates the ehaviour of the protocol over a random sequence of events. Viewing the values of the leader-ids over the period of the simulation provides valuable debugging information as well as intuitions about possible invariants of the system.

3.1 Model checking Model checking verifies, using an algorithm, if a given model is in accordance with the specification. The model is normally programmed in a special purpose language and it is based on the system specification. Given the complexity of the current systems, the models often represent a simplified version of the target systems. Some tools express the properties to be verified using temporal logic formulae. Temporal logic allows the programmer to express system properties and verify them against the model. Figure 2 presents the model checking approach. The tool receives as input the system model and the desired/undesired property to be checked. Model checking fully automatic, systems modelled by finite state automata (Kripke structures), specifications given as logical formulas, model checking algorithms return true if the system satisfies the specification give a counterexample otherwise.

Fig 3: SPIN Model Checker

4. Evaluated Protocols

Ad

hoc

Routing

Formal verification is the process of verifying, through a series of formal proofs, if a system has or has not a given property. Formal verification is an excellent technique for performance evaluation. Through the use of formal verification we can optimize the performance evaluation parameters of ad hoc routing protocols. SPIN is an efficient verification system for modeling distributed software systems. It provides a powerful and concise notation for expressing general correctness requirements. SPIN accepts the design specifications written in the verification language PROMELA (Process Meta Language). SPIN translates each process template into a finite automaton. Instead of doing the verification directly on the PROMELA code, to improve its performance, SPIN generates C code from the model. This saves memory, improves performance, and allows the insertion of C code directly into the model. This tool was used in a number of proposals to verify different routing protocols for wireless ad hoc networks, such as AODV, WARP, LAR, DREAM, OLSR and LUNAR. In the following section we review the verification of AODV and WARP. 4.1 AODV AODV [12] is a relative of the Bellmann-Ford distant vector algorithm, but is adapted to work in a mobile environment. AODV determines a route to a destination only when a node wants to send a packet to that destination. Routes are maintained as long as they are needed by the

Fig 2: Process of model Checking 3.2 Spin Model Checker The SPIN model checking system shown in fig 3 has been widely used to verify communication protocols. The SPIN system has three main components: (1) the Promela protocol specification language, (2) a protocol simulator that can perform random and guided simulations, and (3) a model checker that performs an exhaustive state-space search to verify that a property holds under all possible simulations of the system. To verify the leaderelection protocol using SPIN, we first model the protocol in Promela. A Promela model consists of processes that communicate by message passing along buffered channels. Processes can modify local and global state as a result of an event. The Promela process ehaviour the leader-election protocol at a

538

National Conference on Advanced Computing and Communication Technology source. Sequence numbers ensure the freshness of routes and guarantee the loop-free routing. D.Obradovic et. Al [5] show how to use a model checker SPIN, together with an interactive theorem prover, HOL to prove key properties of Ad Hoc On Demand Distance Vector (AODV) routing protocol. The main disadvantage of this work is the intense user interaction. HOL is a semi automatic theorem prover that needs the user to guide it. Another problem is the complexity in defining the theorems and lemmas to perform the real proof. AODV routing protocol for mobile ad hoc networks frequent changes in topology routes on demand. The analysis based on the version 2 draft specifications. D.Obradovic chooses LOOP FREEDOM property to verify by using SPIN model checking tool. Assumption (suggested by the discovered scenarios) under which this paper proves loop freedoms are: • When a node discovers that its route to a destination has expired or broken, it increments the sequence number for the route. • Nodes never delete routes. • Nodes always immediately detect when a neighbor restarts its AODV process. The restart is treated as if all links to the neighbor have broken. The results of their work shows that it is possible to provide formal analysis of correctness for routing protocols from IETF standards and drafts with reasonable effort and speed, thus demonstrating that these techniques can effectively supplement other means of improving assurance such as manual proof, simulation, and testing. Specific technical contributions include: the first proof of the correctness of the RIP standard, statement and automated proof of a sharp realtime bound on the convergence of RIP, and an automated proof of loop-freedom for AODV. With the help of correctness proof of the properties we can optimizes the performance evaluation of routing protocols.

Adaptive Routing Protocol (WARP) using a five-node network. No strong justification or proof is given for using this number of nodes. They represent basic model consist of five nodes (a sender, a receiver, and three intermediate nodes). Each node can only initialise only one link with another node. The model is made to check link update propagations and message delivering in every five node network. The reactive part of the protocol and route failure reporting are properties of WARP which are not included in their model since they are add-in features made to improve performances. These properties can be verified independently using a different model.

5. Conclusions Formal verification is a promising technique to optimize performance of the routing protocols. The method presented is simple, but effective Formal verification does not NEED to be hard to give useful results. General verified procedures can be aggregate into a library to make the verification of newer protocols even easier. The number of mobile equipments users, such as mobile phones. PDAs, and laptops, are increasing tremendously and adhoc networking is one solution for connecting these devices. Therefore, protocol reliability becomes as important as protocol performances, formal verification is an excellent technique for gaining true reliability.\

References [1] O. Wibling. Ad hoc routing protocol validation, Licentiate Thesis 2005-004, Departmentof Info Technology, Uppsala University, Sweden, 2005. [2] S. Kurkowski, T. Camp, and M. Colagrosso, “MANET simulation studies: the incredibles,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 9, no. 4, pp. 50– 61, 2005. [3] D, Caˆ mara, C. F. Santos, and A.A. F. Loureiro. Formal verification of routing protocols for ad hoc networks, Brazilian Symposium on Computer Networks, SC, Brazil, 2001. (In Portuguese) [4] Zakiuddin, M. Goldsmith, P. Whittaker, and P. H. B. Gardiner. A methodology for model-checking ad-hoc networks, Lecture Notes in Computer Science, Volume 2648,Springer Verlag, May 2003. [5] K. Bhargavan, D. Obradovic, and C. A. Gunter. Formal verification of standards for distance vector routing protocols, Journal of the ACM, 49(4): 538–576, July 2002.

4.2 WARP Renesse and Aghvami [7] present a technique to use SPIN to formally verify routing algorithms for ad hoc networks. In their work, Renesse and Aghvami argue that the super trace mode of SPIN is more suitable for large models. The super trace mode of a SPIN validation can be performed in much smaller amount of memory, and still present reasonable coverage. They present simple examples in PROMELA of how to implement timers, mobility, and other needful procedures. They apply their technique to the Wireless

539

National Conference on Advanced Computing and Communication Technology [6] O. Wibling. Ad hoc routing protocol validation, Licentiate Thesis 2005-004, Department of Info Technology, Uppsala University, Sweden, 2005. [7] R. de Renesse, and A. H. Aghvami, Formal verification of ad-hoc routing protocols using SPIN model checker, 12th Mediterranean Electrotechnical Conference, Croatia, 2004. [8] C. Xiong, T. Murata, and J. Tsai, Modeling and simulation of routing protocol for mobile ad hoc networks using colored petri nets, Research and Practice in Information Technology, 12: 145–153, Australian Computer Society, 2002. [9] S. Das, and D. L. Dill. Counter-example based, predicate discovery in predicate abstraction. Formal Methods in ComputerAided Design, Portland, Oregon, November, 2002. [10] R. Ogier. Topology dissemination based on reverse-path forwarding (TBRPF): Correctness and simulation evaluation, Technical report, SRI International, October 2003. [11] D. Caˆ mara, A. A. F. Loureiro, and F. Filali. Methodology for formal verification of routing protocols for ad hoc wireless networks, IEEE GLOBECOM 2007, Washington DC, November 2007. [12] Das S. Perkins C.E., Belding-Royer E.M. Ad-hoc on-demand distance vector (aodv) routing. RFC 3561, IETF Network Working Group, 2003.

540

Detecting Abnormal Node by Pair Based Approach using Intrusion Detection System in Wireless Sensor Network Deepak Tyagi (Dean, IT department) Jaipuria Institute of Management Ghaziabad, UP

Pooja Sharma (Lecturer, MCA department) B.S. Anangpuria Institute of Technology and Management. Faridabad, Haryana, India [email protected]

Abstract-A wireless sensor network (WSN) consists of a large number of inexpensive and small nodes with sensing, data processing, and communication capabilities, which are densely deployed in a region of interest and collaborate to accomplish a common task, such as environmental monitoring, military surveillance, and industry process control. Sensor networks may interact with sensitive data and/or operate in hostile unattended environments. However, due to inherent resource and computing constraints, security in sensor networks poses different challenges than traditional network/computer security. Mission critical wireless sensor networks require an efficient, lightweight and flexible intrusion detection methodology to identify abnormal node or malicious attackers. . In this paper, we explore intrusion detection schemes that can be used to improve security from both external and internal eavesdroppers. The proposed idea in this paper is to develop a new approach of abnormal node detection in wireless sensor network using IDS security routing methodology by dividing the network into number of pairs. Every node of each pair is responsible to identify abnormality of other node in that particular pair considering different attributes of nodes. The abnormal node detection algorithm uses both signatures and knowledge based routing methodology to achieve most accurate and reliable result.

1. INTRODUCTION WSN consists of small battery powered wireless devices (sensors) that are capable of monitoring environmental conditions such as humidity, temperature, noise, etc. Sensor networks do not have a fixed infrastructure but form an ad hoc topology using wireless communication channels. They are deployed in a hostile environment [1] .The scale of deployments of wireless sensor networks requires careful decisions and trade-offs among various security measures. Depending on the application, a sensor network must support certain quality of service, robustness (i.e., the network should remain operational even if certain well defined failures occur), tamper-resistance (i.e., the network should remain operational even when subject to deliberate attacks), and eavesdropping resistance (i.e., external entities cannot eavesdrop on data traffic). Current security mechanisms in ad-hoc sensor networks do not guarantee reliable and robust network functionality. Even with these mechanisms, the sensor nodes could be made non-operational by malicious attackers or physical break-down of the infrastructure. Sensor networks can also be subjected to various forms of intrusions and attacks. Many different kinds of attacks against wireless

sensor networks have been identified so far, e.g., bogus routing and sensed data attack, select forward attack, sinkhole attack, wormhole attack, black hole attack and hello flood attack, etc. [2] .The motivation for attacking a sensor networks could be, for example, to gain an undeserved and exclusive access to the collected data. Wireless network security is an active research area at the present. All the security solutions proposed so far can be classified into two main categories: prevention based techniques and detection based techniques. Prevention based techniques, such as encryption and authentication, are often regarded as the first line of defense against attacks. Detection based techniques are designed to identify and isolate attackers after prevention based techniques fail. Furthermore, there are two types of detection based techniques: signature based detection and anomaly based detection. Signature based detection techniques match the known attack profiles with suspicious behaviors whereas anomaly based detection techniques detect unusual deviations from pre-established normal profiles to identify the abnormal behaviors. Anomaly detection or to some extent abnormal node detection in wireless network is not the same as in the wired network. Techniques geared towards wired networks would not suffice for an environment consisting of multihop wireless links because of the various differences such as lack of fixed infrastructure, mobility, the ease of listening to wireless transmissions, lack of clear separation between normal and abnormal behavior in sensor networks. Considering all options and limitations the proposal is to divide the network into number of pairs and to combine the signature base and the knowledge base routing methodology, which can overcome the barrier in abnormal node detection scheme in wireless sensor network. The proposed approach is a novel one in the context of abnormal node detection. Following the proposal, the whole network is divided into number of pairs where a pair defines a community between two adjacent nodes. This will reduce the complexity of the methodology to identify the abnormality between nodes in a network. In order to provide more accurate result, signature based routing and knowledgebase abnormal behavior searching technique can be used which is known as

541

Intrusion Detection System (IDS). The total developed platform and structured methodology can detect abnormal node in wireless sensor network in a more reliable and efficient manner. 2. ARCHITECTURE OF SENSOR NETWORK Typically sensor networks follow the model of a command node or base station, where sensors send the data to a command node either periodically or based on events as shown in figure-1.The command node is located faraway from the area where the sensors are usually deployed. In order to conserve energy consumed in communication with the command node various multi-hop and energy aware routing techniques have been suggested in the literature [11] [12]. These techniques suffer from the various overhead due to route discovery and, there will be extra burden on the nodes, which are located around the command node, as most of the traffic will be routed through them. To avoid these overheads and unbalanced consumption of energy some high-energy nodes called “Gateways” are deployed in the network which is responsible for establishing, organizing the cluster and performs the tasks of a certification authority. Each sensor belongs to only one cluster and communicates to the gateway which in turn communicates with the command node. As we said, sensors are susceptible to device failures due to limited battery power but will also be inactive if the gateway in their cluster suffers from some faults. Reconfiguration of the system can be used to recover the sensors in a faulty cluster through re-clustering. Reclustering the system complicates the network setup and bootstrapping. Moreover, frequent faults will result in frequent re-clustering wasting precious energy and time.

Fig. 1: Multi-gateway clustered sensor network

Nodes in WSNs are prone to failure due to energy depletion, hardware failure, communication link errors, and malicious attack and so on. Nodes in sensor networks have very limited energy and their batteries cannot usually be recharged due to hostile environments. Wireless networks are vulnerable to security attacks due to the broadcast nature of the transmission

medium. Attackers may device different types of security threats to make the WSN system unstable. 3. SECURITY RELATED ISSUES IN WSN In this section, some related works in the security field of wireless sensor network is reviewed. An important aspect of the broad area of security is anomaly detection. Many solutions have been proposed to traditional networks [18] [15], but restrictions of wireless sensor network resources make direct application of those solutions unavailable. Encryption and authentication are two primary techniques to secure wireless sensor networks against malicious access. The core ideas behind such techniques rely on key management. Li et al. proposed the hexagon-based key pre-distribution scheme that can improve the effectiveness of key management in sensor network by using the bivariate polynomial in a hexagonal coordinate system based on the deployment information about expected locations of the sensor nodes [13]. The key management schemes mentioned belongs to the type of static key management schemes. Another type of key management scheme is kind of dynamic scheme in which keys are updated periodically or on demand as a response to node capture. Moharrum and Eltoweissy compared dynamic key management with static key management and, as the result, proposed an EBS (Exclusion basis system)-based dynamic key management scheme[16] . Eltoweissy et al. proposed a dynamic key management scheme called LOCK LOcalized Combinatorial Keying) which is an EBS-based hierarchical key management scheme that can only be used in hierarchical wireless sensor networks[19] . In [19], an IDS model for ad-hoc networks is presented following the behavioral paradigm. The IDS is decentralized and detection is made by clusters. A technique to safely elect the responsible node for monitoring each cycle was developed. This solution is expensive, thus being in adequate to a wireless sensor network. Doumit et al. proposed a selforganized criticality and stochastic learning based intrusion detection scheme that takes advantage of self organized criticality for a certain location based on an environment variable and uses a Hidden Markov Model to detect future anomalies[17]. Agah et al. proposed a non-cooperative game approach in which the key is to find the most vulnerable node in a sensor network and protect it . Silva et al. defined multiple rules that can be used to determine if a failure has happened and to raise an intrusion alarm if the number of failures exceeds a predefined threshold [14]. A newly proposed scheme, called the insider attacker detection scheme, takes into consideration of multiple attributes simultaneously in node behavior evaluation without the requirement for prior knowledge about normal or malicious sensor activities [20]. It has high accuracy and low false alarm rate when some sensor nodes are misbehaving.

542

4. INTRUSION DETECTION IN WIRELESS SENSOR NETWORK Wireless sensor network uses cryptography provides protection against some types of attacks from external nodes, but it will not protect against malicious inside nodes, which already have the required cryptographic keys. Therefore, intrusion detection is an important aspect within the broader area of computer security, in particular network security, so an attempt to apply the idea in WSNs makes a lot of sense. An Intrusion Detection System (IDS) is a defense system, which detects malicious activities in a network. One feature of intrusion detection systems is their ability to detect a third party’s attempts of exploiting possible insecurities and warn for malicious attacks, even if these attacks have not been experienced before.In figure2, we represent the overview of the IDS analysis process. IDS tools are capable of distinguishing between attacks coming from own employees or customers and attacks posed by hackers.

IDS Specifications

Description of IDSs

original form while the traffic reconstruction would be theoretically possible for an arbitrarily powerful system, a NIDS faces performance and implementation constraints. Therefore the NIDS engine should implement some contextawareness functionalities. b) Host-based A host-based IDS (HIDS) monitors a single machine (or a single application) and audits data traced by the hosting operating system (or application). Typical examples of audited data are system calls (their parameters and their order), resource usage, and/or system logs. HIDSs have been deployed first to attempt the detection of malicious or unauthorized events against computer systems: the first implementations were mainly designed to analyse system logs [4]. Nowadays, the host-based approach is less attractive than a decade ago. First, modern operating systems have grown in complexity, “driven” by the explosive growth of the Internet, thus it is more difficult to achieve an extensive monitoring. Secondly, system administrators are usually concerned about the impact of an HIDS on host performance.

IDS Descriptions Analysis of IDSs

Attacks

Classification Descriptions and of Attacks

Generalized Alarms

Attack class Descriptions

Fig 2—Overview of the IDS analysis process

5. INTRUSION DETECTION ARCHITECTURE In sensor networks, most adversaries would target the routing layer, since that allows them to take control of the information flowing in the network. Besides, sensor networks are mainly about reporting data back to the base station, and disrupting this process would make an attack a successful one. So, for such networks, the most appropriate architecture for IDS would be network-based, as opposed to host-based. 5.1 IDS Architectures a) Networked Based A network-based IDS (NIDS) monitors a network segment and analyse the traffic which flows through the segment. The NIDS detection engine can analyse different data: network streams (i.e., connection properties such as endpoints, bytes exchanged by peers, connection time) or network payloads. The main advantage of the NIDS approach is the possibility to monitor data and events without affecting host performance. On the other hand, the common problem for the NIDS is the reconstruction of network traffic. Data streams are split into TCP segments and IP data grams. In order to analyse the content, the system needs to reassemble the traffic into

5.2 Wireless Network’s IDS Architectures Furthermore, in [3] Brutch and Ko divide wireless ad-hoc network IDS architectures into three categories. This classification can be adjusted to the needs of WSN IDS. a) Stand-alone In this category each node operates as a independent IDS and is responsible for detecting attacks only for itself. Such an IDS does not share any information or cooperate with other systems. This architecture implies that all the nodes of the network are capable of running IDS. b) Distributed and Cooperative The second architecture is the distributive and cooperative architecture. In this case, an intrusion detection agent still resides on each node (as in the case of the stand- alone architecture) and nodes are still responsible for detecting attacks against themselves (local attacks), but also cooperate to share information in order create a global intrusion detection mechanism [3]. c) Hierarchical These architectures are suitable for multi-layered wireless sensor networks. In this case the network is divided into

543

clusters with cluster-head nodes. These nodes are responsible for routing within the cluster and accept all the accusation messages from the other cluster members indicating something malicious. Additionally, the cluster-head nodes may also detect attacks against the other cluster-head nodes of the network, as they constitute the backbone of the routing infrastructure. 6. PROPOSED METHODOLOGY Pair based abnormal node detection system required to make a sensor network platform where the whole network will be segregated into number of pairs. A pair consists of two member node where they are adjacent to each other. Many pairs constitute wireless sensor network group. Nodes can send and receive message with other node where source and destination location can be within pair, pair–to-pair or groupto-group. Special algorithm is used for making pairs between adjacent nodes. Predefined attributes of nodes like distance between nodes, energy level, initial request response time etc can drive the algorithm to make decision to form pair in the network. New node occurred into the network then searches for the single node which wants to make a pair. If there is no single node available then new node will advertise that it wants to make pair. The first pair in the network is considered as the central pair of the group which is responsible for the group controlling and group-to-group communication. Once a pair is formed then each node has the responsibility to detect the abnormality of the other node in that particular pair using the locally and centrally deployed knowledgebase (databases). As a result, one node is completely dedicated to find out the abnormal condition of one node only which will obviously produce more better, accurate and reliable result than other approach.

request message between nodes are verified by central key management engine. Central Signature Management key is always communicating with local detection engine for verification and up gradation. Advantages The main advantage of signature based detection is that it can accurately and efficiently detect instances of known attacks. Another advantage , however, is that the signature based intrusion detection system requires less computation in order to identify intruders as the comparison of network events to the available signatures is relatively low cost [9]. Disadvantages Unfortunately, there are some significant flaws in this approach that render the IDS incapable of recognizing attacks. As network speeds increase, the IDS sensor does not have the resources to look at every packet, so some packets are discarded, allowing attacks to slip by unnoticed by the sensor. Most IDS sensors can only operate effectively up to about 60Mb/sec. Higher data speeds generally decrease their detection rate and increase their false positive rate considerably, thus reducing their effectiveness. However, since it can be difficult to characterize attacks on wireless sensor networks, such databases may be inherently limited and difficult to generate.

By dividing the network into many numbers of pairs, naturally it is reducing the problem space and also decreases the complexity of the algorithm. All the IDS security solutions proposed so far can be classified into two main categories: prevention based techniques and detection based techniques [5]. Prevention based techniques, such as encryption and authentication etc. and detection base techniques are designed to identify and isolate attackers after prevention based procedures fail [7][8]. This system uses two types of detection based approach for detecting abnormal node in wireless sensor network: First one is Signature based detection or misuse detection; second one is Anomaly based detection. 1) Signature based detection system Signature based detection system is deployed for secure communication between pair-to-pair nodes and group-togroup communication. Data exchanged between nodes contains defined packet format. Two types of tag are added to the packet format to make sure the maximum security. One is specific to message ID and other one is system specific DSA/RSA key. The format can be encoded and decoded by those pair only who keeps continuous communication with central engine which is dynamic always. Response and

544

Fig 3 : Proposed System Model for Abnormal Node Detection

2) Anomaly based detection Anomaly based detection: This approach of detection technique identifies unexpected and abnormal behavior in the network. This is typically a good approach for unknown problems but still it can be prone to false positives [6]. Known behaviors are stacked in the knowledge base and it is used by local detection engine to identify the problem pattern. Local knowledgebase contains the information about the attribute (like distance between them) of the adjacent node. This information is used by the local detection engine to identify the abnormal condition of the adjacent node. In figure 3 we represent the proposed system model for abnormal node detection. Central knowledgebase contains information of every pair in a group and also other group’s information. It’s always updating the database and sinks with the all local knowledgebase so that all local detection engines can be updated about the anomaly behavior of the group and outside the group. Similarly new findings of local databases also should be updated in central databases. In any test case, local detection engine first consult with local knowledgebase and if fails then contact with Central knowledgebase to identify the problem. This methodology ensures the development of the most valued knowledgebase to identify abnormal node detection in a wireless sensor network. The more the lifetime of the network, the system can give more accurate result. The final development of the proposed system can fill up the gap to detect abnormal node in wireless sensor network. Advantages The advantage of using an anomaly based system is that it is able to detect previously unknown attacks based only upon knowing that the system behavior is unusual. Another advantage which makes it suitable for an ad hoc network is that there is no need to store a database for attack profiles [10]. Disadvantages The idea with anomaly detection is appealing but the method has many drawbacks. The first is that the anomaly based approach is susceptible to high false positives. This is due largely to the fact that it can be difficult to define normal system behaviors. To help combat this, new profiles can be taken of the network to ensure that the profile in use is up-todate. However, this takes time. And further, even with the most up-to-date profile possible, it can still be difficult to discern unusual, but legitimate, behavior from an actual intrusion. Another fault in the anomaly based intrusion detection techniques is that the computational cost of comparing the current system activity to the profile can be quite high [9]. In the case of a wireless sensor network, such added computation can severely impact the longevity of the network. 7. CONCLUSION WSN pose unique challenges and because of this traditional security threats that all the other wireless network face can not

assumed for WSN. The very common threat to wireless sensor network communication is abnormal node detection. Mission critical wireless sensor networks require an efficient, lightweight and flexible intrusion detection methodology to identify abnormal node or malicious attackers. In this paper, we explore intrusion detection schemes that can be used to improve security from both external and internal eavesdroppers. The proposed idea in this paper is to develop a new approach of abnormal node detection in wireless sensor network using IDS security routing methodology by dividing the network into number of pairs. Every node of each pair is responsible to identify abnormality of other node in that particular pair considering different attributes of nodes. The abnormal node detection algorithm uses both signatures and knowledge based routing methodology to achieve most accurate and reliable result. REFERENCES [1] F. Liu, X. Cheng, F. An, “On the Performance of In-Situ Key Establishment Schemes for Wireless Sensor Networks,” in IEEE GLOBECOM 2006, San Francisco, CA, November 27-Decrmber 1, 2006. [2] C. Karlof and D. Wagner, “Secure routing in wireless sensor networks: Attacks and countermeasures”, Ad Hoc Networks, Vol. 1, No. 2-3, 2003, pp. 293-315. [3] P. Brutch and C. Ko. Challenges in intrusion detection for wireless adhoc networks. In 2003 Symposium on Applications and the Internet Workshops (SAINT’03 Workshops), 2003. [4] J.P. Anderson. Computer Security Threat Monitoring and Surveillance. Technical report, James P. Anderson Co., Fort Washington, PA, April 1980. [5] Guorui Li, Jingsha He, Yingfang Fu “A Distributed Intrusion Detection Scheme for Wireless Sensor Networks”, 28th International Conference on Distributed Computing Systems (ICDCS). [6] “Secure Routing and Intrusion Detection in Ad Hoc Networks”, 3rd International Conference on Pervasive Computing and Communications (PerCom 2005), Kauai Island, Hawaii”. [7] S. Marti, T.J. Giuli, K. Lai and M. Baker, “Mitigating Routing Misbehavior in Mobile Ad Hoc Networks”, Mobicom 2000. [8] Y. Zhang and W. Lee, “Intrusion Detection in Wireless Ad Hoc Networks”, Mobicom 2000 [9] I. Sato, Y. Okazaki, and S. Goto. An improved intrusion detection method based on process profiling. IPSJ Journal, 43(11):3316–3326, 2002. [10] A. Mishra, K. Nadkarni and A. Patcha, Intrusion detection in wireless ad hoc networks, IEEE Wireless Communications, vol. 11, no. 1, Feb 2004, pp. 48 – 60. [11] S. Singh, M. Woo and C. S. Raghavendra, "Power-Aware Routing in Mobile Ad Hoc Networks", Proc. Of ACM MOBICOM'98, Dallas, Texas, October 1998 [12] D. Estrin, R. Govindan, J. Heidemann, and S. Kumar. Scalable coordination in sensor networks. Proc. of ACM/IEEE MobiCom 1999, Seattle, Washington, August 1999. [13] G. Li, J. He and Y. Fu, “Key management in sensor networks”, in Proc. International Conference on Wireless Algorithms, Systems and Applications 2006, August 2006, pp. 457-466. [14] A. P. da Silva, M. H. Martins, B. P. Rocha, A. A. Loureiro, L. B. Ruiz, H. C. Wong, “Decentralized intrusion detection in wireless sensor networks,” in ACM Q2SWinet’05, 2005. [15] K. Ilgun, R. A. Kemmerer, and P. Porras, State transition analysis: A rule-based intrusion detection approach, IEEE Trans on Software Engineering, 21 (1995), pp. 181–199. [16] M. Moharrum, M. Eltoweissy and R. Mukkamala, “Dynamic key management in sensor networks”, IEEE Communications. Vol. 44, No. 4, 2006, pp. 122- 130.

545

[17] S. Doumit and D.P. Agrawal, “Self-organized criticality & stochastic learning based intrusion detection system for wireless sensor network”, in 2003 IEEE Military Communications Conference, Vol. 22, No. 1, 2003, pp. 609-614. [18] K. Ilgun, Ustat: A real-time intrusion detection system for unix, in Proc of IEEE Computer Society Symp on Research in Security and Privacity, May 1993. [19] Y. an Huang and W. Lee, A cooperative intrusion detection system for ad hoc networks, in Proc of the 1st ACM Workshop on Security of Ad hoc and Sensor Networks, 2003, pp. 135–147. [20] F. Liu, X. Cheng and D. Chen, “Insider Attacker Detection in Wireless Sensor Networks”, in 26th IEEE International Conference on Computer Communications, 2007, pp. 1937-1945.

546

National Conference on Advanced Computing and Communication Technology

Connectivity-based Localization schema for Wireless Sensor Network 1

Sanjay Kumar Sonker, 2 Amit Kumar Singh

M. Tech (CSE), USIT , GGSIP University, Delhi(INDIA) [email protected]

technology. Wireless sensor networks (WSN) hold a promise to “dwarf previous revolutions in the information revolution”. WSN are envisioned to consist of hundreds to thousands of sensor nodes communicating over a wireless channel, performing distributed sensing and collaborative data processing tasks for a variety of vital military and civilian applications. 2. RELATED WORK Connectivity-based Multi-hop localization algorithm In this paper, non-anchor nodes are not essentially one hop neighbors to anchor nodes. Here we don’t need any measurement techniques like RSSI, AOA etc. but generally uses connectivity information, i.e.” who is within the communication range of whom”.[1] to calculate the location of sensor nodes. Here we have focused idealized model that makes two assumptions: • Perfect spherical radio propagation

ABSTRACT This paper examines localization error will be minimal after adjusting the radio range of beacons’ and error performance tradeoffs. The other type of deployment is random which is performed by robot, airplane or by any other means. This is another issue how anchor node deployment should be optimal. At low and medium density areas we can add more number of beacons to improve the quality of localization. At high density areas it is required how do we coordinate densely deployed beacons so as to diminish channel contention. Anchor based-rangefree schema for Wireless Sensor Network. Localization algorithms are different in different network environments. Anchor nodes are special nodes that know their physical location. Anchor nodes are connected to GPS to find out their location. Wireless sensor nodes that do not know their location are known as unknown nodes. Unknown nodes communicate with anchor nodes. Anchor nodes use beacons and broadcast it within the range and make the proximity. Keywords- WSN,GPS, MATLAB.

•

Identical transmission (power) for all radios.

range

Various nodes in deployed with overlapping areas of coverage termed as reference points (labeled R 1 to R n ). Let’s assume these anchors are placed at defined positions, (X 1 , Y 1 )–(X n , Y n ), this formed a regular mesh type structure and broadcasts its beacon at regular time period (period = T) having their relevant positions. Here a certain assumption is followed that neighboring reference points might be coordinated in order that their beacon signal transmissions may not overlap in

1. INTRODUCTION Now days, advances in miniaturization, Wireless Sensor Networks have become very famous in different applications in the present life. These are benefited for control and monitoring like, target tracking, civil and military applications, disaster management, habitat monitoring, climate control etc. A wireless sensor node can be built easily using small electronic cheap 547

National Conference on Advanced Computing and Communication Technology

time. In addition to that, in any time period T, each reference node would have broadcasted exactly one beacon signal [3]. Each sensor node snoops for a given time period t and assembles all the beacon signals it gets from different reference points. In this paper, Bulusu et al. defined the information per reference node R i by a connectivity metric (CM i ), that is:

3. TESTING METHODOLOGY AND SIMULATION RESULTS Before starting the work, I want to discuss two assumptions for idealized model before working on Centroid algorithm that is based on range free localization procedure. 1- Radio propagation will be perfect spherical. 2- All sensor nodes will have identical radio range.

For reliability in presence of various environmental vagaries, we have to configure our connectivity metric with a sample of S, where S is the sample size that tuned with total sending beacons (i.e., N sent (i, t) = S). Further we have to configure T to be the time interval between two consecutive beacon signal transmissions, we set t, the receiver’s sampling time[3] as: t = (S + 1 – e)T (0 < e « 1). When sensor nodes receive the beacons, then node make a proximity to the reference points and we have to set our connectivity metric, CM thresh (lets 90 percent). The receiver computes itself to the area with the intersection of the connectivity areas of this set of anchor nodes, that is named as the centroid of these reference points: So we use multitrilateration property to find node location.

As we know in centroid method, we have to deploy anchor nodes or reference nodes within applied field where unknown nodes deploy randomly in the specified coverage field. 3.1 Deployment of anchor nodes Here we start two types of deployment of anchor nodes: • •

Grid based deployment Random deployment

Random deployment: The other type of deployment is random which is performed by robot, airplane or by any other means. This is another issue how anchor node deployment should be optimal. At low and medium density areas we can add more number of beacons to improve the quality of localization. At high density areas it is required how do we coordinate densely deployed beacons so as to diminish channel contention. Now we make same infrastructure with 90m x90m deployment area where 100 anchor and 100 non-anchor nodes are deployed randomly and then this centroid algorithm is applied on this network. After deployment randomly we find the poor quality of localization overall because the probability of forming regular geometry or regular closed proximity of neighbor nodes are very less or random. So for making acceptable our algorithm and to improve the overall localization quality up to some extent, we apply some other technique in

But the accuracy depends on how far estimated location is to actual location, so we have to compute localization error. Localization error (LE) is defined as: Hence, to make a better result we should use more reference nodes to become localization area finer, and we should deploy nodes on grid to fine the covered localization area. Ultimately the ratio(R/d) must be increase to perform better result so that the localization error might be minimized.

548

National Conference on Advanced Computing and Communication Technology

order to maintain our localization system compatible, cost effective and power efficient. 4. PROPOSED SOLUTIONS AND SIMULATION RESULT So far, we have simulated the grid based localization and random localization. In grid based system, after adjusting the ranges of beacon we can make acceptable solution for localization and range will lie in between grid length and its double of the length. But in random deployment we cannot make such acceptable arrangement for reducing the localization error. As diminishing our localization error is our main objective in case of developing the best algorithm related to wireless sensor network. So here, we will try to reduce such localization error.

Centroid 0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0

10

20

30

40

50

60

70

80

90

100

Fig 4.2 Error variation per node within network

In old deployment the anchor nodes and non-anchor nodes are applied same working areas in 90m x90m working region. But if we confine the sensing region for testing nodes over keeping anchor node region same, then its beneficial and applicable to reduce the localization error up to some extent. So if we deploy testing nodes within 80m x80m working region with keeping anchor nodes in 90m x90m working region then the scenario will be totally changed and the testing nodes which were falling at edges now will be covered with some extent with anchor sensing field. So localization error of sensor network might be reduced up to desirable level.

Random Deployment strategy: If we deploy the optimal anchor nodes to achieve good quality of localization, yet there will be such borders areas where the non-anchor nodes will strive for regular geometry to overcome the closed proximity neighbor in adequate quantity. So more nodes falling at the edges of sensing field, will overall make poor localization. In figure 4.1 it is shown that at edges localization error is greater. Centroid 100 90

5. COMPARISON BETWEEN NEW AND OLD DEPLOYMENT STRATEGY:

80 70

Y->

60

After simulating the both condition we find that error is reduced at some level in new strategy as compare old deployment strategy. Here we take two conditions.

50 40 30

First Condition:

20

Here we test with 100 anchor and 100 testing nodes within 90m x90m working area in first condition. Now we apply same centroid algorithm with changing radio ranges of reference nodes and take many samples. We get the mean localization error with accordingly different radio

10 0

0

10

20

30

40

50 X->

60

70

80

90

100

Fig4.1 Random deployment of anchor nodes in 90x90 and testing nodes Within 80x80 working field.

549

National Conference on Advanced Computing and Communication Technology

ranges of anchor points. These samples are shown in table 4.1. In this condition firstly localization error are reduced with increasing radio range of anchors but further increasing ranges the localization error increases due to increase the irregularity of anchor points.

Now here both tables are combined and find the relative errors. Here we individually calculate the corresponding mean error reduction in both conditions.

6. GRAPH BETWEEN THESE TWO

Table 5.1 Range of anchor

10

15

20

25

30

Mean localization error

0.6068

0.3032

0.2760

0.2575

0.2674

Second Condition: Here we reduced the working deploying area of testing node. The anchor nodes are deploying within 90m x90m sensing province but testing nodes within 80m x80m region. Then we apply same centroid algorithm for this strategy. After simulating this condition we find the mean localization error samples in accordance to different ranges of anchor nodes. Theseoutcomes are shown in table 5.2.

STRATEGIES DEPENDING UPON RESULTS. Now here, in figure 6.1 two graphs are shown. After analyzing both condition, we can calculate that the localization error in both condition. So applying in second condition the average reduction of localization error is 5% of total mean localization error. So in second strategy we reduced 5% localization error after converging the deploying area for testing

Table5.2 Range of anchor

10

15

20

25

30

Mean localization error

0.5090

0.2458

0.2127

0.2345

0.2550

Table 5.3 Range of

10

15

20

25

30

anchor

Mean localization

0.6068

0.3032

0.2760

0.2575

0.2674

0.5090

0.2458

0.2127

0.2345

0.2550

6.33

2.3

1.24

error –I Mean localization error –II Reduction

9.78

5.75

of error (%)

550

Fig 6.1 a graph between mean localization error and beacon range CONCLUSIONS & FUTURE WORK This paper introduces and discusses wireless sensor networks and localization concepts. In random deployment scheme, we shorten the testing node deployment working area but keeping anchor node deploying area normal. After applying the same procedure we find that 5% error is reduced. So if we compare it with other algorithm, the smart feature of the connectivity-based localization scheme is their simplicity. In my future work to

National Conference on Advanced Computing and Communication Technology

comparison between Grids based deployment and Random deployment.

BIOGRAPHICS:

REFERENCES: [1] Guoqiang Mao Baris,Fidan and Brian D.O.Anderson,“ Wireless Sensor Network Localization Techniques “ . [2] Achieving High- Accuracy Distributed Localization in Sensor Network. [3]Nirupama Bulusu, John Heidemann, and Deborah Estrin. “GPS-less low cost outdoor localization for very small devices.” IEEE Personal Communications Magazine, 7(5):28–34, October 2000. [4] I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks,” IEEE Communication Magazine, vol. 40, pp. 102–116, Aug. 2002.

Sanjay Kumar Sonker Currently pursusing M.Tech(CSE) from USIT, Guru Gobind Singh Indraprastha University,Delhi-06.I have completed my B.Tech(IT) from UPTU.I have published a paper on topic “Anchor Based-Range Free Localization Schema for Wireless Sensor Network” in National conference. Area of interests are computer networking, wireless network.

[5] L. Doherty, K. S. J. Pister and L. El Ghaoui, “Convex position estimation in wireless sensor networks,” IEEE INFOCOM, vol. 3, pp. 1655-1663, April 2001 [6] D. Niculescu and B. Nath, “Ad-hoc positioning system,” IEEE GLOBECOM, vol. 5, pp. 2926-2931, Nov. 2001

Amit Kumar Singh Currently pursusing M.Tech(CSE) from USIT, Guru Gobind Singh Indraprastha University,Delhi-06.I have completed my B.Tech(IT) from CSA University, Kanpur(U.P)

551

National Conference on Advanced Computing and Communication Technology

Modbus Communication over Zigbee Wireless Sensor Network for Better Interactivity Isha1, Nitin Sharma2, Manisha3, Parvinder4 1

2

Lecturer Bhagwan Pershuram College of Engineering Gohana, India Assistant Professor N.C. College of Engineering Israna, Panipat, India 3 Senior lecturer karnal institute of technology and management karnal, India 4 Principal I.P polytechnic college Pundri, kethal, India

[email protected],[email protected],[email protected],dhiry2kgmail.com [email protected]

Abstract— ZigBee is the emerging industrial standard for ad hoc networks based on IEEE 802.15.4. Due to characteristics such as low data rate, low price, and low power consumption, ZigBee is expected to be used in wireless sensor networks for remote monitoring, home control, and industrial automation. Since one of the most important goals is to reduce the installation and running cost, ZigBee stack is embedded in small and cheap micro-controller units. . However, most successful business cases still rely on mobile tools, such as PDA, WIFI, RFID, and GPS, to realize the concept of seeming to be everywhere at the same time. This work emplies the use of modbus protocol with the 802.15.4 zigbee based wireless communication network system. For real time accessing of the data, we require a protocol that can provide the real time debugging of the data, this can be acheived by using the modbus protocol as it is using raw bits without proposing so many restrictions to the vendors and most important it is open source. This modbus protocol is implemented in the 802.15.42006 based zigbee stack this has been provided by MAXSTREAM. The microcontroller used here is atmega8 with the arduino based boards that itself is a open source hardware.

Because ZigBees can sleep most of the time, average power consumption can be very low, resulting in long battery life. ZigBee protocols are intended for use in embedded applications requiring low data rates and low power consumption. ZigBee's current focus is to define a generalpurpose, inexpensive, self-organizing mesh network that can be used for industrial control, embedded sensing, medical data collection, smoke and intruder warning, building automation, home automation, etc. The resulting network will use very small amounts of power — individual devices must have a battery life of at least two years to pass ZigBee certification. There are three different types of ZigBee devices:

Keywords—Zigbee, ATMega8, Modbus Protocol

1.

Introduction

ZigBee is a low-cost, low-power, wireless mesh networking proprietary standard. The low cost allows the technology to be widely deployed in wireless control and monitoring applications, the low power-usage allows longer life with smaller batteries, and the mesh networking provides high reliability and larger range.The ZigBee Alliance, the standards body that defines ZigBee, also publishes application profiles that allow multiple OEM vendors to create interoperable products. Because ZigBee can activate (go from sleep to active mode) in 15 msec or less, the latency can be very low and devices can be very responsive — particularly compared to Bluetooth wake-up delays, which are typically around three seconds.

ZigBee coordinator (ZC): The most capable device, the coordinator forms the root of the network tree and might bridge to other networks. There is exactly one ZigBee

552

National Conference on Advanced Computing and Communication Technology coordinator in each network since it is the device that started the network originally. It is able to store information about the network, including acting as the Trust Centre & repository for security keys.

two clock cycles, making AVRs relatively fast among the eight-bit microcontrollers. The AVR family of processors were designed with the efficient execution of compiled C code in mind and has several built-in pointers for the task.

ZigBee Router (ZR): As well as running an application function, a router can act as an intermediate router, passing on data from other devices.

3 WHAT IS MODBUS PROTOCOL Modbus is a serial communications protocol for use with its programmable logic controllers (PLCs). It has become a de facto standard communications protocol in industry, and is now the most commonly available means of connecting industrial electronic devices. Modbus allows for communication between many devices connected to the same network, for example a system that measures temperature and humidity and communicates the results to a computer. Modbus is often used to connect a supervisory computer with a remote terminal unit (RTU) in supervisory control and data acquisition (SCADA) systems.

ZigBee End Device (ZED): Contains just enough functionality to talk to the parent node (either the coordinator or a router); it cannot relay data from other devices. This relationship allows the node to be asleep a significant amount of the time thereby giving long battery life. A ZED requires the least amount of memory, and therefore can be less expensive to manufacture than a ZR or ZC.

2. WHAT IS ATMEGA8 ATmega8 is a microcontroller from Atmel AVR family. Other microcontrollers from that family, it has an 8-bit RISC CPU core. Both ATmega8 and ATmega16 are very popular and inexpensive microcontrollers. The very popular Arduino project uses ATmega16, but has an ATmega8 version. Atmel company provides some software tools to work with AVR microcontrollers, but there are also enough free software tools for that.The original AVR MCU was developed at a local ASIC house in Trondheim, Norway, where the two founders of Atmel Norway were working as students. It was known as a μRISC (Micro RISC). When the technology was sold to Atmel, the internal architecture was further developed by Alf and Vegard at Atmel Norway, a subsidiary of Atmel founded by the two architects.Atmel says that the name AVR is not an acronym and does not stand for anything in particular. The creators of the AVR give no definitive answer as to what the term "AVR" stands for. Note that the use of "AVR" in this article generally refers to the 8-bit RISC line of Atmel AVR Microcontrollers.Among the first of the AVR line was the AT90S8515, which in a 40-pin DIP package has the same pinout as an 8051 microcontroller, including the external multiplexed address and data bus. The polarity of the RESET line was opposite (8051's having an active-high RESET, while the AVR has an active-low RESET), but other than that, the pinout was identical. Flash, EEPROM, and SRAM are all integrated onto a single chip, removing the need for external memory in most applications. Some devices have a parallel external bus option to allow adding additional data (or code) memory, or memorymapped devices. All devices have serial interfaces, which can be used to connect larger serial EEPROMs or flash chips. Atmel's AVRs have a two stage, single level pipeline design. This means the next machine instruction is fetched as the current one is executing. Most instructions take just one or

553

Modbus RTU Frame Format Name Start

Length 4 bits

Function 4 bits of idle charactors

Address 8 bits

Station Address

Function 8 bits

Indicates the function codes like read coils / inputs

Data

n*8 bits

Data + length will be filled depending on the message type

CRC Check

16 bits

Error checks

End

4 bits

4 idle characters indicating end of frame

Fig 1 General modbus frame

National Conference on Advanced Computing and Communication Technology But modbus protocol have some limitations Modbus is a master/slave protocol, there is no way for a field device to "report by exception" (except over Ethernet TCP/IP, called open-mbus)- the master node must routinely poll each field device, and look for changes in the data. This consumes bandwidth and network time in applications where bandwidth may be expensive, such as over a low-bit-rate radio link fig 1 show the frame size of a modbus. Modbus is restricted to addressing 255 devices on one data link, which limits the number of field devices that may be connected to a master station

I. IMPLEMENTATION FRAMWORK Implementation frame work contain both hardware platform work as well as software platform In the hardware platform we use Arduino board

u8ModbusADU [u8ModbusADUSize++]=(u16WriteRegister[low Byte(u16QtyWrite)]); break; case ku8MBWriteMultipleCoils u8ModbusADU [u8ModbusADUSize++] = (u16QtyWrite); u8ModbusADU [u8ModbusADUSize++] = (u16QtyWrite); u8Qty = (u16QtyWrite % 8) ((u16QtyWrite >> 3) + 1): (u16QtyWrite >> 3); u8ModbusADU [u8ModbusADUSize++] = u8Qty; for (i = 0; i < u8Qty; i++) { Switch (i % 2) { case 0: // i is even u8ModbusADU [u8ModbusADUSize++]=l(u16WriteRegister[i >> 1]); Break;

II. Limitation of the system In the Modbus protocol communication mechanism each Node has a address, we called Modbus ID. In the ZigBee Wireless network each node has a 64 bit IEEE address, which is a constant, Each 64 bit IEEE address[3]. Corresponds to a Modbus address. Modbus address scope is From 1 to 255, which is a 8 bit address. So The limitation of the system is that only apply to middle and small network[1] [14].

III. Conclusions This board contains ATmega8 microcontroller An XBee ZNet 2.5 Module [13]. For software we use Arduino IDE in which we use C, C++ Low level languages Switch (u8MBFunction) { Case ku8MBWriteSingleCoil u8ModbusADU [u8ModbusADUSize++]= (u16QtyWrite); u8ModbusADU [u8ModbusADUSize++]=(u16QtyWrite); Break;

The ZigBee WSN developed with Modbus protocol find significance due to following advantages: The wireless sensors system is very convenience in the course of the system installation. The 802.15.4 technology makes the power consumption very low. The Modbus protocol provides a friendly interface for the system observation. Modbus protocol as a mature field bus standard, which provide a general interface for the system. So we can use this interface to connect with GPRS, Industry Ethernet and so on. So this system can be expanded well.

Case ku8Mb u8ModbusADU [u8ModbusADUSize++](u16WriteRegister[low Byte(u16QtyWrite)]);

554

National Conference on Advanced Computing and Communication Technology

REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9]

[10] [11] [12] [13] [14]

IEEE STD 802.15.4[S].www.zigbee.org. ZigBee Technology: Wireless Control that Simply Works LioYanfei,wang Chenge,Yu Chengbo Research n zigbee wireless sensor network based on modbus protocol Liting Cao, Wei Jiang, Zhaoli Zhang..Networked wireless meter Reading system based on ZigBee technology. Control and Decision Conference, 2008.Chinese.2008 P3455 – 3460. Wan-Ki Park, hang-Sic Choi, Jinsoo Han, Intark Han. Design and Implementation of ZigBee based URC Applicable to Legacy Home Appliances.Consumer Electronics, 2007. ISCE 2007. IEEE International Symposium.2007.P1-6. Zhou Yiming, Yang Xianglong, Guo Xishan, Zhou Mingang, Wang Liren. A Design of Greenhouse Monitoring & Control System Based on ZigBee Wireless Sensor Network. Wireless Communications, Networking and Mobile Computing, 2007. WiCom 2007. International Conference.2007 .P2563 – 2567. Modbus Application Protocol Specification. [S]. www.ModbusIDA.org. Texas Instruments. Datasheet. CC2430 [Z].www.ti.com. WXL. Zigbee Stack User Guide [Z].WWW.C51rf.com

555

National Conference on Advanced Computing and Communication Technology

IMPACT OF COEXISTENCE ON HETEROGENEOUS NETWORKS Neetu Sharma & Amanpreet Kaur [ [email protected] , amanpreet @itmindia.edu] Institute of Technology and Management, Maharshi Dayanand University Gurgaon (Haryana), India

Abstract— In the recent years, the wireless technology has shown an exponential growth, which has great impact on developing and improving the field of telecommunications beyond the means of transmission wire to the radio frequency communication. Growth is due to emergence of new standards and technologies (Infrared, Wi-Fi, Bluetooth, UMTS, WiMAX, Zigbee, UWB etc.).The choice of technology from multiple ones, according to the needs and a range of situations is very important. As the explosive growth of the ISM band usage continues, there are many scenarios where different systems operate in the same place at the same time. One of growing concerns is the coexistence of heterogeneous wireless network systems Coexistent heterogeneous wireless networks may interfere with each other and result in significant performance degradation when devices are collocated in the same environment. For the successful deployment of mission-critical systems such as wireless sensor networks, it is required to provide a solution for the coexistence. With the increasingly deployed Wireless Personal Area Network (WPAN) and Wireless Local Area Network (WLAN) devices, channel conflict has become very frequent and severe when one WPAN technology coexists with other WLAN technologies in the same interfering range. In this work, we considered a heterogeneous network and analyzed the coexistence issue between IEEE 802.15.4 and IEEE 802.11b. To evaluate the performance of this network, measurement and simulation study are conducted and developed in the QualNet Network simulator , version 5.0. Model is analyzed for different placement models or topologies such as Random . Grid & Uniform . Performance is analyzed on the basis of characteristics such as throughput, average jitter and average end to end delay .Here, the impact of varying different antenna heights for this heterogeneous network is considered for the purpose of analysis.

I INTRODUCTION As a low-power and low-cost technology, IEEE 802.15.4, is establishing its place on the market as an enabler for the emerging wireless sensor networks. The goal of the IEEE 802.15.4 is to provide a standard, which has the characteristics of ultra-low complexity, low-cost and extremely low-power for wireless connectivity among inexpensive, fixed, and portable devices such as sensor networks and home networks .To provide the global availability, the IEEE 802.15.4 devices use the 2.4GHz industrial scientific and medical (ISM) unlicensed band. Because this ISM band is commonly used for the low cost radios such as IEEE 802.11b (WLAN) and IEEE 802.15.1 (Bluetooth),an unrestricted access to the ISM

556

band exposes the IEEE 802.15.4 devices to a high level of interference. Since the IEEE 802.15.4 and the IEEE 802.11b have been designed for different purposes, they can be collocated within the communication range of each other. For example, the IEEE 802.15.4 network is used for a sensor and control network and the IEEE 802.11b network is used for audio/video (A/V) network within a home. When a notebook is capable of supporting these two standards, the collocation distance may be smaller than 1 m. Therefore, the coexistence performance of the IEEE 802.15.4 and the IEEE 802.11 needs to be evaluated. Some related researches study the coexistence problem between the IEEE 802.15.4 and the 802.11b[5],[6].In [5], the packet error rate (PER) of the IEEE 802.15.4under the IEEE 802.11b and IEEE 802.15.1 is obtained by experiments only. In [6], the impact of an IEEE 802.15.4 network on the IEEE 802.11b devices is analyzed. In [3], the Packet Error Rate (PER) of IEEE 802.15.4 under the IEEE 802.11b interference is analyzed from an assumption of blind transmissions, i.e. both IEEE 802.11b and IEEE 802.15.4 transmit packets regardless of whether the channel state is busy or not. In [4], measurements are performed to quantify coexistence issues .According to the literature survey IEEE 802.15.4 has a little impact on the IEEE 802.11performance. However, IEEE 802.11 can have a serious impact on the IEEE 802.15.4 performance if the channel allocation is not carefully taken into account [1] [3]. While the conclusion is true in general, we believe the studies so far have dealt with only limited cases of coexistence scenarios. The author concluded that despite its low transmit power and simple modulation technique, IEEE 802.15.4 shows a robust behavior against interference of other 2.4 GHz systems and the worst case conditions for frequency overlap, local distance and high traffic load for interference. Once again, here we shall consider the impact of coexistence in a heterogeneous network consisting of Zigbee & WLAN standard and analyse the performance of the network on different placement models or topologies .The remainder of the paper is organized as follows: Section II gives an overview of the interference problem, Section III discusses the coexistence issue. Section IV gives overview of simulation software used ,section V discusses different placement strategies, section VI discusses technology used, section VII discusses simulation setup, & parameters Simulation results are shown in Section VIII. Conclusion is drawn in Section IX.

National Conference on Advanced Computing and Communication Technology II INTERFERENCE PROBLEM Because both IEEE Std 802.11b and IEEE Std. 802.15.4 specify operations in the same 2.4 GHz unlicensed frequency band, there is mutual interference between the two wireless systems that may result in severe performance degradation. There are many factors that effect the level of interference, namely, the separation between the WLAN and WPAN devices, the amount of data traffic flowing over each of the two wireless networks, the power levels of the various devices, and the data rate of the WLAN. Also, different types of information being sent over the wireless networks have different levels of sensitivity to the interference .For example, a voice link may be more sensitive to interference than a data link being used to transfer a data file. This sub clause gives an overview of the mutual interference problem. Subsequent sub clauses describe the modeling of the mutual interference and give illustrations of the impact of this mutual interference on both the WLAN and WPAN networks. There are several versions of IEEE 802.11 physical (PHY) layer. All versions of IEEE 802.11 use a common MAC sublayer. When implementing distributed coordination function (DCF) the 802.11 MAC uses carrier sense multiple access with collision avoidance (CSMA/CA) for medium access control. The scope of this recommended practice is limited to DCF implementations of IEEE 802.11, and does not include point coordination function (PCF) implementations. Initially, 802.11 included both a 1- and 2-Mbit/s frequency hopping spread spectrum (FHSS) PHY layer, as well as a 1- and 2Mbit/s direct sequence spread spectrum (DSSS) PHY layer. The FHSS PHY layer uses a 1-MHz channel separation and hops pseudo-randomly over 79 channels. The DSSS PHY layer uses a 22 MHz channel and may support up to three nonoverlapping channels in the unlicensed band. Subsequently, the IEEE 802.11 DSSS PHY layer was extended to include both 5.5 and 11 Mbit/s data rates using complementary code keying (CCK). This high-rate PHY layer is standardized to be named IEEE 802.11b. This high-rate version includes four data rates: 1, 2, 5.5, and 11 Mbit/s. The channel bandwidth of the IEEE 802.11b PHY layer is 22 MHz. The WPAN covered in this recommended practice is Zigbee technology i.e. IEEE Std 802.15.4, which is is a low-cost, low-power, wireless mesh networking proprietary standard.It operates in the industrial, scientific and medical (ISM) radio bands; 868 MHz in Europe, 915 MHz in the USA and Australia, and 2.4 GHz in most jurisdictions worldwide Impact of IEEE 802.11b WLAN in the presence of IEEE 802.15.4 Zigbee interference is very important to consider in order to analyse the performance level of the system. Most implementations allow manual or automatic modification of the data rate. The higher rates are desirable for many applications but the distance of transmission using the higher rates is less than that of the lower rates. Many implementations automatically scale the data rate to the highest data rate that is sustainable to each WLAN mobile unit. There is a potential chances of packet collision between a WLAN packet and an IEEE 802.15.4 packet when the

WPAN device coexists with WLAN device. Because there are four data rates defined within IEEE 802.11b, the temporal duration of the WLAN packets may vary significantly for packets carrying the exact same data. The longer the duration of the WLAN packet, the more likely that it may collide with an interfering WPAN packet. One of the important issues that effects the level of interference is the WLAN automatic data rate scaling. If it is implemented and enabled, it is possible for the WPAN interference to cause the WLAN to scale to a lower data rate. At a lower data rate the temporal duration of the WLAN packets is increased. This increase in packet duration may lead to an increase in packet collisions with the interfering WPAN packets. In some implementations, this may lead to yet a further decrease in the WLAN data rate. This may result in the WLAN scaling down its data rate to 1 Mbit/s. The IEEE 802.11 MAC sublayer incorporates ARQ to insure reliable delivery of data across the wireless link. So there is little chance that the data will be lost. The effect of this on the WLAN device is that the delivered data throughput decreases and the network latency increases. The application’s requirements determine if these degradations are tolerable. III

COEXISTENCE ISSUE

Coexistence is defined as “the ability of one system to perform a task in a given shared environment where other systems may or may not be using the same set of rules” There are two categories of coexistence mechanisms: collaborative and non-collaborative. Collaborative coexistence mechanisms exchange information between two wireless networks. That is in this case a collaborative coexistence mechanism requires communication between the IEEE 802.11 WLAN and the IEEE 802.15.4 WPAN. Non-collaborative mechanisms do not exchange information between two wireless networks .These coexistence mechanisms are only applicable after a WLAN or WPAN are established and user data is to be sent. These coexistence mechanisms will not help in the process for establishing a WLAN or WPAN. Both types of coexistence mechanisms are designed to mitigate interference resulting from the operation of IEEE 802.15.4 devices in the presence of frequency static or slow-hopping WLAN devices (for example IEEE 802.11b).. When collocated within the same network, there needs to be a communication link between the WLAN and WPAN devices which could be a wired connection between these devices or an integrated solution.. Non-collaborative coexistence mechanisms are intended to be used when there is no communication link between the WLAN and WPAN. The three collaborative coexistence mechanisms defined consist of two MAC sublayer techniques and one PHY layer technique Both MAC sublayer techniques involve coordinated scheduling of packet transmission between the two wireless (WLAN and WPAN) networks. The PHY layer technique is a programmable notch filter in the IEEE 802.11b receiver to notch out the narrow-band IEEE 802.15.1 interferer. These collaborative mechanisms may be used separately or combined with others to provide a better coexistence mechanism. The collaborative coexistence

557

National Conference on Advanced Computing and Communication Technology mechanism provides coexistence of a WLAN (in particular IEEE 802.11b) and a WPAN by sharing information between the two collocated technologies and locally controlling transmissions to avoid interference. There are two modes of operation and particular mode is chosen depending on the network topology and supported traffic. The use of the unlicensed 2.4GHz ISM band avoids the need of regionalspecific solutions and governmental licenses, reducing costs and providing a free band available worldwide. Therefore, many wireless technologies are operating in the 2.400 – 2.500 GHz band. Coexistence studies typically focus on the major technologies operating in the ISM band and provide analytical or either empirical results on how they interfere each other in order to determine in which situations can do they fairly coexist. Table1

V PLACEMENT STRATEGIES This section describes different node placement methods in Qualnet Node placement can be done manually or by using Node Placement Wizard in this software.In the Grid Node Placement model, the terrain is divided into a number of squares. One node is placed at each grid point, starting at the origin or the south-west corner. The size of the squares is determined by a user-specified parameter, grid unit. In the Random Node Placement model, nodes are placed on the terrain randomly. In the Uniform Node Placement model, the terrain is divided into a number of equal-sized cells, each proportional to the size and shape of the terrain. The number of cells is calculated by rounding the number of nodes to the next higher square of an integer. Beginning with the lower left cell, proceeding first along the X-axis, then along the Y-axis, one node is placed at a random position in each cell, until all nodes are placed. VI

IV

SIMULATION SOFTWARE

QualNet is a commercial spin-off from the GloMoSim simulator, which was developed at the University of California, Los Angeles, UCLA, and is distributed by Scalable Network Technologies. The simulator itself is C++ based. All protocols are implemented in a series of C++ files, and called by the simulation kernel . QualNet comes with a java based graphical user interface . During simulation runtime, it allows the user to observe the signals being transmitted and received at each node, which aids in the understanding of what is physically happening. QualNet® is very scalable ,it provides a comprehensive environment for designing protocols, creating and animating network scenarios, and analyzing their performance. QualNet enables users to: • Design new protocol models. • Optimize new and existing models. • Design large wired and wireless networks using preconfigured or user-designed models. • Analyze the performance of networks and perform what-if analysis to optimize them. Model each layer’s protocol rapidly and estimate its performance accurately. The simulator runs the given simulation, the analyzer displays the results and the packet tracer allows us to follow the path of a packet through the network. QualNet is a comprehensive suite of tools for modeling large wired and wireless networks. It uses simulation and emulation to predict the behavior and performance of networks to improve their design, operation and management.

TECHNOLOGY USED

A wireless local area network (WLAN) links devices via a wireless distribution method (typically spread-spectrum or OFDM) and usually provides a connection through an access point to the wider Internet. WLAN-IEEE 802.11b standard defines the Medium Access Control (MAC) sublayer and the Physical (PHY) layer for wireless LANs. Both standards operate at 13 overlapping channels in the 2.4 GHz ISM band and the bandwidth of each channel is 22 MHz. IEEE 802.11b MAC employs the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism. Before initiating a transmission, an IEEE 802.11b node senses the channel to determine whether another node is transmitting. If the medium is sensed idle for a Distributed coordination function Inter-Frame Space (DIFS) time interval the transmission will proceed. If the medium is busy the node defers its transmission. When the medium becomes idle for a DIFS interval, the node will generate a random back-off delay uniformly chosen in an interval. This interval [0,W] is called Contention Window, where W is the size of the contention window. The initial W is set to CW min. The back-off timer is decreased by one as long as the medium is sensed idle for a back-off time slot. The back off counter will become frozen when a transmission is detected on the medium, and resumed when the channel is sensed idle again for a DIFS interval. When the back off timer reaches zero, the node transmits a data packet. Immediately after receiving a packet correctly, the destination node waits for a Short Inter Frame Spacing (SIFS) interval and then transmits an ACK back to the source node. ZigBee is an open technology developed by the SigBee Alliance to overcome the limitations of Bluetooth and Wi-Fi. ZigBee is an IEEE 802.15.4 standard for data communications with business and consumer devices. The ZigBee standard provides network, security, and application support services operating on top of the IEEE 802.15.4 Medium Access Control (MAC) and Physical Layer (PHY) wireless standard

558

National Conference on Advanced Computing and Communication Technology Zigbee-IEEE 802.15.4 standard defines the MAC sublayer and the PHY layer for low-rate wireless personal area networks. Its operational frequency band includes the 2.4 GHz ISM band. Like IEEE 802.11b/g, IEEE 802.15.4 also employs CSMA/CA for media access control. However there is a key difference between their CSMA/CA mechanisms. Unlike in IEEE 802.11b/g, a channel in IEEE 802.15.4 is not sensed during a back off period but only during a Clear Channel Assessment (CCA) period. However, the contention window remains the same size when the channel is determined busy and is doubled only when ACK is not received. This difference has a significant impact on their behavior of sharing a channel. The purpose of IEEE 802.15.4 is to provide a standard for ultra-low complexity, ultra-low cost, ultra-low power consumption, and low data rate wireless connectivity among inexpensive devices. VII SIMULATION SET UP

1

In these scenarios , heterogeneous network of 25 nodes ,consisting of WLAN & Zigbee standard is considered for different placement models i.e. Grid, Random & Uniform. The performance is analysed using different antenna heights.. Two channels are considered for different subnets and CBR application is used for data transfer for both the standards. The Two-ray path-loss model and constant lognormal shadowing model is used for analysis .Two ray path-loss model uses free space path-loss for the direct line-of-sight propagation path and the reflection from flat earth. For the reflected signal, the signal strength decays as the fourth power of the distance between the transmitter and receiver assuming that the distance is much larger than the product of antenna heights. A shadowing model is used to represent the signal attenuation caused by obstructions along the propagation path. The constant shadowing model is suitable for the scenarios without mobility where the obstructions along the propagation paths remain unchanged. The characteristics used for the purpose of analysis are throughput, average jitter & average end to end delay.

Grid placement Parameters used:

2

Random placement

3

Uniform placement VIII

SIMULATION RESULTS

(i)

Results of varying antenna heights for Grid topology Antenna Antenna Antenna Antenna height height 1.5 height 3 height 5 Parameter Throughput 553.25 553.25 553.25

559

Jitter

0.0219478

0.0222665

0.0222665

Delay

0.0742785

0.0750072

0.0750072

National Conference on Advanced Computing and Communication Technology

Throughput

Average jitter

Throughput

Average end to end delay

Average Jitter

Average end to end delay

(ii)

Results of varying antenna heights for Random topology Antenna Antenna Antenna Antenna height height 1.5 height 3 height 5 Parameter Throughput 432.75 569 569 jitter

0.0398609

0.0193066

0.0193421

delay

0.101365

0.0543837

0.0544273

Comparison of all parameters for all models

Throughput

Average Jitter

Throughput

Average Jitter

Average end to end delay Average end to end delay

IX Results of varying antenna heights for Uniform topology Antenna Antenna Antenna Antenna height height 1.5 height 3 height 5 Parameter Throughput 522 468 468.5

CONCLUSION

(iii)

Jitter

0.0957468

0.0253463

0.0241766

Delay

0.219672

0.0536602

0.0520043

In this paper, we present analysis on the performance of coexistence heterogeneous networks consisting of Zigbee & WLAN standard operating in the same ISM band. We consider three placement models or topologies i.e. Grid , Random & Uniform using Qualnet 5.0 simulation software. Performance is analysed for different placement models for varying antenna heights. The simulation results show that Grid placement model is least affected by the variation of antenna height in terms of throughput , jitter & delay. Random placement model shows better performance of the network in

560

National Conference on Advanced Computing and Communication Technology terms of increase in throughput & decrease in jitter and delay by increasing the height of system. & Uniform placement model shows better performance of the network in terms of decrease in jitter and delay ,but decrease in throughput by increasing the height of system . In future ,the analysis can be extended for different other placement models such as circular , pedestrian etc .We can also consider coexistence issue between other networks operating in same ISM band, such as Bluetooth, UWB band etc. Performance can also be analysed considering other parameters & characteristics. By using this analysis, performance level of such heterogeneous network can be improved and it can be very helpful for future research work also. REFERENCES [1] IEEE Std.802-15.4, “IEEE Standard for Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs),” 2003. [2] Jianliang Zheng and M.J. Lee, “Will IEEE 802.15.4 make ubiquitous networking a reality?: a discussion on a potential low power, low bit rate standard,” IEEE Communications Magazine, vol. 42, pp. 140– 146, 2004. [3] IEEE Std.802-11, “IEEE Standard for Wireless LAN Medium Access Control(MAC) and Physical Layer(PHY) Specificaton,” 1997. [4] A. Sikora, “Coexistence of IEEE802.15.4 (Zig- Bee) with IEEE802.11 (WLAN), Bluetooth, and Microwave Ovens in 2.4 GHz ISM-Band,” web document, http://www.ba-loerrach.de/stzedn/, 2004. [5] I. Howitt and J.A. Gutierrez, “IEEE 802.15.4 low rate - wireless personal area network coexistence issues,” IEEE Wireless Communications and Networking, vol. 3, pp. 1481 – 1486, 2003 [6] E. Callaway, P. Gorday, Lance Haster, J.A.jutierrez,Macro Naeve, B. Heile and V. Bahl, “Home networking with IEEE 802.15.4: A Developing Standard for Low-rate Wireless Personal Area Networks,” IEEE Communications, vol.40, Issue 8, 70-77, 2002 [7] J. A. Gutierrez and D. B. Durocher, “On the Use of IEEE 802.15.4 to Enable wireless Sensor networks in in Building Automation,” Pulp and Paper Industry Technical Conference, vol.3, 1865-1869, 2004

[8] 3 Yuan, W.; Wang, X.; Linnartz, J.: “A Coexistence Model of IEEE 802.15.4 and IEEE 802.11b/g”, Philips Research, Eindhoven, November 2007. [9] IEEE 802 Working Group: Standard for Part 15.4,“Wireless Medium Access Control(MAC) and Physical Layer(PHY) Specifications for Low Rate Wireless Personal Area Networks(LR-WPANs),”ANSI/IEEE 802.15.4, 2003 [10] J. Lansford, A. Stephens, and R. Nevo, “Wi-fi (802.11b) and Bluetooth : Enabling coexistence,” IEEE Network, pp. 20–27, Sept/Oct 2001. [11] Ling-Jyh Chen, Tony Sun, Mario Gerla,” Modeling Channel Conflict Probabilities between IEEE 802.15 based Wireless Personal Area Networks”, Communications, 2006. ICC apos;06. IEEE International Conference on Volume 1, Issue , June 2006 Page(s):343 – 348 [12] Soo Young Shin, Hong Seong Parky, Sunghyun Choi, Wook Hyun Kwon,” Packet Error Rate Analysis of IEEE 802.15.4 under IEEE 802.11b Interference”, IEICE Transactions on Communications 2007 [13] Dae Gil Yoon, Soo Young Shin ,Wook Hyun Kwon and Hong Seong Park ,” Packet Error Rate Analysis of IEEE 802.11b under IEEE 802.15.4 Interference” Vehicular Technology Conference, 2006. VTC 2006Spring. IEEE 63rd Publication Date: 7-10 May 2006 Volume: 3, On page(s): 1186-1190 [14]. G.M.Tamilselvan, Dr.A.Shanmugam, “Probability Analysis of Channel Collision between IEEE 802.15.4 and IEEE 802.11b using Qualnet Simulation for Various Topologies” International Journal of Computer Theory and Engineering, Vol. 1, No. 1, April 2009,On page(s):59-64 [15] Ivan Howit and Jose A. Gutierrez, “IEEE 802.15.4 Low Rate-Wireless Personal Area Network Coexistence,” Issues Wireless Communications and Networking, Vol.3, pp. 1481-1486, 2003. [16] Steibeis-Transfer Centre, “Compatibility of IEEE802.15.4 (Zigbee) with IEEE802.11 (WLAN)”. [17] BOUDHIR Anouar Abdelhakim, BOUHORMA Mohammed, BENSLIMANE Abderrahim, ERIT ,FST of Tangier Morocco, University of Avignon LIA France,”Quality of Service and Communication Technologies for Wireless Multimedia Sensor Networks”17th 17th Telecommunications forum TELFOR Serbia, Belgrade, November 24-26, 2009 [18] Charalambos D. Charalambous School of Information Technology and Eng.University of Ottawa, Ottawa, Ontario K1N 6N5,Nickie Menemenlis,Department of Electrical and Computer Eng.,McGill University, Montreal, P.Q., Canada,” Dynamical spatial Log-normal shadowing models for mobile communication”

561

A survey of security protocols in MANETs Pankaj Kapoor, Parikshit Singla and Rama Chawla Department of Computer Science & Engineering, Doon Valley Institute of Engineering and Technology, Karnal – 132001, Haryana This is generally provided by encryption. Two types of encryption are commonly used: o Symmetric Encryption, where 2 nodes share a key (e.g. - DES, AES). Any data transmitted between the nodes is encrypted using this key. This key must be provided to the nodes over a secure channel. Symmetric encryption generally requires less computational resources than public key encryption. o Public Key Encryption, where all nodes participating generate a public\private key pair puck/ proven. The node makes its public key puck available to all nodes. If other nodes wish to send data to node n, they encrypt their data using puck, safe in the knowledge that it can only be decrypted by n’s private key proven, which only node n knows.

Abstract Abstract: - Mobile ad-hoc network (MANET) is one of the recent active fields and has received marvelous attention. MANET is a collection of mobile nodes having the capability of forming temporarily network without the aid of any established infrastructure or centralized administration. Due to the self-configuration and selfmaintenance capabilities, MANETs become more vulnerable. So, Security challenges have become a primary concern to provide secure communication between mobile nodes. Many techniques have been developed to identify different type of network attacks. In this paper, we study the major attack types that MANET faces and the security goals to be achieved. This paper gives out a brief survey of major security protocols with their relative comparison. Keywords: MANET, Attacks, Security Protocols 1. INTRODUCTION

•

MANET With rapid development of wireless technology, the Mobile Ad-hoc Network (MANET) has emerged as a new type of wireless network. MANET is a collection of wireless mobile nodes (e.g. laptops) that dynamically function as a network without the use of any existing infrastructure and centralized administration. It is an autonomous system where each node operates not only as an end system but also as a router to forward packets for other nodes.

•

Since the nodes in MANET move around, the wireless links break and re-establish frequently. Furthermore, most of mobile nodes are resource limited in computing capability and battery power and therefore traditional computing content routing protocols are not suitable for MANET. Several ad-hoc security protocols have been proposed for secure communication.

•

1.1 NETWORK SECURITY GOALS •

In providing a secure networking environment some or all of the following services may be required: • Confidentiality: It ensures that the intended receivers can only access the transmitted data.

562

Integrity: Ensures that the data has not been altered during transmission. The integrity service can be provided using cryptographic hash functions along with some form of encryption. When dealing with network security the integrity service is often provided implicitly by the authentication service. Authentication: Both sender and receiver of data need to be sure of each other’s identity. Authentication can be provided using encryption along with cryptographic hash functions, digital signatures and certificates. Non-repudiation: Ensures that parties can prove the transmission or reception of information by another party, i.e. a party cannot falsely deny having received or sent certain data. Nonrepudiation requires the use of public key cryptography to provide digital signatures. A trusted third party is required to provide a digital signature, such as Thawte used on the Internet, nowadays. Availability: Ensures that the intended network security services listed above are available to the intended parties when required. The availability is typically ensured by redundancy, physical protection and other non-cryptographic means, e.g. use of robust protocols. This is a vague

metric and is provided in varying degrees by all security protocols. 1.2 SECURITY IN MANETS Mobile Ad-hoc Network (MANET) is different from the traditional wired networks due to its mobility, infrastructure less topology and the absence of central authority in the network. Any system that has to be protected might have weaknesses or vulnerabilities, some or all of which may be targeted by an attacker. Hence, one approach to designing security mechanisms for systems is to look at the threats that the system faces and the attacks possible given the vulnerabilities. The designed security mechanisms should then ensure that the system is secure in the light of these threats, attacks, and vulnerabilities. We start by providing definitions of the terms, threat, vulnerability, and attack. •

•

•

Threat is the means through which the ability or intent of an agent to adversely affect an automated system, facility or operation can be manifested. All methods or things used to exploit a weakness in a system, operation, or facility constitute threat agents. Examples of threats include hackers, industrial espionage, national intelligence services, and criminal organizations. Vulnerability is any hardware, firmware, or software flaw that leaves an information system open for potential exploitation. The exploitation can be of various types, such as gaining unauthorized access to information or disrupting critical processing. An attack is an attempt to bypass the security controls on a computer. The attack may alter, release, or deny data. The success of an attack depends on the vulnerability of the system and the effectiveness of existing countermeasures. Examples of attacks include actions such as stealing data from storage media and devices, obtaining illegitimate privileges, inserting data falsely, modifying information, analyzing network traffic, obtaining illegitimate access to systems through social engineering, or disrupting network operation using malicious software.

information that is being transmitted. Passive attacks are very difficult to detect because they do not involve any alteration of data. Examples of passive attacks are eavesdropping, traffic analysis, and traffic monitoring. 1.3.2 Active Attacks Active attacks are the attacks that are performed by the malicious nodes that bear some energy cost in order to perform the attacks. Active attacks involve some modification of data stream or creation of false stream. Active attacks with the aim of damaging other nodes by causing network outage are considered to be malicious. Examples of active attacks include jamming, modification, denial of service (DoS), and message replay. Other advanced routing attacks include spoofing, selective forwarding, sinkholes, Sybil attack, wormhole, Byzantine attack, Rushing attack, Replay attack, Location disclosure attack. 1.4 SECURITY MECHANISMS Security mechanisms for wireless ad-hoc networks should aim to provide all the security services listed below and prevent any of the attacks mentioned. However, due to the lack of infrastructure in an ad-hoc wireless network, typical wired-network implementations of the methods mentioned above may not be possible. Along with the general issues listed above, there are also other specific key issues and challenges for providing security in ad-hoc. •

Link Level Security: In wireless environment the links are susceptible to attacks where eavesdropper can intercept data packets. Physical barriers such as walls, rooms, etc. provide no barrier to wireless radio packets.

•

Routing/Network layer Security: The routing within ad-hoc networks is more vulnerable to attack as each device itself acts as a router. An attacker can pose as a member node and incorrectly route packets to achieve an attack. Denials of service attacks are particularly easy doing this. Thus implementation of secure routing protocol is one of the challenges within ad-hoc network. The use of IPSec to provide authentication, confidentiality and integrity is discussed in this report. By securing all IP traffic (or whatever network layer protocol is used), you are also securing routing.

•

Key Management: General network security implementation of keys involves a trusted authority. Given the lack of infrastructure in adhoc, it is generally not possible to have a fixed

1.3 TYPES OF ATTACKS Attacks can be divided into two main categories: 1.3.1 Passive Attacks In passive attacks the attacker does not perturb the routing protocol, instead try to extract the valuable information like node hierarchy and network topology from it. Passive attack is in nature of eavesdropping on, or monitoring of, transmission. The goal of opponent is to obtained

trusted authority; therefore an alternative to this is required. A variety of security mechanisms have been invented to counter malicious attacks. The conventional approaches such as authentication, access control, encryption, and digital signature provide a first line of defense. As a second line of defense, intrusion detection systems and cooperation enforcement mechanisms implemented in MANET can also help to defend against attacks or enforce cooperation, reducing selfish node behavior. Preventive Mechanism: The conventional authentication and encryption schemes are based on cryptography, which includes asymmetric and symmetric cryptography. Cryptographic primitives such as hash functions (message digests) can be used to enhance data integrity in transmission as well. Threshold cryptography can be used to hide data by dividing it into a number of shares. Digital signatures can be used to achieve data integrity and authentication services as well. It is also necessary to consider the physical safety of mobile devices, since the hosts are normally small devices, which are physically vulnerable. For example, a device could easily be stolen, lost, or damaged. In the battlefield they are at risk of being hijacked. The protection of the sensitive data on a physical device can be enforced by some security modules, such as tokens or a smart card that is accessible through PIN, passphrases, or biometrics. Although all of these cryptographic primitives combined can prevent most attacks in theory, in reality, due to the design, implementation, or selection of protocols and physical device restrictions, there are still a number of malicious attacks bypassing prevention mechanisms. Reactive Mechanism: An intrusion detection system is a second line of defense. There are widely used to detect misuse and anomalies. A misuse detection system attempts to define improper behavior based on the patterns of wellknown attacks, but it lacks the ability to detect any attacks that were not considered during the creation of the patterns; Anomaly detection attempts to define normal or expected behavior statistically. It collects data from legitimate user behavior over a period of time, and then statistical tests are applied to determine anomalous behavior with a high level of confidence. In practice, both approaches can be combined to be more effective against attacks. 1.5 SECURE AD-HOC ROUTING PROTOCOLS There exist several proposal that attempt to architect a secure routing protocol for mobile ad-hoc network in order to offer protection against the attacks. There are several

solutions proposed by researcher they are either completely new stand-alone protocol or in some cases incorporation of security mechanism into existing one like DSDV and AODV. Since routing is an essential function for ad-hoc networks, the integrated security procedures should not hinder its operation. Another important part of analysis is the examination of assumption and the requirements that each solution depend on. Although a protocol might be able to satisfy certain security constraints, its operational requirements might thwart its successful employment. In order to analyze exiting solution in structure way we have classified them into three categories; Solution based on Symmetric cryptography, solution based on Asymmetric cryptography and Hybrid solution. However, this classification is only indicative since a lot of solution can be classified into more than one category; 1.5.1 SYMMETRIC CRYPTOGRAPHY SOLUTIONS SEAD The Secure Efficient Ad-hoc Distance Vector (SEAD) is a secure ad-hoc network routing protocol [YC2003]based on the design of the Destination-Sequenced Distance-Vector (DSDV) [CP1994] algorithms. To developing SEAD, follow the table driven approach. In table driven routing protocol maintain at all times routing information regarding to the network connectivity of every node to all other nodes. It is also known as proactive routing protocol. In order to find shortest path between two nodes, the distance vector routing protocol utilize a distributed version of Bellman Ford Algorithm [CP1994]. The SEAD routing protocol employ the use of hash chains to authenticate hop count and sequence numbers. SEAD is prone through wormhole attack. Even if authentication is provided using hash functions, a wormhole attack is possible through tunneling the packets from one location and retransmitting them from other location into the network. All packets in the wormhole attack flow in a circle around instead of reaching the destination. Spoofing attack is possible through compromised node acting like a destination node in the route discovery process by spoofing the identity of the destination node that can cause route destruction. Blackhole attack is also possible through a compromised node advertising the shortest roots to non-existing nodes in the network. Tunneling and DOS attacks are also possible through compromised nodes. Table driven protocols are much more prone to security threats. SRP Secure Routing Protocol (SRP) [ZH2002] was developed based on Destination Source Routing protocol (DSR) [CP1994]. The operation of SRP requires the existence of a Security association (SA) between source node initiating

a route query and the destination node. The security association can be utilized in order to establish a shared secret key between the two nodes, which is used by SRP. The SRP protocol appends a header (SRP header) to the packet of the basic routing protocol. The source node sends a route request with a query sequence number (QSEQ) that is used by the destination in order to identify outdated requests, a random query identifier (QID) that is used to identify the specific request. The intermediate nodes broadcast the query to their neighbors, after updating their routing tables. Ariadne Ariadne is a secure routing protocol [YC2003] developed by Yih-Chun Hu, David B. Johnson and Adrian Perrig based on the Dynamic Source Routing protocol (DSR) [DJ1996]. Ariadne is an on-demand routing protocol, which find routes as when it required, dynamically [YL2004]. Ariadne uses MAC s and shared keys between nodes to authenticate between nodes and use time stamps for packet lifetime. It contains two phases in its routing mechanism; Route discovery and Route maintenance. In the route discovery phase the source node establishes a route by flooding route request packets (RREQ). The RREQ contains the source IP address and destination IP address. Route maintenance is carried whenever there is a broken link observed in the specific route to the destination. When the packets are forwarded through a specific route, each node sends the packet to the next node in the route and the next node acknowledges the packet received. When a broken link is observed in the destination path the broken link will not acknowledge to the packet transmitted by the neighbor node, and the node send a route error message (RERR) to the source node. 1.5.2 Asymmetric Cryptography Solutions ARAN The Authenticate routing for ad-hoc network (ARAN) [BD2002] is a secure routing protocol for MANETs, developed by Kimaya Sanzgiri, Bridget Dahilly, Brian Neil Leviney, Clay Shieldsz and Elizabeth M. BeldingRoyer based on AODV [CP1994]. ARAN utilizes cryptography mechanism in order to achieve security goals such as; authentication, message integrity, and non-repudiation in ad-hoc networks [ZH2002]. It uses asymmetric cryptography to securing routing in an ad-hoc network and require universal trusted third party (T) [RY2009]. It consist three distinct operational stages: • The first stage is the preliminary certification process that requires existence of a trusted certificate authority (CA). •

The second operational stage of the protocol is the route discovery process that provided end-to-

end authentication. This ensures that the intended destination was indeed reached. •

The third operation stage of ARAN protocol is optional and ensures that the shortest path is discovered.

ARAN uses public key cryptography and a central certification authority server for node authentication and neighbour node authentication in route discovery. It prevents spoofing attacks using a timestamp. It prevents many attacks such as replay. Denial-of-service attacks are possible with compromised nodes. Tunneling attacks are possible in ARAN. Two compromised neighbor nodes can collaborate to falsely represent the length of available paths by encapsulating and tunneling the routing message between them. Wormhole attack is also possible through two compromised nodes. Table overflow, Blackhole attacks are impossible due to node level authentication with signatures. SAR SAR was developed using a trust-based framework. Each node in the network is assigned with a trust level. So the attacks on this framework can be analyzed based on trust level and message integrity. As show below the author [Seung, Prasad, Robin] evaluated the security of SAR in terms of trust level and message integrity. Trust Level: SAR routing mechanism is based on the behavior associated with the trust level of a user. It is a binding between the identity of the user and the associated trust level. To follow the trust-based hierarchy, cryptographic techniques like: encryption, public key certificates, shared secrets, etc. are employed. Message integrity: The compromised nodes can utilize the information flow in between nodes and reading of packets to launch attacks. It results in corruption of information, confidentiality of the information, and in denial of network services. 1.5.3 HYBRID SOLUTIONS In this category we have included the secure routing protocols that employ both symmetric and asymmetric cryptographic operations. The most common approach is to digitally sign the immutable fields of routing messages in order to provide integrity and authentication, and to use hash chains to protect the hop count metric. SOADV Secure Ad-hoc On-demand Distance Vector (SAODV) is a proposal for security extensions to the AODV protocol [DJ1996, YC2003]. The proposed extensions utilize digital signatures and hash chains in order to secure AODV packets. In order to facilitate the transmission of

the information required for the security mechanisms, SAODV defines extensions to the standard AODV message format. These SAODV extensions consist of the following fields. The hash function field identifies the oneway hash function that is used. The field max hop count is a counter that specifies the maximum number of nodes a packet is allowed to go through. The top hash field is the result of the application of the hash function max hop count times to a randomly generated number, and finally the field hash is this random number. When a node transmits a route request or a route reply AODV packet it sets the max hop count field equal to the time to live

(TTL) field from the IP header, generates a random number and sets the hash field equal to it, and applies the hash function specified by the corresponding field max hop count times to the random number, storing the calculated result to the top hash field. Tunneling attacks are possible through two compromised nodes. Wormhole attacks are always possible with compromised nodes in any ad-hoc network topology. The use of sequence numbers could prevent most of the possible reply attacks.

Performance parameter

ARAN

ARIADNE

SAODV

SAR

SEAD

SRP

Type

Reactive

Reactive

Reactive

Reactive

Proactive

Reactive

Encryption Algorithm

Asymmetric

Symmetric

Asymmetric

Symmetric/ Asymmetric

Symmetric

Symmetric

MANET Protocol

AODV/DSR

DSR

AODV

AODV

DSDV

ZHLS

Synchronization

No

Yes

No

No

Yes

No

Central Trust Authority

CA Required

KDC Required

CA Required

CA/KDC Required

CA Required

CA Required

Authentication

Yes

Yes

Yes

Yes

Yes

Yes

Confidentiality

Yes

No

No

Yes

No

No

Integrity

Yes

Yes

Yes

Yes

No

Yes

Nonrepudiation

Yes

No

Yes

Yes

No

No

Antispoofing

Yes

Yes

Yes

Yes

No

Yes

DoS Attacks

No

Yes

No

No

Yes

Yes

Anrnymity

No

No

No

No

No

No

References [CP1994]

C. E. Perkins and P. Bhagwat. “Highly Dynamic Destination-Sequenced Distance-Vector routing (DSDV) for Mobile Computers,” SIGCOMM ’94: Computer Communications Review, 24(4), PP 234–244, October 1994. [DJ1996] D. Johnson and D. Maltz, “Dynamic Source Routing in Ad-hoc Wireless Network,” Mobile Computing, T. Imielinski and H. Korth, Ed. Kluwer, 1996. [ZH2002] P. Papadimitratos and Z. Haas, “Secure Routing for Mobile Ad-hoc Network,” in Proc. of CNDS 2002. (TTL) field from the IP header, generates a random

[BD2002]

B. Dahill, B. N. Levine, E. Royer, and C. Shields, “ARAN: A secure Routing Protocol for Ad-hoc Network,” UMass Tech Report PP 02-32, 2002 [YC2003] Y. C. Hu, A. Perrig, and D. B. Johnson, “Ariadne: A Secure On Demand Routing Protocol for Ad-hoc Network,” in Proceeding of 8th ACM Int’l, Conf. on Mobile Comp, Georgia, September 2003. [RY2009] Rai Tirthraj, Verma A K, “Survey and Analysis of Secure Routing Protocols for MANETs,” in the proceeding of National Conference on Cutting Edge Computer and Electronics Technology

[HD2002]

(CECT 2009), Pantnager, PP 501-06 in February 14-16,2009. H. Deng, W. Li, Agrawal, D.P., “Routing security in wireless ad-hoc networks,” Cincinnati Univ., OH, USA; IEEE Communications Magazine, Oct. 2002, Volume: 40, PP: 70- 75, ISSN: 0163-680

567

Comparative Study of Mobile Ad-hoc Routing Protocols Pankaj Kapoor, Parikshit Singla and Rama Chawla Department of Computer Science & Engineering, Doon Valley Institute of Engineering and Technology, Karnal – 132001, Haryana Abstract Abstract – A Mobile Ad-hoc NETwork (MANET) is a mobile device that comes together to form a network as needed, not necessarily with any support from the existing internet infrastructure or any other kind of fixed stations. MANETs are basically peer-to-peer, multihop networks. The highly dynamic nature of mobile ad hoc networks results in frequent changes and unpredictability in network topologies, adding difficulty and complexity to routing among the mobile nodes within the network. These added challenges, coupled with the critical importance of routing protocols in establishing communications among mobile nodes, make the routing area perhaps the most active research area within the MANET domain. A number of routing protocols have been developed in MANETs. This paper elaborates various categories of routing protocols available for mobile ad-hoc networks with their relative comparisons. Keywords: MANET, Routing, Protocol Comparison 1. Introduction Mobile ad-hoc (or spontaneous) networks (MANET) are IP networks made up of a collection of wireless and mobile nodes communicating via radio links. They do not depend on any predefined infrastructure or centralized administration to operate and could, for example, find applications in the case of networks created for the needs of participants to a conference or meeting, students and teachers in a classroom, rescuers in a search and rescue operation, soldiers on a battlefield. Ad-hoc networks can either be standalone networks (this would be the case of a spontaneous network created for the needs of a meeting between participants away from their home network -for example an airport or a hotel lounge) or peripheral networks connected, for instance, to a wired local area network or to the Internet (this would certainly be the case for a virtual classroom where students need to intercommunicate and may need to access documentation on the web or in the school’s digital library). Ad-hoc networks being spontaneous and mobile, their configuration should be done with as little user intervention as possible and the nodes should be able to rely on an adapted routing algorithm to exchange

568

information across the network. Furthermore, ad-hoc networks should offer the necessary security level for user applications. Due to the lack of an underlying infrastructure, basic functionalities, such as routing, configuration of the hosts or security management cannot rely on predefined or centralized entities to operate, and must be carried out in a distributed manner. For instance, in the case of security, the nodes cannot rely on network architecture based defense techniques such as centralized firewalls. Each node thus becomes a point of vulnerability and must assume, by itself, its own security. 1.1 MANET Characteristics A mobile ad-hoc network has following characteristics: • Autonomous Terminal: In MANET, each mobile terminal is an autonomous node, which may function as both a host and a router. In other, since there is no background network words, besides the basic processing ability as a host, the mobile nodes can also perform switching functions as a router. So usually endpoints and switches are indistinguishable in MANET. •

Distributed Operation: For the central control of the network operations, the control and management of the network is distributed among the terminals. The nodes involved in a MANET should collaborate amongst themselves and each node acts as a relay as needed, to implement functions e.g. security and routing.

•

Multihop Routing: Basic types of ad-hoc routing algorithms can be single-hop and multihop, based on different link layer attributes and routing protocols. Single-hop MANET is simpler than multihop in terms of structure and implementation, with the cost of lesser functionality and applicability. When delivering data packets from a source to its destination out of the direct wireless transmission range, the packets should be forwarded via one or more intermediate nodes

•

Dynamic Network Topology: Since the nodes are mobile, the network topology may change rapidly and unpredictably and the connectivity among the terminals may vary with time. MANET should adapt

to the traffic and propagation conditions as well as the mobility patterns of the mobile network nodes. The mobile nodes in the network dynamically establish routing among themselves as they move about, forming their own network on the fly. •

Light-weight Terminal: In most cases, the MANET nodes are mobile devices with less CPU processing capability, small memory size, and low power storage. Such devices need optimized algorithms and mechanisms that implement the computing and communicating functions.

1.2 Routing Protocols The highly dynamic nature of mobile ad-hoc networks results in frequent changes and unpredictability in network topologies, adding difficulty and complexity to routing among the mobile nodes within the network. These added challenges, coupled with the critical importance of routing protocols in establishing communications among mobile nodes, make the routing area perhaps the most active research area within the MANET domain. Especially over the last few years, numerous routing protocols and algorithms have been proposed and their performance under various network environments and traffic conditions closely studied and compared. The ultimate goal of the MANET community is to provide a set of standardized protocols that can be both robust and scalable to tens of thousands of network nodes to enable fast commercialization of mobile ad-hoc networks in increasing network applications suites. A number of routing protocols have been developed in MANETs. Depending on when the route is computed, routing protocols can be divided into two categories: proactive routing and reactive routing.

1.2.1 Proactive Routing Proactive routing is also called tabledriven routing or Precomputed routing. In this method, the routes to all destinations are computed a priori. In order to compute routes in advance, nodes need to store the entire or partial information about link states and network topology. In order to keep the information up to date, nodes need to update their information periodically or whenever the link state or network topology changes. The advantage of precomputed routing is that when a source needs to send packets to a destination, the route is already available, i.e., there is no latency. The disadvantage is that some routes may never be used. Another problem is that the dissemination of routing information will consume a lot of the scarce wireless network bandwidth when the link state and network topology change fast (this is especially true in a wireless ad-hoc network). The conventional OLSR and DVR are examples of proactive routing. 1. 2.1.1 Distance Vector Routing Distance Vector" is a term used to describe routing protocol which is used by routers to forward packets between networks. The purpose of any routing protocol is to dynamically communicate information about all network paths used to reach a destination and to select the from those paths, the best path to reach a destination network. The term distance vector is based on whether the routing protocol selects the best routing path based on a distance metric (the distance) and an interface (the vector), or selects the best routing path by calculating the state of each link in a path and finding the path that has the lowest total metric to reach the destination. 1.2.1.2 Destination-Sequenced Distance Vector (DSDV) Protocol The Destination-Sequenced Distance Vector (DSDV) routing protocol proposed by C. E. Perkins and P. Bhagwat [CP1994] is a distance vector protocol that implements a number of customizations to make its operation more suitable for ad-hoc mobile networks. DSDV utilizes per-node sequence numbers to avoid the counting to infinity problem common in many distance vector protocols. A node increment its sequence number whenever there is a change in its local neighborhood (i.e., a link addition or removal). When given a choice between two routes to a destination, a node always selects the route with the greatest destination sequence number. This ensures utilization of the most recent information. 1.2.1.3 Clusterhead Gateway Switch Routing (CGSR) The Clusterhead Gateway Switch Routing (CGSR) protocol purposed by C.-C. Chiang, H.K.Wu, W. Liu, and M. Gerla [CC1997] differs from the previous protocol in the type of addressing and network organization scheme

employed. Instead of a “flat" network, CGSR is a clustered multihop mobile wireless network with several heuristic routing schemes. The authors state that by having a cluster head controlling a group of ad-hoc nodes, a framework for code separation (among clusters), channel access, routing and bandwidth allocation can be achieved. A cluster head selection algorithm is utilized to elect a node as the cluster head using a distributed algorithm within the cluster. 1.2.1.4 Optimized Link State Routing Protocol Optimized Link State Routing (OLSR) protocol purposed by T. Clausen, P. Jacquet, A. Laouiti, P. Muhlethaler, A. Qayyum, and L. Viennot. [TC2001] is an optimization of a pure link state protocol for mobile ad-hoc networks. First, it reduces the size of control packets: instead of all links, it declares only a subset of links with its neighbors who are its multipoint relay selectors. Secondly, it minimizes flooding of this control traffic by using only the selected nodes, called multipoint-relays, to diffuse its messages in the network. Only the multipoint relays of a node retransmit its broadcast messages. This technique significantly reduces the number of retransmissions in a flooding or broadcast procedure. Protocol

Routing Structure

No. of tables

DSDV

Flat

2

WRP

Flat

4 3+1list

GSR

Flat

FSR

Flat

CGSR

Hierarchical

2

OLSR

Flat

3

HSR

Hierarchical

2

3+1list

Freq of updates Periodic and as required Periodic Periodic and local Periodic and local Periodic Periodic Periodic

significantly reduced the number of control message transmitted through the network. However, the size of update messages is relatively large, and as the size of the network grows they will get even larger. Therefore, a considerable amount of bandwidth is consumed by these update messages. 1.2.1.6 Fisheye state routing (FSR) The FSR protocol [MG2002] is the descendent of GSR. FSR reduces the size of the update messages in GSR by updating the network information for nearby nodes at a higher frequency than for the remote nodes, which lie outside the fisheye scope. This makes FSR more scalable to large networks than the protocols described so far in this section. However, scalability comes at the price of reduced accuracy. This is because as mobility increases the routes to remote destination become less accurate. This can be overcome by making the frequency at which updates are sent to remote destinations proportional to the level of mobility. 1.2.1.7 Wireless routing protocol (WRP) Wireless routing protocols (WRP) given by Shree Murthy

Critical Nodes

Memory Overhea d

Control Overhea d

Characteristic Feature

No

O(N)

O(N)

Loop free

No

O(N2)

O(N)

Loop free using predecessor info

2

No

O(N )

O(N)

Localized updates

No

O(N2)

O(N)

Controlled Frequency of updates

O(2N)

O(N)

Cluster heads exchange info

O(N2)

O(N2)

O(N2)

O(N)

Yes, cluster head No Yes, cluster head

1.2.1.5 Global state routing (GSR) The GSR protocol proposed by Tsu-Wei Chen, Mario Gerla [CH1998] is based on the traditional Link State algorithm. However, GSR has improved the way information is disseminated in Link State algorithm by restricting the update messages between intermediate nodes only. In GSR, each node maintains a link state table based on the up-to-date information received from neighboring nodes, and periodically exchanges its link state information with neighboring nodes only. This has

Reduces CO by using MPR Low CO and Hierarchical structure

and J.J. Garcia-Luna-Aceves [MY1995] is a path-finding algorithm with the exception of avoiding the count-toinfinity problem by forcing each node to perform consistency checks of predecessor information reported by all its neighbors. WRP is a loop free routing protocol. Each node maintains 4 tables: distance table, routing table, linkcost table & message retransmission list table..

The characteristics and performance comparison of the above proactive protocols is given below in the table – 1 1.2.2 Reactive Routing On-demand routing is also called reactive routing. In this method, the route to a destination may not exist in advance and it is computed only when the route is needed. The idea is as follows. When a source needs to send packets to a destination, it first finds a route or several routes to the destination. This process is called route discovery. After the route(s) are discovered, the source transmits packets along the route(s). During the transmission of packets, the route may be broken because the node(s) on the route move away or go down. The broken route needs to be rebuilt. The process of detecting route breakage and rebuilding the route is called route maintenance. The major advantage of on-demand routing is that the precious bandwidth of wireless ad-hoc networks is greatly saved because it limits the amount of bandwidth consumed in the exchange of routing information by maintaining routes to only those destinations to which the routers need to forward data traffic. On-demand routing also obviates the need for disseminating routing information periodically, or flooding such information whenever a link state changes. The primary problem with on-demand routing is the large latency at the beginning of the transmission caused by route discovery. 1.2.2.1 Ad-hoc On-Demand Distance Vector Routing Protocol C. E. Perkins and E. M. Royer purposed the Ad-hoc OnDemand Distance Vector (AODV) [CE1999] algorithm which enables dynamic, self-starting, multihop routing between participating mobile nodes wishing to establish and maintain an ad-hoc network. AODV allows mobile nodes to obtain routes quickly for new destinations, and does not require nodes to maintain routes to destinations that are not in active communication. AODV allows mobile nodes to respond to link breakages and changes in network topology in a timely manner. The operation of AODV is loop-free, and by avoiding the Bellman-Ford ‘‘counting to infinity’’ problem offers quick convergence when the ad-hoc network topology changes (typically, when a node moves in the network). When links break, AODV causes the affected set of nodes to be notified so that they are able to invalidate the routes using the broken link. 1.2.2.2 Dynamic Source Routing Protocol purposed by D. B. Johnson and D. A. Maltz [DB1996] describes the design and performance of a routing protocol for ad-hoc networks that instead uses dynamic source routing of packets between hosts that want to communicate. Source routing is a routing

technique in which the sender of a packet determines the complete sequence of nodes through which to forward the packet; the sender explicitly lists this route in the packet’s header, identifying each forwarding “hop” by the address of the next node to which to transmit the packet on its way to the destination host. Source routing has been used in a number of contexts for routing in wired networks, using either statically defined or dynamically constructed source routes and has been used with statically configured routes in the Tucson Amateur Packet Radio (TAPR) work for routing in a wireless network. The protocol presented here is explicitly designed for use in the wireless environment of an ad-hoc network. There are no periodic router advertisements in the protocol. Instead, when a host needs a route to another host, it dynamically determines one based on cached information and on the results of a route discovery protocol. 1.2.2.3 Light-weight mobile routing (LMR) The Light-weight mobile routing (LMR) purposed by M. Scott Corson [CS1995].LMR protocol is another ondemand routing protocol, which uses a flooding technique to determine its routes. The nodes in LMR maintain multiple routes to each required destination. This increases the reliability of the protocol by allowing nodes to select the next available route to a particular destination without initiating a route discovery procedure. Another advantage of this protocol is that each node only maintains routing information to their neighbors. This means avoids extra delays and storage overheads associated with maintaining complete routes. However, LMR may produce temporary invalid routes, which introduces extra delays in determining a correct loop. 1.2.2.4 Temporally-Ordered Routing Algorithm (TORA) M. S. Corson and A. Ephremides present a new distributed routing protocol [MS1995] for mobile, multihop, wireless networks. The protocol is one of a family of protocols which we term "link reversal" algorithms. The protocol’s reaction is structured as a temporally-ordered sequence of diffusing computations; each computation consisting of a sequence of directed link reversals. The protocol is highly adaptive, efficient and scalable; being best-suited for use in large, dense, mobile networks. In these networks, the protocol’s reaction to link failures typically involves only a localized "single pass" of the distributed algorithm. This capability is unique among protocols which are stable in the face of network partitions, and results in the protocol’s high degree of adaptivity. This desirable behavior is achieved through the novel use of a "physical or logical clock" to establish the "temporal order" of topological change events which is used to structure (or order) the algorithm’s reaction to topological changes.

1.2.2.5 Associativity-Based Routing (ABR) C-K. Toh present a totally different approach in mobile routing is proposed in [CK1996]. The Associativity-Based Routing (ABR) protocol is free from loops, deadlock, and packet duplicates, and defines a new routing metric for adhoc mobile networks. This metric is known as the degree of association stability. In ABR, a route is selected based on the degree of association stability of mobile nodes.

Protocol

Routing Structure

Multiple Routes

Beacons

strength of the beacons is measured for determining the link stability between the nodes. With the help of the link stability and the location stability the links are classified as stable or unstable. The characteristics and performance comparison of the above reactive protocols is given below in the table – 2

Route maintained

Communicati on Complexity

Advantage

Disadvantage

Yes, hello message

Routing Table

O(2N)

Adaptablity

large delays, hello message

AODV

Flat

DSR

Flat

Yes

No

Route Cache

O(2N)

LMR

Flat

Yes

No

Routing Table

O(2N)

TORA

Flat

Yes

No

Routing Table

O(2N)

SSA

Flat

Yes

Routing Table

O(N+R)

ABR

Flat

Yes

Routing Table

O(N+R)

No

No No

Each node periodically generates a beacon to signify its existence. When received by neighboring nodes, these beaconing causes their associativity tables to be updated. For each beacon received, the associativity tick of the current node with respect to the beaconing node is incremented. Association stability is defined by connection stability of one node with respect to another node over time and space. A high degree of association stability may indicate a low state of node mobility, while a low degree may indicate a high state of node mobility. Associativity ticks are reset when the neighbors of a node or the node itself moves out of proximity. A fundamental objective of ABR is to derive longer-lived routes for adhoc mobile networks. 1.2.2.6 Signal Protocol

Stability-based

Adaptive

Routing

Signal Stability Based Adaptive Routing Protocol (SSA) sugested by Rohit Dube, Cynthia D. Rais, Kuang-Yeh Wang, Satish K. Tripathi [RD1996]. SSA tries to discover stronger routes based on signal strength and location stability of the nodes. The location stability defines paths which have existed for a longer period of time. SSA is beacon-based like ABR which means, that the signal

Multiple routes Multiple routes Multiple Route stability Route stability

flooding, large delays Temporary routing loops Temporary routing loops Scalability problems route failure and reconstruction

1.3 Hybrid routing protocols Hybrid routing protocols are a new generation of protocol, which are both proactive and reactive in nature. These protocols are designed to increase scalability by allowing nodes with close proximity to work together to form some sort of a backbone to reduce the route discovery overheads. This is mostly achieved by proactively maintaining routes to near by nodes and determining routes to far away nodes using a route discovery strategy. Most hybrid protocols proposed to date are zone-based, which means that the network is partitioned or seen as a number of zones by each node. Others group nodes into trees or clusters. This section describes a number of different hybrid routing protocol proposed for MANETs. 1.3.1 Zone routing protocol (ZRP) In ZRP purposed by Zygmunt J. Haas, Marc R. Pearlman, Prince Samar [HS1999] , the nodes have a routing zone, which defines a range (in hops) that each node is required to maintain network connectivity proactively. Therefore, for nodes within the routing zone, routes are immediately available. For nodes that lie outside the routing zone, routes are determined on-demand (i.e. reactively), and it can use any on-demand routing protocol to determine a

route to the required destination. The advantage of this protocol is that it has significantly reduced the amount of communication overhead when compared to pure proactive protocols. It also has reduced the delays associated with pure reactive protocols such as DSR, by allowing routes to be discovered faster. This is because, to determine a route to a node outside the routing zone, the routing only has to travel to a node which lies on the boundaries (edge of the routing zone) of the required destination. Since the boundary node would proactively maintain routes to the destination (i.e. the boundary nodes can complete the route from the source to the destination by sending a reply back to the source with the required routing address).

References [CP1994]

[MS1995]

[MY1995] 1.3.2 Zone-based hierarchical link state (ZHLS) Unlike ZRP, ZHLS routing protocol suggested by Mario Joa-Ng [JN1999] employs hierarchical structure. In ZHLS, the network is divided into non-overlapping zones, and each node has a node ID and a zone ID, which is calculated using a GPS. The hierarchical topology is made up of two levels: node level topology and zone level topology, as described previously. In ZHLS location management has been simplified. This is because no cluster-head or location manager is used to coordinate the data transmission. This means there is no processing overhead associated with cluster-head or Location Manager selection when compared to HSR, MMWN and CGSR protocol s.

[DB1996]

[CC1997]

The characteristics and performance comparison of the hybrid proactive protocols. [ZH1999] Prot ocol

Rout ing Stru cture

ZR P

Flat

ZH LS

Hier archi cal

Mult iple Rout es

No

Yes

Beac ons

Time Comp lexity

Yes

Intra: O(I) Inter: O(2D)

No

Intra: O(I) Inter: O(D)

Advan tage

Reduc e trans missio n Reduc tion of single point of failure , low CO

Disadva ntage

[CP1999]

Overlap ping Zones

[TC2001]

Static zone map required

[MG2002]

573

C. E. Perkins and P. Bhagwat. “Highly Dynamic Destination-Sequenced Distance-Vector routing (DSDV) for Mobile Computers,” SIGCOMM ’94: Computer Communications Review, 24(4), PP 234–244, October 1994. M. S. Corson and A. Ephremides, “A Distributed Routing Algorithm for Mobile Wireless Networks," ACM/Baltzer Wireless Networks Jouornal, Vol. 1, No. 1, PP. 61-81, February 1995. S. Murthy J.J. Garcia-Luna-Aceves, “A routing protocol for packet radio networks”, in: Proceedings of the First Annual ACM International Conference on Mobile Computing and Networking, Berkeley, CA, 1995, PP. 86–95, in 1995. D. B. Johnson and D. A. Maltz, “Dynamic Source Routing in Ad-Hoc Wireless Networks," Mobile Computing, ed. T. Imielinski and H. Korth, Kluwer Academic Publishers, PP. 153-181, 1996. C.-C. Chiang, H.K.Wu, W. Liu, and M. Gerla, “Routing in Clustered Multihop, Mobile Wireless Networks with Fading Channel," Proceedings of IEEE SICON'97, PP. 197-211, April 1997. L. Zhou, Z.J. Haas, Cornell Univ., “Securing ad-hoc networks,” IEEE Network, Nov/Dec 1999, Volume: 13, PP: 24-30, ISSN: 0890-8044 in 1999. C. E. Perkins and E. M. Royer, “Ad-hoc On-Demand Distance Vector Routing," Proceedings of 2nd IEEE Workshop on Mobile Computing Systems and Applications, February 1999. T. Clausen, P. Jacquet, A. Laouiti, P. Muhlethaler, A. Qayyum, and L. Viennot. ‘‘Optimized Link State Routing Protocol,’’ in Proceedings of IEEE INMIC, Lahore, Pakistan, December 2001. M. Gerla, “Fisheye state routing protocol (FSR) for ad-hoc networks”, Internet Draft, draft-ietf-manet-aodv03.txt, work in progress, 2002.

ARAN –Authenticated Routing Protocol AND ITS ADVANTAGES PREETI NAGRATH Lecturer Bharti Vidya Peeth College of Engg.,Delhi environment[2]. It aims to increase security , achieve robustness, introduces authentication, message integrity and non repudiation to an adhoc environment as a part of a minimal security policy[1] and solve single point of failure and attack problems by introducing certificate authority servers whose public key is known to all legitimate nodes. ARAN makes use of cryptographic certificates to offer routing security and to accomplish its task with authenticity.Three distinct stages of ARAN consist of preliminary certification process followed by mandatory authenticated route discovery or route instatiation process which guarantees end to end authentication , route maintenance phase and an optional stage that provides secure shortest paths [3]. ARAN protocol is based on Adhoc On Demand Distance Vector Routing so as to take benefit of high performance and low cost .

ABSTRACT Wireless mobile adhoc network (MANET) have become very popular and indivisible part for communication due to its advantages like mobility and convenience. Wireless networks have swapped the wired networks but to achieve security in them is challenging due to dynamic topologies and vulnerable wireless links. Many routing protocols have been proposed for adhoc networks like AODV, DSR .These protocols are capable of handling large number of hosts with limited resources such as bandwidth and energy but these have no security considerations.Routing protocol ARAN provides authentication and security over other routing protocols . This paper discusses ADVANTAGES of ARAN over other routing protocols like AODV and also discusses some solutions to one major threat to Adhoc Networks :WORMHOLES.

KEYWORDS:AODV,ARAN,REQ,REP,RDP,W ormhole.

Solutions to Vunerabilities networks provided by ARAN

in

Ad-hoc

1.Unauthorized participation:Authorization is a process in which an entity is issued a credential, which specifies the privileges and permissions it has and can not be falsified by the certificate authority. Authencity assures that participants in communication are genuine and impersonators. ARAN requires use of trusted certificate server before entering in adhoc network, each node has to request a certificate signed by the server.All nodes are supposed to maintain fresh certificates with the trusted server and must know server‟s public key, so ARAN participants will only accept the packets that have been signed by a certified key which is issued by trusted authority server. This trusted authority is also single point of failure and attack. So many authorities can be used and Area can be divided into zones (given by Zhou and Haas )

INTRODUCTION An Adhoc Network consist of a number of mobile nodes in which there is no predefined infrastructure where all nodes work in forwarding the packets received by them. So it is very important to have efficient routing protocol as all nodes act as router. Designing a good protocol that satisfies all requirements of routing is not an easy task .As adhoc networks has the following features: 1. Constantly changing topology 2. Unreliability of wireless links between nodes. 3. Limited power supply of wireless nodes. 4. Lack of incorporation of security features Due to which these networks suffer from malacious behavior like modification, impersonation and fabrication [1]that make the mobile adhoc networks vunerable and insecure .

2.Attacks using modification or maintaining integrity:Integrity guarantees the identity of messages when they are transmitted [5].ARAN provides integrity to adhoc network. As we know that ARAN specifies all fields of RDP and REP packets must remain unchanged between source and destination [3]. AS both packet type are signed by the initiating node, any changes can be detected and the altered packet

ARAN:Dahill, Hevine, Royee and Shields proposed ARAN(Authenticated routing for ad-hoc networks) , a Secure routing protocol that detects and protects against malicious actions by third parties and peers in one particular adhoc

574

will be discarded. Modification attacks can thus be prevented.

have ever sent or received such a message then a node

3.Attacks using impersonation:This attack is very severe threat to the security of mobile adhoc networks if there is no such mechanisms which is authentic, then many compromising nodes can join the networks as normal nodes and becomes malicious nodes to do some inappropriate fake routing to access some confidential information. In ARAN the source node can sign with its own private key, nodes cannot spoof others nodes in route information. Similarly Reply packets include the destination node certificate and signature which ensures that only destination nodes can respond to the route discovery. When the source receives the REP, it checks the destination signature as the destination can answer the RDP packets, so ARAN removes this possible exploit and cuts down on reply traffic received by the source by disabling this option. This prevents impersonations attack where either the source and the destination nodes is spoofed .

Well known fabrication attacks like Blackhole attacks , Grayhole attacks and wormhole attacks[6] cannot be resolved by ARAN. Wormhole attack is quite severe threat which is very challenging to detect and prevent. It consist of recording traffic from one region of the network and tunneling it in the different region.It is carried out by intruder or malicious nodes located with in the range of legitimate nodes A and B,where A & B are not themselves within the transmission range of each other.A dangerous threat can be perpetrated of a wormhole attacker tunnels all packets through the wormhole honestly and reliably since no harmseems to be done,moreover the attacker seems to provide a useful service in connecting the network more efficiently.However communication may be badly damaged when attacker routes only control messages and no data packets.

3. Securing shortest path:The second stage of ARAN protocol guarantees in a secure way that the path received by a source initiating a route discovery process is the shortest. Similarly in the first stage of the protocol ,the source broadcasts a shortest path confirmation(SPC) message to its neighbour,SPC message is different from RDP message in two additional fields that provide destination certificate and encryption of data prevents nodes in the middlefrom changing that path length as doing so would cause loosing the integrity of SPC packet[2].

4.Attacks using fabrication:The route maintaince phase of ARAN protocol is secured by digitally signing the route error packets so ARAN ensures nonrepudiation and unauthorized participation in routing. It doesn‟t prevent fabrication but it does offer a deterrent by ensuring nonrepudiation. Nonrepudiation ensures that the sender and receiver of a message can‟t disagree that they

Hu, Perrig &Johnson proposed an approach based on packet leaches mechanism[10] to detect a wormhole.This can be done by authenticating either the timestamp or the location information ,a receiver can determine if the packet has traversed a distance which is very unrealistic for the specific technology used.These are of two typesTemporal leaches and Geogrophic leaches.Temporal leaches are based on time synchronization & timestamps whereas Geogrophical leaches are based on location info &loosly synchronized clocks.If the clocks of the sender and receiver are synchronized within a certain threshold and the velocity of any node is bounded,the receiver can compute on upper bound on the distance between the sender & itself and use it to detect abnormalities in traffic flow. „Time of flight‟ is also a technique used to prevent wormholes by calculating the roundtrip journey time of a message.Here acknowledgment estimate between the nodes based on this time&calculate whether the distance is within the maximum possible range .If wormhole attack is ther ,then packets end up travelling further.

„Directional Antennas‟ are also solution for wormhole detection.In this ,each pair of nodes determines direction of received signalfrom the neighbor if the direction of both matches only then the relation is set[26]. CONCLUSION:This paper presented how ARAN proves to be solution to many routing problems and attacks as it protects against exploits using modification,fabrication and impersonization .It is a secure protocol which provides Integrity, Availability, Confidentiality, Authenticity, Nonrepudiation, Authorization & Anonymity.ARAN is a simple protocol using simple predetermined cryptographic certificates which guarentees end-to-end authentication ,it doesnot requires additional work from nodes with in the group.It is based on Ah hoc on demand distance vector routing which guarantees high performance and low cost due to its on reactive nature,it is as efficient as AODV in discovering and maintaining routes.Help from some existing infrastructure can be taken for obtaining certificates from trusted third party.But ARAN does not scale well in large networks since any request packet is flooded to the entire network.For this problem ARANz[4] can be used .Also wormhole problem is also there in ARAN ,many solutions are proposed for this. REFERENCES [1] K. Sanzgiri, B. Dahill, B.N. Levine, C. Shields, E.M. Belding-Royer, A secure routing protocol for ad hoc networks, in: Proceedings of 2002 IEEE International Conference on Network Protocols (ICNP), November 2002 [2]Security in adhoc networks .REFIK Molva and Pietro Michiardi [3]Seema Mehla et. al. / (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 03, 2010, 664-668 [4]Liana Khamis Qabajeh,Dr.Miss Laiha Mat Kiah,Mohammad Moustafa Qabajeh A Scalable secure routing protocol for Manet,2009 international Conference on Computer Technology and Development. [5]Wenjia liand Anupam joshi Security issues in mobile Ad-Hoc Networks.

[6]JCSNS International Journal of Computer Science and Network Security, VOL.10 No.4, April 2010,Jhaveri et al [7] Y.-C. Hu, A. Perrig, D.B. Johnson, Wormhole detection in wireless ad hoc networks, A Technical Report TR01-384, Rice University Department of Computer Science. [8] K. Sanzgiri et al., “A Secure Routing Protocol for Ad hoc Networks,” Proc. 10th IEEE Int’l. Conf. Network Protocols (ICNP’02), IEEE Press, 2002, pp. 78–87. [9] S. Yi, P. Naldurg, and R. Kravets, “SecurityAware Ad hoc Routing for Wireless Networks,” Proc. 2nd ACM Symp. Mobile Ad Hoc Net. and Comp. (Mobihoc’01), Long Beach, CA, Oct. 2001, pp. 299–302. [10] E. M. Royer and C.-K. Toh, “A Review of Current Routing Protocols for Ad hoc Mobile Wireless Networks,” IEEE Pers. Commun., vol. 2, no. 6, Apr. 1999, pp. 46–55. [11] Dirk Balfanz, D. K. Smetters, Paul Stewart, and H. Chi Wong. Talking To Strangers: Authentication in Ad-Hoc Wireless Networks. In Symposium on Network and Distributed Systems Security (NDSS 2002), February 2002. [12] Yih-Chun Hu, Adrian Perrig, and David B. Johnson. Ariadne: A Secure On-Demand Routing Protocol for Ad Hoc Networks. In Proceedings of the Eighth Annual International Conference on Mobile Computing and Networking (MobiCom 2002), pages 12–23, September 2002. [13] Jean-Pierre Hubaux, Levente Butty´an, and Srdjan Cˇ apkun. The Quest for Security in Mobile Ad Hoc Networks. In Proceedings of the Third ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2001), Long Beach, CA, USA, October 2001 [14] Stefano Basagni, Kris Herrin, Emilia Rosti, and Danilo Bruschi. Secure Pebblenets. In ACM International Symposium on Mobile Ad

Hoc Networking andComputing (MobiHoc 2001), pages 156–163, Long Beach, California, USA, October 2001. [15] Sergio Marti, T.J. Giuli, Kevin Lai, and Mary Baker. Mitigating Routing Misbehaviour in Mobile Ad Hoc Networks. In Proceedings of the Sixth Annual InternationalConference on Mobile Computing and Networking (MobiCom 2000), pages 255– 265, Boston MA, USA, August 2000. [16] Frank Stajano and Ross Anderson. The Resurrecting Duckling: Security Issues for Ad-hoc Wireless Networks. In Security Protocols, 7th International Workshop, edited by B. Christianson, B. Crispo, and M. Roe. Springer Verlag Berlin Heidelberg, 1999. [17] Seung Yi, Prasad Naldurg, and Robin Kravets. Security-Aware AdHoc Routing for Wireless Networks. Technical Report UIUCDCS-R2001-2241, Department of Computer Science, University of Illinois at Urbana-Champaign, August 2001. [18] Yih-Chun Hu, David B. Johnson, and Adrian Perrig. Secure Efficient Distance Vector Routing in MobileWireless Ad Hoc Networks. In Fourth IEEEWorkshop on Mobile Computing Systems and Applications (WMCSA ’02), June 2002. [19] Steven Cheung and Karl Levitt. Protecting Routing Infrastructures from Denial of Service Using Cooperative Intrusion Detection. In The 1997 New SecurityParadigms Workshop, September 1998. [20] Kirk A. Bradley, Steven Cheung, Nick Puketza, Biswanath Mukherjee, and Ronald A. Olsson. Detecting Disruptive Routers: A Distributed Network Monitoring Approach. In Proceedings of the IEEE Symposium on Research in Security and Privacy, pages 115– 124, May 1998 ISSN

Black-Hole-and-Cooperative-Black-HoleAttacks-in-Wireless-Ad-hoc-Networks [22] Study of Secure Reactive Routing Protocols in Mobile Ad http://www.docstoc.com/docs/30136052/Studyof-Secure-Reactive-Routing-Protocols-inMobile-Ad [23] Charles E. Perkins and Elizabeth M. Royer, “Ad-hoc On-Demand Distance Vector Routing” [24] Luke Klein-Berndt, “A Quick Guide to AODV Routing” [25] C. Perkins, E. Belding-Royer and S. Das, “Ad hoc On-Demand Distance Vector (AODV) Routing”, 2003 [26] Khin Sandar Win,” Analysis of Detecting Wormhole Attack in Wireless Networks”, World Academy of Science, Engineering and Technology 48, pp. 422-428, 2008 Rutvij H. Jhaveri Ashish D. Patel. Jatin D. Parmar Bhavin I. Shah

[21] Avoiding Black Hole and Cooperative Black Hole Attacks in Wireless Ad hoc Networks http://www.scribd.com/doc/26788447/Avoiding-

577

National Conference on Advanced Computing and Communication Technology

A Performance Evaluation Study on WiMAX Using Glomosim Surender singh1, Prof. Dharminder Kumar2 1

M.Tech (CSE), 2 Professor Department of Computer Science & Engineering Guru Jambheshwar University of Science & Technology, Hisar (Haryana) - India 1

[email protected],

Abstract--WiMax or the 802.16 standard is the acronym for the World Interoperability for Microwave Access. It is more flexible and much cheaper than other possible solutions of MAN. IEEE802.16 designed to provide broadband wireless access for mobile users. WiMax can support wider area coverage than UWB and the higher speed access than 3G. WiMax has nature technological advantage than others in the market usage of 5Mbps to 100Mbps accessing speed and 100m-30km coverage distance. Therefore, WiMax is an efficient and economical technology to achieve broadband Internet access. In this paper we have designed various scenarios using the Glomosim simulator study the performance of WiMax in delivering traffic under different operational conditions for a point to multi point configuration. The results showed how different factors such as load and mobility might affect the performance of WiMax in a single cell environment, end to end delay, delay jitter and throughput were considered as the performance measures in this study. Keywords- IEEE 802.16, WiMax, Glomosim

I.

INTRODUCTION

Wi-Max is considered the evolution of the wireless broadband. The technology is similar to WiFi but with the capability to sending and receiving large amount of packed data through the use of base stations, similar to those used in the cellular networks. [17] Wi-Max is a new technology that has just been introduced in 2002. The initial implementation of WiMax, IEEE802.16 was intended for the 10-66 GHz licensed band. Later modifications of the standards, IEEE802.16a and d made it possible to deploy WiMax in the licensed and unlicensed frequency bands in the range of 2-11 GHz.Wi-Max was born to resolve the weaknesses of Wi-Fi network such as low range, inadequate bandwidth and encryption[4]. Although this technology is still young, it is expected that the subscribers will grow from 80,000 by the end of 2005 to 3.8 million by the end of 2009. A typical IEEE 802.16 network is made up of one central base station (BS) that communicates with one or more Subscriber Stations (SS). This communication can take place in several different network architectures to include point-to-point (PTP) connections between two nodes, point-to-multipoint

2

[email protected]

(PMP) a connection between one BS and multiple SS nodes Point-to-consecutive point (PTCM) Involves the creation of a closed loop through multiple PTP connection. It also supports mesh IEEE 802.16a substandard, [6] where each node is able to route data adaptively to its destination. It performs about similar to some extend like Wi-Fi but at higher speed at great distance and for greater number of users. It can provide data rate up to 70Mbps from larger distances which it can be reached up 30 miles so as named WMAN. It is also refer as Wireless broadband access which can transfer not only data but voice data, video data etc and at much higher rates [16]. To enable mobility the IEEE 802.16 working group came up with the IEEE802.16e standards to support mobility. The aims to apply high data rates, quality of services long range and low deployment costs to a wireless access technology on a metropolitan scale. IEEE 802.16 technology supports various applications like voice communications, video communications, data services and backhaul services. [5] WiMax provides fixed, nomadic, portable, and soon, mobile wireless broadband connectivity without the need for a direct line-of-sight with a base station. II. SIMULATION TOOL Our performance study of WiMax, we have used the MAC802.16 model of Glomosim 2.03, which has implemented features defined in IEEE 802.16 [1]. GloMoSim provides a scalable simulation environment for large wireless and wired communication networks. It uses a parallel discrete-event simulation capability provided by Parsec. Parsec is a C-based simulation language. It uses sequential and parallel execution of discrete-event simulation models. For all simulation scenarios a single circular WiMax cell was used with omnidirectional antenna model at the alleviation of 15 meters above the ground with a transmission power of 25 dbm. The simulation time was for one hour. We will analyze the effect of Changing Mobility on the performance of both these technologies. [17]

578

National Conference on Advanced Computing and Communication Technology

III. SIMULATION SCENARIOS AND RESULTS

Receiver Collision Ratio Collision

The performance study of WiMax, we have developed several simulation scenarios using Glomosim. These scenarios were designed to target WiMax performance under specific conditions such as load, traffic type, mobility and coverage. The packet load, delay jitters for all scenarios, where CBR traffic was used. These are discussed below. [5] TABLE 1

5 4 3 2 1 0

Wi M ax

10 15 15-20 20-25 25-30 Mobility

SIMULATION PARAMETERS Parameter

WiMax

Description

FIG. I RECEIVER SIDE COLLISION RATIO

Simulation time

10M

Maximum execution time

TABLE 3

No. of Nodes

20

Nodes particip. the network

SENDER SIDE COLLISION RATIO

Traffic Model

CBR

Constant Bit Rate link used

Node Placement

Uniform

Node placement policy

Mobility

5-30(m/s)

Speed of node are moving

Sender Collision Ratio

MAC-Protocol

802.16

MAC layer protocol used

Routing Protocol

Bellmanf.

Routing protocol used

Mac Prop. delay

NA

Propagation delay

Bandwidth

12000000

Bandwidth used

Mobility

WiMax

10-15

3

15-20

3

20-25

1

25-30

1

Sender Collision Ratio

Collision

SCENARIO 1: COLLISION RATIO Packet collision occurs when two or more packets from different source nodes arrive at the same destination node simultaneously. The simulation measures the number of total packets (total_pkt) arriving at a specific node and calculates how many packets encounter collision (collided_pkt); [17] the packet collision ratio is the ratio of collided_pkt to total_pkt. Fig.1 sows the packet loss rates under various mobility schemes. It is clear from the graph that the less collisions occur in case of WiMax.

3.5 3 2.5 2 1.5 1 0.5 0

Wi Ma x

10 15

15-20

20-25

25-30

Mobility

TABLE 2 FIG. II SENDER SIDE COLLISION RATIO

RECEIVER SIDE COLLISION RATIO

SCENARIO 2: PACKET DELIVERY RATIO

Receiver Collision Ratio Mobility

WiMax

10-15

4

15-20

4

20-25

3

25-30

3

The ratio between the number of packets that are received and the number of packets sent. This metric only considers backward path traffic, i.e., the data packets from the gateways to the mesh nodes. [6] PDR is most important metric that we should consider in packet forwarding. It may affect by different criteria such as packet size, group size, action range and mobility of nodes.

579

National Conference on Advanced Computing and Communication Technology

TABLE 4

End To End Delay

PACKET DELIVERY RATIO

3963.5

Mobility

WiMax

5-10

0.00527

10-15

0.00533

15-20

0.00533

20-25

0.00533

25-30

0.00533

Collision

Packet Delivery Ratio

3962.5

Receive d by Wimax

3962 3961.5 5 10 15- 20- 2510 15 20 25 30 Mobility

FIG. IV AVERAGE END TO END DELAY

Packet Delivery Ratio

Collision

Total Packet sent

3963

IV. CONCLUSIONS

0.00534 0.00532 0.0053 0.00528 0.00526 0.00524 0.00522

Wi ma x

5 10 15- 20- 2510 15 20 25 30 Mobility

FIG. III PACKET DELIVERY RATIO SCENARIO 3: AVERAGE END TO END DELAY The delay is the total latency experienced by a packet to traverse the network from the source to the destination. At the network layer, the end-to-end packet latency is the sum of processing delay, packetization, transmission delay, queuing delay, and propagation delay. The end-to-end delay of a path is the summation of the node delay at each node plus the link delay at each link on the path. Higher mobility causes more links broken and frequent re-routing and thus causes larger end-to-end delay.

In this paper several scenarios were simulated using the simulation tool Glomosim for WiMax. The results obtained from these scenarios showed how the several performance measures such as delay, PDR and throughput can vary due to changing certain factors such as load, mobility and position of SSs. For example, the simulation results showed that the maximum throughput obtained within a WiMax. For our future work, we intend to look at PDR which is most important metric that we should consider in packet forwarding. It may affects by different criteria such as packet size, group size, action range and mobility of nodes. The end-to-end delay of a path is the summation of the node delay at each node plus the link delay at each link on the path. When two or more packets from different source nodes arrive at the same destination node simultaneously than collision occurs.

TABLE 5

References [1]

[2]

[3]

AVERAGE END TO END DELAY Average End To End Delay Mobility

Total Pack. Sent

Rec. By WiMax

5-10

3963

3962

10 -15

3963

3962

15-20

3963

3962

20-25

3963

3962

25-30

3963

3962

[4]

[5]

[6]

[7]

580

Bshara M. and Biesen L. V. “Localization in wireless networks depending on map-supported path loss model a case study on WiMAX networks”. Proc. in IEEE ISBN: 978-1-4244-5626 pp. 1-5 (2009) Bshara M. and Gustafsson F. “Signal Strength Measurements a Case Study on Wi-Max Networks”. Proc. In IEEE Transactions on Vehicular Technology, Vol.59: ISSN: 0018-9545 pp. 283-294 (2010). Chou C. M., Li C. Y., Chien W. M. and Lan K. c. “A Feasibility Study on Vehicle-to-Infrastructure Communication WiFi vs. WiMAX”. Proc. in IEEE DOI 10.1109/MDM.2009.127 0-7695-3650 pp. 397-978 (2009) Dhawan S. “Analogy of Promising Wireless Technologies on Different Frequencies Bluetooth, WiFi, and Wi-Max”. Proc. in Aus Wireless, Vol.2: ISBN: 0-7695-2842 pp. 14 (2007). Etemad K. “Overview of Mobile Wi-Max Technology and Evolution”. Proc. in IEEE, Vol.46: ISSN: 01636804 pp. 31-40 (2008). Ghazisaidi N., Kassaei H. and Bohlooli M. S. “Integration of Wi-Fi and Wi-Max Mesh Networks”. Proc. in IEEE, Vol.5: ISBN: 978-0-7695-3667 pp. 1-6 (2008). Imran A. and Tafazolli R. “Performance & Capacity of Mobile Broadband Wi-Max 802.16e) Deployed via High

National Conference on Advanced Computing and Communication Technology

[8]

[9]

[10]

[11]

[12]

Altitude Platform”. Proc. in European Wireless IEEE pp. 319-323 (2009) Ming C. C., Yuan L. C., Chien W., M. and chan K. “A Feasibility Study on Vehicle-toInfrastructure Communication Wi-Fi vs. Wi-Max”. Proc. in IEEE, Vol.1: ISBN: 978-0-7695-3650-7 pp. 397-398 (2009). Palanisamy P. and Sreedhar T.V.S. “Performance Analysis of Raptor Codes in Wi-Max Systems over Fading Channel”. Proc. in Symposium on Foundations of Computer Science, Vol.06: ISBN: 978-1-4244-24085 pp.1-5 (2002). Petajasoja S., Takanen A., Varpiola M. and Kortti H. “Case Studies from Fuzzing Bluetooth, Wi-Fi and WiMax”. Proc. in Securing Electronic Business Processes Vieweg, Vol.2: ISBN: 978-3-8348-0346-7 pp.188-195 (2007). Ryusuke K., Fukao C., Kazuyuki J. H., Masahiro F. Makoto K. “A Study on the Detection Scheme of WiMAX signal for DAA Operation in MB-OFDM”. Proc. in IEEE, Vol.3: 1-4244-0521 pp. 834-839 (2007) Shuaib K. A. “A Performance Evaluation Study of WiMAX Using Qualnet”. Proc. in WCE Vol I ISBN: 978-988-17012 pp. 5-1 (2009)

581

[13]

[14]

[15]

[16]

[17]

[18]

Sim S. and Han S. J. “Seamless IP Mobility Support for Flat Architecture Mobile Wi-Max Networks”. Proc. in Choon Lee Communications Magazine, Vol.47: ISSN: 0163-6804 pp. 142-148 (2009). Talwalkar R. A. and Ilyas M. “Analysis of Quality of Service in Wi-Max networks”. Proc. in IEEE, Vol.2: ISSN: 1556-6463 ISBN: 978-1-4244-3805-1 pp. 1-8 (2008). Tang H., You Y., Rong C.W. and Shiang C. R. “An Integrated Wi-Max and Wi-Fi Architecture with QoS Consistency over Broadband Wireless Networks”. Proc. in IEEE, Vol.18: ISBN: 978-1-4244-2308-8 pp. 1-7 (2009). Tran M, Zaggoulos G, Nix A. and Doufexi A. “Mobile Wi-Max Performance Analysis and Comparison with Experimental Results”. Proc. in IEEE, Vol.3: ISSN: 1090-3038 ISBN: 978-1-4244-1721-6 pp. 1-5(2008). Wang F., Ghosh A., Sankaran C.,Fleming P. J., Hsieh F. and Benes S. J. “Mobile Wi-Max Systems Performance and Evolution”. Wi-Max, Proc. in Vol.08: ISBN: 978-14244-2308-8 pp. 163-204 (2008). Yoqneam. “Finding the Best Fit Wi-Fi versus Wi-Max”. Proc. in Wavion White Paper, Vol.4: pp. 972.909 (2008)

National Conference on Advanced Computing and Communication Technology

A LINK SOLUTION IN MOBILE ADHOC A LINKFAILURE FAILURE SOLUTION NETWORK THROUGH BACKWARD AODV (B-AODV) Sunil Kumar1, Pankaj Negi2 [email protected],[email protected]

Abstract- In mobile ad hoc networks, mobile devices wander autonomously for the use of wireless links and dynamically varying network topology. AODV (Ad-hoc on-demand Distance vector routing) is a representative among the most widely studied on-demand ad hoc routing protocols. AODV and most of the on demand ad hoc routing protocols use single route reply along reverse path. Rapid change of topology causes that the route reply could not arrive to the source node, i.e. after a source node sends several route request messages; the node obtains a reply message, especially on high speed mobility. This increases both in communication delay and power consumption as well as decrease in packet delivery ratio. To avoid these problems, we propose a “Backward AODV (B-AODV)” which tries multiple route replies. Backword AODV (B-AODV), which has a novel aspect compared to other on-demand routing protocols on Ad-hoc Networks: it reduces path fail correction messages and obtains better performance than the AODV and other protocols have. Backword AODV provides good results on packet delivery ratio, power consumption and communication delay.

A drawback of existing on-demand routing protocols is that their main route discovery mechanisms are not well concerned about a route reply message loss. More specifically, most of today’s on-demand routing is based on single route reply message. The lost of route reply message may cause a significant waste of performance. In this study we propose BACKWARD AODV which has a novel aspect compared to other on-demand routing protocols on ad-hoc networks. In B-AODV, route reply message is not unicast, rather, destination node uses backward RREQ to find source node. It reduces path fail correction messages and can improve the robustness .of performance. Therefore, success rate of route discovery may be increased even though high node mobility situation. The comparison results show our proposed mechanism improves performance of AODV in most metrics, including packet delivery ratio, average end to end delay and power consumption.

Key Words : AODV, Backward AODV,NS-2, Performance, Packet delivery ratio, communication delay

II Motivation In mobile ad hoc networks nodes may move from one location to another on variety of node speed. As the result, the network topology changes continuously and unpredictably. Only within a short period of time neighboring nodes can loose communication link, especially when the mobility is high. In on-demand routing protocols, loosing a communication link between nodes brings route breaks and packet losses. Especially, loosing the RREP of AODV protocol produces a large impairment on the AODV protocol [2]. In fact, a RREP message of AODV is obtained by the cost of flooding the entire network or a partial area[1-5]. RREP loss leads to source node reinitiate route discovery process which causes degrade of the routing performance, like high power consumption, long end-to-end delay and inevitably(unavoidable) low packet delivery ratio. Therefore, we are considering how simply to decrease the loss of RREP messages[4]. We can see a situation in Figure 1, where S is a source node, D is a destination node and others are intermediate nodes. In traditional AODV, when RREQ is broadcasted by node S and each node on a path builds reverse path to the previous node, finally the reverse path D->3->2->1->S is built. This reverse path is used to deliver RREP message to the source node S. If node 1 moves towards the arrow direction and goes out of transmission range of node 2, RREP missing will occur and the route discovery process will be useless. We can easily know that several alternative paths built by the RREQ message are ignored.

I. Introduction A mobile ad hoc network is a dynamically self-organizing network without any central administrator or infrastructure support. If two nodes are not within the transmission range of each other, other nodes are needed to serve as intermediate routers for the communication between the two nodes. Moreover, mobile devices wander autonomously and communicate via dynamically changing network. Thus, frequent change of network topology is a tough challenge for many important issues, such as routing protocol robustness, and performance degradation resiliency[2-8]. Proactive routing protocols require nodes to exchange routing information periodically and compute routes continuously between any nodes in the network, regardless of using the routes or not. This means a lot of network resources such as energy and bandwidth may be wasted, which is not desirable in MANETs [6] where the resources are constrained. On the other hand, on-demand routing protocols don’t exchange Routing information periodically. Instead, they discover a route only when it is needed for the communication between two nodes[1,6,7]. Due to dynamic change of network on ad hoc networks, links between nodes are not permanent. In occasions, a node cannot send packets to the intended next hop node and as a result packets may be lost. Loss of packets may affect on route performance in different ways. Among these packet losses, loss of route reply brings much more problems, because source node needs to re-initiate route discovery procedure. [email protected], M.Tech student , YMCA University of Science & Technology, Faridabad 2. [email protected] , M.Tech student , YMCA University of Science & Technology, Faridabad

582

National Conference on Advanced Computing and Communication Technology

Type

Reserved

Hop Count

Broadcast ID Destination IP address Destination Sequence Number Source IP address Source Sequence number Request Time

Fig. 2. RREQ Message Format in AODV Whenever the source node issues a new RREQ, the broadcast ID is incremented by one. Thus, the source and destination addresses, together with the broadcast ID, uniquely identify this RREQ packet. The source node broadcasts the RREQ to all nodes within its transmission range. These neighboring nodes will then pass on the RREQ to other nodes in the same manner. As the RREQ is broadcasted in the whole network, some nodes may receive several copies of the same RREQ. When an intermediate node receives a RREQ, the node checks if already received a RREQ with the same broadcast id and source address. The node cashes broadcast id and source address for first time and drops redundant RREQ messages. The procedure is the same with the RREQ of AODV. When the destination node receives first route request message, it generates Backword Route request (B-RREQ) message and broadcasts it to neighbor nodes within transmission range like the RREQ of source node does. And whenever the original source node receives first B-RREQ message it starts packet transmission, and late arrived B-RREQs are saved for future use. The alterative paths can be used when the primary path fails communications. Let’s see the same case of AODV, we have mentioned above, in figure 3. In B-AODV, destination does not unicast reply along predecided shortest reverse path D->3->2->1->S.

Fig 1. RREP Delivery Fail in AODV There are some possibilities that after sending a number of RREQ messages, source node can obtain a route reply message. We propose the B-AODV to avoid RREP loss and improve the performance of routing in MANET. B-AODV uses absolutely same procedure of RREQ of AODV to deliver route reply message to source node. We call the route reply messages Backward Route Request (B-RREQ). B-AODV protocol can reply from destination to source if there is at least one path to source node. In this manner, B-AODV prevents a large number of retransmissions of route request messages, and hence diminishes the congestion in the network. Moreover, B-AODV will improve the routing performance such as packet delivery ratio and end-to-end delay.

III Proposed B-AODV Protocol In this section we present an overview and purpose of proposed Backword AODV protocol. 3.1 Protocol Overview Analyzing previous protocols, we can say that most of on-demand routing protocols, except multipath routing, uses single route reply along the first reverse path to establish routing path. As we mentioned before, in high mobility, pre-decided reverse path can be disconnected and route reply message from destination to source can be missed. In this case, source node needs to retransmit route request message. Purpose of our study is to increase possibility of establishing routing path with less RREQ messages than other protocols have on topology change by nodes mobility. Specifically, the proposed B-AODV protocol discovers routes ondemand using a reverse route discovery procedure. During route discovery procedure source node and destination node plays same role from the point of sending control messages. Thus, after receiving RREQ message, destination node floods Backword Route Request (BRREQ) to find source node. When source node receives a B-RREQ message, data packet transmission is started immediately. 3.2 Route Discovery in B-AODV Since B-AODV is reactive routing protocol, no permanent routes are stored in nodes. The source node initiates route discovery procedure by broadcasting the RREQ message contains following information (Figure 2): message type, source address, destination address, broadcast ID, hop count, source sequence number, destination sequence number, request timestamp.

Fig -3 B-RREQ from Destination to Source Node Rather it floods B-RREQ to find source node S. And forwarding path to destination is built through this B-RREQ. Following paths might be built: S->4->5->6->D, S->10->9->8>7->D and etc. Node S can choose best one of these paths and start forwarding data packet. So RREP delivery fail problem on AODV does not occur in this case, even though node 1 moves from transmission range.

583

National Conference on Advanced Computing and Communication Technology

B-RREQ message (Figure 4) contains following information: reply source id, reply destination id, reply broadcast id, hop count, destination sequence number, reply time (timestamp). When broadcasted B-RREQ message arrives to intermediate node, it will check for redundancy. If it already received the same message, the message is dropped, otherwise forwards to next nodes.

IV COMPARISON BETWEEN BACKWARD-AODV AND AODV To evaluate performance of B-AODV with that of AODV protocol, we compare them using four metrics: Type

Reserved

Furthermore, node stores or updates following information of routing table: …

Destination Node Address

…

Source Node Address

…

Hops up to destination

…

Destination Sequence Number

Broadcast ID Destination IP address Destination Sequence Number Source IP address Source Sequence number

… Route expiration time and next hop to destination node. 3.3 Route Update and Maintenance

Request Time

When control packets are received, the source node chooses the best path to update, i.e. first the node compares sequence numbers, and higher sequence numbers mean recent routes. If sequence numbers are same, then compares number of hops up to destination, routing path with fewer hops is selected. Since the wireless channel quality is time varying, the best path varies over time. The feedback from the MAC layer can be used to detect the connectivity of the link. When a node notifies that its downstream node is out of its transmission range, the node generates a route error (RERR) to its upstream node. If fail occurs closer to destination node, RERR received nodes can try local-repair, otherwise the nodes forward RERR until it reaches the source node. The source node can select alternative route or trigger a new route discovery procedure.

Fig. 4. B-RREQ Message Format Delivery Rate: The ratio of packets reaching the destination node to the total packets generated at the source node. We can see performance according to increasing number of nodes, packet deliver ratio of AODV and B-AODV, by increasing number of nodes brings apparent difference between the two protocols.

Average End-to-End Delay: The interval time between sending by the source node and receiving by the destination node, which includes the processing time and queuing time. Average end-to-end delay of each protocol. It should be noted that the delay is considered for the packets that actually arrive at the destinations. We can see that B-AODV has lower delay than AODV. The reason is that AODV chooses route earlier, BAODV chooses recent route according to reverse request. Average end to end delay where maximum speed of node varies. As fast node mobility causes high topology changes, recently selected path may have better consistency.

3.4 Control Packet Overhead Intuitively, we can say that B-AODV causes a lot of control packet overhead. However, we can prove that route discovery procedure based on single reply message may cause even more packet overhead for some cases. We define the followings: • •

An ad hoc network has N number of nodes Required number of control messages to discover routing path for AODV is AODV(N) Required number of control messages to discover routing path for RAODV is B-AODV (N)

Average Energy Remained: B-AODV has more remained energy than AODV, which will be helpful for nodes to survive in network. Mean value of energy remained in each node, due to overall route discovery and route maintenance is less than AODV.

Let’s say m nodes participate to discover a routing path. Then AODV obtains a routing path using control message shown in (1), if it does not fail in first try. AODV(m) = (m-1+t)

Control Overhead: All route request messages, route reply messages and route error messages are considering for control overhead. Control packet overhead required by the transportation of the routing packets. AODV has less control packet overhead. The reason is that BAODV floods route reply message, but route reply message in AODV is unicast along reverse path. So we can say that, half of these messages are B-RREQ.

(1)

Where t is the number of nodes relied on route reply message. If source node fails in first try, because route reply message could not arrive, the node reinitiates path discovery, the number of control messages increase by the number of tries expressed in function (2). AODV (m) =C (m −1+ t)

Hop Count

We can see that BACKWARD AODV (B-AODV) have some advantages over AODV in Table 1

(2)

Metrics Link Failure Data loss Route Discovery Control overhead End-to-End delay Packet delivery ratio

Where C is the number of tries for route discovery. When we assume that B-AODV has at least one stable path by a RREQ, then the number of control messages for B-AODV is in function (3). It will require only 2m-2 messages for route discovery. B-AODV(m )=Ο(2m−2 ) (3) So we can conclude when c>1, then AODV causes more packet overhead than the case of c=1 on R-AODV routing.

B-AODV Not occur Not occur Less Less Less Take less time

AODV Occur Occur More More More Take more time

Table 1 Comparison between BACKWARD-AODV and AODV

584

National Conference on Advanced Computing and Communication Technology

V. Conclusion 8. C. Perkins, E. Belding-Royer “Ad hoc on-Demand Distance Vector (AODV) Routing”, RFC 3561, July 2003 9. I. Stojmenovic, M. Seddigh, J. Zunic, ”Dominating sets and neighbor elimination-based broadcasting algorithms in wireless networks”, IEEE Transactions on Parallel and Distrib- uted Systems, 2002, pp. 14-25. 10. J.Wu, and H. Li, ”On Calculating Power-Aware Connected Dominating Sets for Efficient Routing in Ad HocWireless Networks”, in Proc. of the 3rd Int’l Workshop on Discrete Al- gorithm and Methods for Mobile Computing and Commun., 1999, pp. 7-14. 11. Jae-Ho Bae, Dong-Min Kim, Tae-Hyoun Kim, Jaiyong Lee. An AODV-based Efficient Route Re-Acquisition Scheme in Ad Hoc Networks. 12. Y.Kim, J.Jung, S.Lee and C.Kim, “A Belt-Zone Method for Decreasing Control Messages in Ad Hoc Netowkrs” ICCSA 2006, LNCS 3982, pp 64-72, 2006.

Successful delivery of RREP messages are important in on-demand routing protocols for ad hoc networks. The loss of RREPs causes serious impairment on the routing performance. This is because the cost of a RREP is very high. If the RREP is lost, a large amount of route discovery effort will be wasted. Furthermore, the source node has to initiate another round of route discovery to establish a route to the destination. We proposed the idea of “BACKWARD AODV (BAODV)”, which attempts backward RREQ. B-AODV route discovery succeeds in fewer tries than AODV. We conducted extensive comparison study to evaluate the performance of BAODV and compared it with AODV. B-AODV improves the performance of AODV in most metrics, as the packet delivery ratio, end to end delay, and energy consumption. Our future work will focus on studying practical design and implementation of the BAODV. Multipath routing is another topic we are interested in.

References 1. C. E. Perkins and E. M. Royer, “Ad hoc on-demand distance vector routing,” in Proc. WMCSA, New Orleans, LA, Feb. 1999, pp. 90–100. 2. Zhi Li and Yu-Kwong Kwok, “A New Multipath Routing Approach to Enhancing TCP Security in Ad Hoc Wireless Networks” in Proc. ICPPW 2005. 3. Rendong Bai and Mukesh Singhal, “Salvaging Route Reply for On-Demand Routing Pro- tocols in Mobile Ad-Hoc Networks” in MSWIM 205, Montreal, Quebec, Canada. Oct 2005 4. C. K.-L. Lee, X.-H. Lin, and Y.-K. Kwok, “A Multipath Ad Hoc Routing Approach to Combat Wireless Link Insecurity”. Proc. ICC 2003, vol. 1, pp. 448–452, May 2003. 5. S.-J. Lee and M. Gerla, “Split Multipath Routing with Maximally Disjoint Paths in Ad Hoc Networks,” Proc. ICC 2001, vol. 10, pp. 3201–3205, June 2001. 6. M. K. Marina and S. R. Das “On-Demand Multi Path Distance Vector Routing in Ad Hoc Networks,” Proc. ICNP 2001, pp. 14– 23, Nov. 2001. 7. A. Nasipuri and S. R. Das, “On-Demand Multipath Routing for Mobile Ad Hoc Net- works,” Proc. ICCN 1999, pp. 64– 70, Oct. 1999.

585

National Conference on Advanced Computing and Communication Technology

LDPC Code based Advanced & Secure Wireless Communication Systems Naresh Kumar1, M.Tech*, N.C. College of Engineering Israna, Panipat Rajesh Kumar Malik1, Assistant Professor, N.C. College of Engineering Israna, Panipat Somnath Deepak2, Lecturer, Jind Institute of Engineering & Technology, Jind (Haryana) India 1

[email protected] , [email protected], 2 [email protected]

Abstract- The rapid evolution of global mobile communication demands high data rate transmission via satellites, which in turn requires spectrally efficient modulation technique and power efficient forward-error correction, schemes [19]. The main objective of any communication system is error free transmission with maximum possible data rate [8]. Noisy communication channels are the major problems in this case. To overcome this problem we can use the channel coding along with the suitable modulation scheme. Thus Channel coding and modulation have the same objective of producing the appropriate signal waveforms to cope with the noisy channel. Today, LDPC are the hottest channel linear block codes and provides the best performance in noisy channels like AWGN and Rayleigh (Fading) channel etc. along with suitable modulation schemes like MPSK and QAM etc. Under the fading conditions we can further improve the performance by applying the concept of diversity analysis. The performance of LDPC codes like any other code is measured in terms of BER, FER and [20] Girth Test. LDPC codes were ignored for long time, but now they are used even more than Turbo Codes because of their good block error correcting performance, low error floor and their suitability for parallel implementation. This has been possible now because of the availability of iterative decoding schemes, parallel decoding structures and message passing algorithms etc. [6] Today LDPC codes are used in most of the Wireless, Wired, Optical, OFDM, ADSL

communication systems. The future of 4G wireless, Satellite Communication and Digital Television lies with LDPC codes. [9], [10] L DPC codes could be used in magnetic stor age devi ces because of their better decoding per for mance compar ed to other er r or cor r ection codes. Index Terms- AWGN, Bit Error Rate (BER), Bipartite Graph, BPSK Modulation, Diversity, Frame Error Rate (FER), Iterative Decoding, LDPC Codes, Linear Block Codes, Message Passing Algorithm, Parity Check Matrix, 32-QAM Modulation.

I.

INTRODUCTION

Recently, low-density parity-check (LDPC) codes have attracted much attention because of their excellent error correcting performance and [23] highly parallelizable decoding scheme. [1], [2] Low-density parity-check (LDPC) codes are a class of linear block codes. The name comes from the characteristic of their parity-check matrix which contains only a few 1’s in comparison to the amount of 0’s. Their main advantage is that they provide a performance which is very close to the capacity for a lot of different channels and linear time complex algorithms for decoding. These are best suited for implementations that make heavy use of parallelism. Low-density parity-check code is an error correcting code. LDPC codes are also known as Gallager codes, in honor of Robert G. Gallager. [4], [5], [22] LDPC was the first code to allow data transmission rates close to the theoretical 586

National Conference on Advanced Computing and Communication Technology [2] Low density parity check codes (LDPC) are linear block codes with sparse parity check matrices. The original Gallager’s LDPC codes are called regular LDPC codes in which the number of 1’s is the same in every row and every column. The following H matrix is the parity check matrix of a rate-1/2, length-10 regular LDPC code:

maximum limit. In 2003, an LDPC code beat six turbo codes to become the error correcting code in the new DVB-S2 standard for the satellite transmission of digital television. Given a specific channel, the performance of LDPC codes is measured in terms of either biterror probability or block-error or frame error probability. [7] LDPC codes make use of parity check matrix. • A parity check matrix is an r-row by ncolumn binary matrix. Remember k=n-r. • The rows represent the equations and the columns represent the digits in the code word. • There is a 1 in the i-th row and j-th column if and only if the i-th code digit is contained in the j-th equation. • Decoding of LDPC codes is best understood by the graphical description of the parity equations. • The graph has two types of nodes: bit nodes and parity nodes. • Each bit node represents a code symbol and each parity node represents a parity equation. • There is a line drawn between a bit node and a parity node if and only if that bit is involved in that parity equation.

II.

1 0  H = 0  1 1

0 0 1 (3)  1 1 0 0 1 0 1 0 1 1

1 0 1 0

1 1 0 1

1 1 1 0

0 1 0 1

1 1 1 0

1 1 0 0

0 1 1 1

0 0 1 1

In this matrix, the number of 1’s in each row is 6 and the number of 1’s in each column is 3. This parity check matrix is not very spare because the code is still short. This code can be presented by a bipartite graph as in Fig. 1. [11], [12], [13] and [14] In this graph, each left node, called a variable node, or bit node represents a bit of the codeword. Each right node, called a check node, represents a parity check bit. The number of variable nodes corresponds to the number of columns in the parity check matrix H, while the number of check nodes corresponds to the number of rows in H. Edges connect the variable nodes to the check nodes according to the parity check matrix H.

SYSTEM MODEL USING LDPC CODES

Objective of this paper is to evaluate the bit error rate (BER) performance of bandwidthefficient LDPC coded communication system in AWGN channel. BER is the most common criterion for performance evaluation of a communication system. Each linear block code has a parity check matrix H that is often used in the decoder. The valid codewords satisfy: x. HT = 0 (1) The parity check matrix H is related to the generator matrix G by (2) G.HT = 0 587

National Conference on Advanced Computing and Communication Technology capacity, where δ is some small positive constant. It is known [6] that the number of ones in the parity-check matrix H has to scale at least like n ln(1/ δ) as a function of δ, as δ approaches zero, where n is the block length of the code. Message-passing decoding algorithm works by performing several (simple) decoding rounds, and the required number of such rounds is guessed to grow like 1/ δ ln(1/ δ).

Fig. 1. The bipartite graph of a regular (3, 6) LDPC code of length 10 If the number of edges emanating from a variable node is called variable node degree d v and the number of edges emanating from a check node is called check node degree d c , then the rate of the (d v ,d c ) regular LDPC code is d Rc = 1 − v . The number of 1’s in the parity dc check matrix H is N.d v , while the total number of elements in H is N2.Rc, where N is the length of the code. Obviously, when N increases, the number of 1’s increases linearly and the total number of elements increases quadratically. Hence, the parity check matrix is sparse with large N. [2] The sparse characteristic of the parity check matrix is important, because the number of 1’s presents the number of relations between a variable node and a check node. Since the decoder uses these relations to decode, this quantity determines the complexity of the decoder.

Message-Passing Algorithm [18] In message-passing algorithm, messages are exchanged along the edges of the graph, and computations are performed at the nodes, as shown in Fig. 2. Each message represents an estimate of the bit associated with the edge carrying the message. These decoders can be understood by focusing on one bit as follows: Suppose the bits of an LDPC codeword are transmitted over a communications channel and, during transmission, some of them are corrupted so that a 1 becomes a 0 or vice versa. Each bit node in the decoder gets to see the bit that arrived at the receiver corresponding to the one that was transmitted from the equivalent node at the transmitter. Imagine that the node would like to know if that bit is in error or not and therefore asks all its neighboring check nodes what the bit’s value should be. Each neighboring check node then asks its other neighbors what their values are and sends back to the original bit node the modulo 2 sum of those value. The bit node now has several opinions as to the bit’s correct value must somehow reconcile these opinions; it could, for example, take a majority vote. In order to improve performance, a further such iteration can be performed.

Iterative Decoding Scheme [3] The principal objective of defining the code in terms of explicit subcodes, such as LDPC codes, is to reduce the complexity of the decoding process to provide high quality codes that can be decoded effectively by computational process whose complexity grows only very slowly with increasing code length at fixed code rate. For many parity-check codes the number of 0’s and 1’s in the parity-check matrix H are approximately the same. As mentioned previously, for LDPC codes the number of 1’s is very small compared to the number of 0’s: The parity-check matrix H has a low density of 1’s. [16] And [17] Equivalently, the Tanner graph of the code has a low-density of edges. The complexity of the decoding algorithm of LDPC codes depends directly on this density so that a concerned designer of LDPC codes will try to keep this density as low as possible. Assume we would like to transmit at a fraction − 1 δ of

LDPC Decoder Architecture [1], [15] Gallager sketched a simplified block diagram to show how message passing algorithm can be done. [21] LDPC decoders are low floor decoders. 588

National Conference on Advanced Computing and Communication Technology He guessed from the Fig. 3 that a parallel computer can be simply instrumented requiring principally a number of proportional to n analog adders, modulo 2 adders, amplifers, and nonlinear circuits to approximate the function F (β).

correction factor determined by a table lookup, as illustrated in the Fig. 4.

Fig. 2. Illustration of message-passing algorithm on a bipartite graph

Fig. 4. Check-node processor elements Iterative Decoding Architecture- Parallel architecture

Fig. 3. Decoding implementation However, evaluating the sum in the logprobability domain requires a combination of exponential and logarithmic functions. In order to simplify the implementation, the computation can be approximated with the maximum value of the input operands, followed by an additive Fig. 5. Parallel LDPC decoder architecture 589

National Conference on Advanced Computing and Communication Technology

Generate the Parity Check Matrix, Load H, Define SNR Range, Set Maximum number of iterations, and Set Maximum number of codeword-errors for which to run simulation and select the MATLAB as decoder.

The message passing algorithm is inherently parallel because there is no dependency between computation of either Qi→j, for i = 1, 2. . . N or Rj→i for j = 1, 2. . . N.

Simulation Results Parallel decoder architectures directly map the nodes of a bipartite graph onto message computation units known as processing elements, and the edges of the graph onto a network of interconnect. Thus, such decoders benefit from a small switching activity, resulting in low power dissipation. Very little control logic is needed for the parallel architecture, because the LDPC code graph is directly instantiated by the interconnection of processing elements. Higher throughput with parallel decoder can be achieved by implementing a code with a large block size and maintaining the same clock frequency. The major drawbacks with parallel decoder architecture are the relatively large area and the inability to support multiple block size and code rates on the same core. However, for application that requires high throughput and low power dissipation and can tolerate a fixed code format and large area, the parallel architecture is very suitable.

Performance results of LDPC Codes are shown in Fig. 6. , Fig. 7. , Fig. 8. , Fig. 9 and Fig. 10.

Fig. 6. BER Performance of LDPC Codes

III. PROGRAM CODING & SIMULATION RESULTS The performance of any coding scheme is measured in terms of Bit Error Rate and Frame Error Rate. Now the performance of LDPC Codes is also measured by calculating the BER and FER. The performance is calculated for AWGN channel and modulation scheme as BPSK for low data rate applications and xQAM for high data rate applications in wireless communication. Main steps for Simulation are:

LDPC

Performance 590

National Conference on Advanced Computing and Communication Technology Fig. 7. FER Performance of LDPC Codes

Fig. 10. Performance of BPSK based system with Diversity Order 1, 3 and 5 respectively. Simulation Results show that the BER and FER performance of LDPC Codes is improved with the increase in SNR and for a specific bit error rate, LDPC coded data with BPSK modulation technique gives gain (improved performance) over the use of uncoded data under AWGN environment. So, the performance of LDPC Coded BPSK modulation technique is better in comparison to uncoded BPSK. Fig. 8. also shows that for low value of SNR, coded and uncoded data follows approximately same behavior, for QAM based system the performance is shown in Fig. 9. , we can see for LDPC coded system as the SNR increases the performance is improved sharply, so LDPC code based wireless communication systems are better in AWGN channel for specific application with varying data rates. As for AWGN channel, the BER performance can also be improved for fading channels like Rayleigh with the use of LDPC codes along with the suitable modulation schemes. Under fading conditions the performance can further be improved by applying the concept of diversity analysis.

Fig. 8. LDPC BER performance of BPSK modulation system over AWGN Channel for wireless communication

Fig. 9. SNR vs BER performance of 32-QAM modulation system over AWGN channel for wireless communication. 591

National Conference on Advanced Computing and Communication Technology [1] R. Gallager, \Low-density parity-check codes," IRE Trans. Information Theory, pp. 21{28, January 1962. [2] D. MacKay, \Good error correcting codes based on very sparse matrices," IEEE Trans. Information Theory, pp. 399{431, March 1999. [3] J. L. Fan Constrained coding and soft iterative decoding for storage. PhD thesis, Stanford University, 1999. [4] T. Richardson, M. Shokrollahi, and R. Urbanke, \Design of capacity-approaching irregular low-density parity-check codes," IEEE Trans. Inform. Theory, vol. 47, pp. 638{656, Feb. 2001. [5]Chung, et al, “On the design of low-density paritycheck codes within 0.0045dB of the Shannon limit”, IEEE Comm. Lett., Feb. 2001 [6] L. Van der Perre, S. Thoen, P. Vandenameele, B. Gyselinckx, and M. Engels, “Adaptive loading strategy for a high speed OFDM-based WLAN”, Globecomm 98 [7] Numerous articles on recent developments LDPCs, IEEE Trans. On IT, Feb. 2001 [8] C. E. Shannon, \A mathematical theory of communication," Bell System Technical Journal, vol. 27, pp. 379{423, 1948. [9] H.Song, “Low complexity LDPC codes for magnetic recordings,” IEEE Globecom 2002, November 2002. [10] C.Riggle and S. McCathy, “Design of error correction systems for disk drives,” IEEE Transactions Magazine, July 1998, vol. 34, pp .2362- 2371. [11] H. Zhang and J.M. Moura, “Large-girth LDPC codes based on graphical models,” IEEE Workshop on Signal Processing., 2003, pp-100-103. [12] N.L Biggs, “Constructions for Cubic Graphs of large Girth,” Electronic Journal of Combinatorics, vol. 5, 1998. [13] G. Exoo, “A Simple Method for Constructing Small Cubic Graphs of Girths 14, 15 and 16,” Electronic Journal of Combinatorics, Vol. 3, 1996. [14] P.K Wong, “Cages—A Survey,” Journal of Graph Theory. vol. 3 1982, pp-1-22 [15] R. G. Gallager. Low-Density Parity-Check codes. PhD thesis, MIT Press, Cambridge, MA, July 1963. [16] E. Yeo, B. Nikolic, and Venkat Anantharam. Iterative decoder architectures. IEEE communication magazine, pages 132–140, August 2003. [17] R M. Tanner. A recursive approach to low complexity codes. IEEE transaction information theory, IT- Vol 27, Issue 5, pages :533–547, September 1981. [18] T. Richardson and R. Urbanke, \The capacity of lowdensity parity check codes under message- passing decoding," IEEE Trans. Inform. Theory, vol. 47, pp. 599{618, 2001.3} [19] Abdul Hussein, H.; Al-Asady; Ibnkahla, M., “Performance evaluation and total degradation of 16QAM modulations over satellite channels”. Electrical and Computer Engineering, Canadian Conference on, 2004, Vol.2, pp: 1187 – 1190.

Performance of BPSK based system with Diversity Order 1, 3 and 5 is shown in Fig. 10. with Blue, Green and Red colors respectively. From Fig. 10. it is observed that the BER value 10-2 is achieved at E b /N 0 as 14 dB with diversity order 1, as 7.6 dB with diversity order 3 and as 6 dB with diversity order 5. Thus a significant gain of 8 dB is obtained using the diversity order 5 over the diversity order 1 and gain of 1.6 dB is obtained using the diversity order 5 over the diversity order 3. A gain of 6.4 dB is obtained using the diversity order 3 over the diversity order 1. Thus the performance can be improved using diversity techniques but the receiver side becomes complex and costly.

IV.

CONCLUSION

In this paper we have analyzed the performance of LDPC code based wireless communication systems by calculating the BER. We also found that for BPSK & QAM modulation schemes in AWGN channel the LDPC coded communication system has better performance as compared to the uncoded system in terms of BER. We also can see that at low value of SNR the performance of both coded and uncoded communication system is almost comparable for BPSK based system. For better performance LDPC coding must be used with high value of SNR. The inherent parallelism in decoding LDPC codes suggests their use in high data rate systems using QAM & for low data rate applications using BPSK. Like AWGN the LDPC code based systems along with suitable modulation scheme can also be applied for fading channels like Rayleigh Channel. Under fading conditions the performance is further improved by diversity techniques but this improvement is at the cost of extra money and receiver complexity. It is now possible to closely approach the Shannon limit by using LDPC codes.

V. REFERENCES 592

National Conference on Advanced Computing and Communication Technology [20] Jin Lu and Jose M.F. Moura,”TS-LDPC Codes: Turbo Structured Codes with Large Girth,” IEEE Trans. On Information Theory, Vol. 53, No. 3, March 2007. [21] Yang Han and William E. Ryan, “Low Floor Decoders for LDPC Codes,” IEEE Trans. On Communications, Vol. 57, No. 6, June 2009. [22] Igal Sason, “On Universal Properties of Capacity Approaching LDPC Code Ensembles,” IEEE Trans. On Information Theory, Vol. 55, No. 7, July 2009. [23] Zhen Gang Chen, Tyler Brandon, Duncan G. Elliot, Stephen Bates, Witold A. Krzymien and Bruce F. Cockburn,”Jointly Designed Architecture: Aware LDPC codes & High throughput Parallel Encoders and Decoders,” IEEE Trans. On Circuits & System-I, Reg. Papers, Vol. 57, No. 4, April 2010.

593

National Conference on Advanced Computing and Communication Technology

Security Issues in Near Field Communication (NFC) Abhiruchi Passi

Sunita Virmani

Department of Electronics & Communication Engineering Faculty of Engineering and Technology Manav Rachna International University Faridabad [email protected]

Department of Electronics & Communication Engineering Faculty of Engineering and Technology Manav Rachna International University Faridabad [email protected]

Deepak Batra Department of Electronics & Communication Engineering Faculty of Engineering and Technology Manav Rachna International University Faridabad. [email protected]

Abstract — Near Field Communication (NFC) is a shortrange (a few centimeters) wireless connectivity technology that enables simple and safe two-way interaction between electronic devices, allowing consumers to perform contact less transactions, access digital content, and connect electronic devices with a single touch. Data security is a critical factor for increasing the use of NFC technology. This paper presents the various possible security threats related to applications of NFC i.e Data Corruption, Data modification, Man–in-the-middle attack & Data Insertion, and describes the possible solutions for the same.

Table 1: Communication Configurations Device Device Description A B Active Active When a device sends data it generates an RF field. When waiting for data a device does not generate an RF field. Thus, the RF field is alternately generated by Device A and Device B Active Passive The RF field is generated by Device A only Passive Active The RF field is generated by Device A only

Keywords- NFC, Data Corruption, Data Modification, Man-in-theMiddle Attack, Data Insertion

I.

INTRODUCTION

Near Field Communication (NFC) is a short-range high frequency wireless communication technology which enables the exchange of data between devices over about a 10 centimetre. NFC communicates via magnetic field induction, where two loop antennas are located within each other's near field, effectively forming an air-core transformer. It operates within the globally available and unlicensed radio frequency ISM band of 13.56 MHz, with a bandwidth of 14 kHz.

These configurations are important because the way data is transmitted depends on whether the transmitting device is in active or passive mode. In active mode the data is sent using amplitude shift keying (ASK). This means the base RF signal (13,56 MHz) is modulated with the data according to a coding scheme. If the baudrate is 106 kBaud, the coding scheme is modified Miller coding. If the baudrate is greater than 106 kBaud the Manchester coding scheme is applied. In both coding schemes a single data bit is sent in a fixed time slot. This time slot is divided into two halves, called half bits. In Miller coding a zero is encoded with a pause in the first half bit and no pause in the second half bit. A one is encoded with no pause in the

NFC can operate in several modes. The modes are distinguished whether a device creates its own RF field or whether a device retrieves the power from the RF field generated by another device. If the device generates its own field it is called an active device, otherwise it is called a passive device. Active devices usually have a power supply, passive devices usually don't (e.g. contactless Smart Card). When two devices communicate three dif-ferent configurations are possible. These are described in Table 1:. 594

National Conference on Advanced Computing and Communication Technology When the user wants to perform a payment or use the stored ticket, the user presents the device to a reader, which checks the received information and processes the payment or accepts/rejects the ticket. In this application example the user device must be able to perform a certain protocol with the reader. A simple read operation will not be sufficient in most cases. Also, the user device is likely to have a second interface which is used to load money or to buy tickets. This second interface can for example be linked to the mobile phone CPU. The ticket data could then be loaded into the mobile phone via the cellular network.

first bit, but a pause in the second half bit. In the modified Miller coding some additional rules are applied on the coding of zeros. In the case of a one followed by a zero, two subsequent half bits would have a pause. Modified Miller coding avoids this by encoding a zero, which directly follows a one with two half bits with no pause. In the Manchester coding the situation is nearly the same, but instead of having a pause in the first or second half bit, the whole half bit is either a pause or modulated. Besides the coding scheme also the strength of the modulation depends on the baudrate. For 106 kBaud 100% modulation is used. This means that in a pause the RF signal is actually zero. No RF signal is sent in a pause. For baudrates greater than 106 kBaud 10% modulation ratio is used. According to the definition of this modulation ratio this means that in a pause the RF signal is not zero, but it is about 82% of the level of a non paused signal. This difference in the modulation strength is very important from a security point of view.

3. Device Pairing In this application the two devices communicating would belong to the same group of devices. An example could be a laptop and a digital camera. The user wants to establish a Bluetooth connection between the two devices to exchange image data. The Bluetooth link is established by bringing the two devices close together and running a given pro-tocol over NFC between the two devices. This makes it obvious for the user which two devices get actually linked and takes away the burden of navigating through menus and selecting the right devices from lists of possible communication partners.

In passive mode the data is sent using a weak load modulation. The data is always encoded using Manchester coding with a modulation of 10%. For 106 kBaud a subcar-rier frequency is used for the modulation, for baudrates greater than 106 kBaud the base RF signal at 13.56 MHz is modulatedPower Management

III. II.

APPLICATIONS OF NFC 1.

1.

Contactless Token

THREATS

Data Corruption

Instead of just listening an attacker can also try to modify the data which is transmitted via the NFC interface. In the simplest case the attacker just wants to disturb the communication such that the receiver is not able to understand the data sent by the other device. Data corruption can be achieved by transmitting valid frequencies of the data spectrum at a correct time. The correct time can be calculated if the attacker has a good understanding of the used modulation scheme and coding. This attack is not too complicated, but it does not allow the attacker to manipulate the actual data. It is basically a Denial of Service attack.

This covers all applications, which use NFC to retrieve some data from a passive token. The passive token could be a contactless Smart Card, an RFID label, or a key fob. Also, the token could be physically included in a device without any electric connections to that device. What is important is that the only interface of the token is the contactless interface. This means it cannot act as a communication link to a device main CPU of a device because it cannot connect to the device main CPU via a contact interface. Let us also assume that the token has rather limited computing power, so it cannot run any complex protocols. The primary use would be to store some data, which can then conveniently be read by an active NFC device. Example of such data would be a URL stored in a tag of a consumer product or the user guide of such a product. The user could then read the tag and get automatically linked to the support web page of that product.

2.

Data Modification

In data modification the attacker wants the receiving device to actually receive some valid, but manipulated data. This is very different from just data corruption. The feasibility of this attack highly depends on the applied strength of the amplitude modulation. This is because the decoding of the signal is different for 100% and 10% modulation. In 100% modulation the decoder basically checks the two half bits for RF signal on (no pause) or RF signal off (pause). In order to make the decoder understand a one as a zero or vice versa, the attacker must do two things. First, a pause in

2. Ticketing / Micro Payment in this example application, the NFC interface is used to transfer some valuable infor-mation. The ticket or the micro payment data is stored in a secure device. This could be a contactless Smart Card, but could as well be a mobile phone. 595

National Conference on Advanced Computing and Communication Technology the modulation must be filled up with the carrier frequency. This is feasible. But, secondly, the attacker must generate a pause of the RF signal, which is received by the legitimate receiver. This means the attacker must send out some RF signal such that this signal perfectly overlaps with the original signal at the receiver’s antenna to give a zero signal at the receiver. This is practically impossible. However, due to the modified Miller coding in the case of two subsequent ones, the attacker can change the second one into a zero, by filling the pause which encodes the second one. The decoder would then see no pause in the second bit and would decode this as a correct zero, because it is preceded by a one. In 100% modulation an attacker can therefore never change a bit of value 0 to a bit of value 1, but an attacker can change a bit of value 1 to a bit of value 0, in case this bit is pre-ceded by a bit of value 1.

device. Thus, every such attack should be detectable. 2.

NFC devices can check the RF field while sending. This means the sending device could continuously check for such an attack and could stop the data transmission when an attack is detected. 3.

V. CONCLUSION In this paper, we discussed the communication configurations of NFC devices. We also discussed, the Applications of NFC. followed by threats associated with data transmission and counter measures for the same. Secured channel can be provided to NFC using standard key agreement techniques . REFERENCES [1] Agrawal, Dharma Prakash & Zeng, Qing : “An Introduction to wireless and mobile systems”, Thomson Learning [2] IEEE 802.11 Working Group, “Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification” [3] M. Ilyas, “The Handbook of ad hoc wireless networks,” CRC Press [4] Garg, Vijay K & Wilkes, Joseph E. : “Wireless and Personal Communication Systems”, Prentice Hall [5] Gibson, Jerry D. Mobile communications handbook. CRC Press [6] "Information technology - Telecommunications and information exchange between sys-tems — Near Field Communication — Interface and Protocol (NFCIP-1)", ISO/IEC 18092, First Edition, 2004-04-01.

Data Insertion

This means that the attacker inserts messages into the data exchange between two de-vices. But this is only possible, in case the answering device needs a very long time to answer. The attacker could then send his data earlier than the valid receiver. The insertion will be successful, only, if the inserted data can be transmitted, before the original device starts with the answer. If both data streams overlap, the data will be corrupted. IV.

SOLUTIONS

1.

Data corruption

Data Insertion

Data Insertion can be counter measured by using secure channel between the two devices. A standard key agreement protocol like Diffie-Hellmann based on RSA Curves could be applied to establish a shared secret between two devices. The shared secret can then be used to derive a symmetric key like 3DES or AES, which is then used for the secure channel providing confidentiality, integrity, and authenticity of the transmitted data.

In 10% modulation the decoder measures both signal levels (82% and Full) and compares them. In case they are in the correct range the signal is valid and gets decoded. An attacker could try to add a signal to the 82% signal, such that the 82% signal appears as the Full signal and the actual Full signal becomes the 82% signal. This way the de-code would decode a valid bit of the opposite value of the bit sent by the correct sender. Whether the attack is feasible depends a lot on the dynamic input range of the receiver. It is very likely that the much higher signal level of the modified signal would exceed the possible input range, but for certain situations this cannot be ruled out completely. The conclusion is that for the modified Miller encoding with 100% ASK this attack is feasible for certain bits and impossible for other bits, but for Manchester coding with 10% ASK this attack is feasible on all bits. 3.

Data Modification

NFC devices can counter this attack because they can check the RF field, while they are transmitting data. If an NFC devices does this, it will be able to detect the attack. The power which is needed to corrupt the data is significantly bigger, than the power which can be detected by the NFC

596

National Conference on Advanced Computing and Communication Technology

597

National Conference on Advanced Computing and Communication Technology

GRP and TORA Routing Protocols for wireless Mesh Network Sunil Kumar M.Tech, Student Department of Computer Science, Punjabi University, Patiala. [email protected]

Jyotsna Sengupta Reader and Head of Department Department of Computer Science, Punjabi University, Patiala.

Abstract Wireless Mesh Network (WMN) is a multi-hop wireless network with partial mesh topology, which can replace wired infrastructure backbone in a traditional wireless network, to wireless. It is an exciting new technology that has applications in defense, metro-area Internet access, and disaster management. WMNs are believed to be a highly promising technology and will play an increasingly important role in future generation wireless mobile networks. . Routing in wireless mesh network is nontrivial due to highly dynamic nature of the nodes. In this paper we will discuss about routing protocols in Wireless Mesh Network. Routing protocols GRP and TORA have been implemented using OPNET simulator. The protocols are highly scalable and can support thousands of nodes making it an ideal protocol for wireless mesh networks.

Figure 1: A Typical Multichannel WMN [3] WMNs are dynamically self organized and self configured. WMNs can be deployed incrementally, one at a time, as needed. As more nodes are installed, the reliability and connectivity for the users increase accordingly [1]. It is the ideal technology for providing quick and easy network access where network infrastructure is hard to install or has been destroyed and is equally suited to the low cost extension of network access to a wide area[3]. The self-healing capability combined with the mesh topology’s inherent redundancy provides wireless mesh networks with a high level of robustness and fault tolerance The major features of WMN are listed below [3]. (a) WMNs enhance network performance, because of flexible network architecture, easy deployment and configuration, fault tolerance and mesh connectivity. Due to these features, WMNs have low investment requirement, and network can grow gradually as needed. (b)Mobility dependence on the type of mesh nodes:-Mesh routers usually have minimal mobility, while mesh clients can be stationary or mobile nodes. (c) Dependence of power consumption constraints on the type of mesh:-Mesh routers

Keywords- Ad-hoc, AODV, DSR, OLSR, OPNET I. INTRODUCTION Wireless Mesh Network (WMN) is a radical network form of the ever-evolving wireless networks that marks the divergence from the traditional centralized wireless system such as cellular networks and wireless local area networks (WLANs) [2]. Wireless Mesh Network comprised of two types of nodes: mesh routers and mesh clients. To improve the flexibility of mesh networking, mesh routers are usually equipped with multiple wireless (radio) interfaces built on either same or different wireless access technologies. Mesh routers have minimal mobility and form the mesh backbone for the mesh clients. In addition to mesh networking among mesh routers and mesh clients [1], the gateway/bridge functionalities in mesh routers enable the integration of WMNs with various other networks such as the Internet and other wireless networks. Although mesh clients can also work as a router for mesh networking, the hardware platform and software for them can be much simpler than those of mesh routers [1]. The primary advantages of a WMN lie in its inherent fault tolerance against network failures, simplicity of setting up a network and broadband capability. Figure 1 shows a typical Wireless Mesh network

598

National Conference on Advanced Computing and Communication Technology do not have strict constraints on power consumption. However, mesh clients may require power efficient protocols. (d) Compatibility and interoperability with existing wireless networks:-WMNs are interoperable with Wireless LANs, WiMAX and Cellular networks. (e) Mesh Routers are equipped with multiple radios and non overlapping channels can be assigned to each of these radios.

I I ). GRP Flooding Every node knows the initial position of every other reachable nodes once initial flooding is complete in network. When the node moves a distance longer than user has specified or when the node cross a quadrant the routing flooding will take place. Moreover, Extent of flood is dependent of the node movement with respect to the quadrant boundary.

II OVERVIEW OF ROUTING PROTOCOLS I I I ). GRP Routing Table Most of the proposed approaches extend existing routing protocols from IP-based wired networks. Routing protocols can be classified in proactive, reactive and hybrid approaches. With proactive protocols the route information is periodically exchanged among hosts (e.g. DSDV, OSLR), allowing each node to build a global knowledge of the network independently of the actually used routes. Reactive approach limit the exchange of route information, building routes only towards nodes involved in higher layers communication (e.g AODV, DSR, and TORA). Proactive protocols do not scale with large networks, due to the amount of information needed to collect global routing decisions. A).Geographic

Routing

GRP uses Proactive method to let every node maintains route information of another node. For nodes in different quadrants, the highest level neighboring quadrant information is maintained. I V). Hello Pr otocol in GRP Every node broadcasts a periodic hello message with its position information to all its neighbors and this informs a node of all its neighbors as well as the position of each neighbor node and at any time. The rate of hello messages send out should be set based on the mobility of the network.

Protocol B).Temporally-Ordered Routing Algorithm (TORA) TORA[5] is an on-demand routing protocol based on a directed acyclic graph (DAG). TORA is a source initiated protocol and provides multiple routes for any desired source/destination pair. The key design concept of TORA is the localization of control messages to a very small set of nodes near the occurrence of a topological change. In order to accomplish that nodes maintain routing information about adjacent (one-hop) nodes. The protocol performs three basic functions:

(GRP) GRP (Geographic Routing Protocol) [4] is a kind of position-based protocol which belongs to Proactive Routing Protocol. Each position of the node will be marked by GPS and flooding will be optimized by quadrants. Flooding position updates on distance the node moved and neighborhood crossings. A hello protocol will be exchanged between nodes to identify their neighbors and their positions. At the same time, by means of route locking a node can return its packet to the last node when it can’t keep on sending the packet to the next node.

  

I ). GRP Quadr ant

Route Creation Route Maintenance Route Erasure.

TORA maintains state on a per-destination basis and runs a logically separate instance of the algorithm for each destination. TORA assigns directional heights to links so as to direct the flow of traffic from a higher source node to a lower destination [5]. The significance of these heights, which are assigned based on the direction of a link towards the destination, is that a node may only forward packets downstream but not upstream to another node that has a higher, undefined or unknown

In order to reduce route flooding GRP divides Ad hoc into many quadrantsThe size of the quadrant is specified by customers in meters and they should be squares. By doing this the entire world is divided into quadrants from Lat, Long (-90, 180) to Lat, Long (+90, +180).

599

National Conference on Advanced Computing and Communication Technology height. So it can be loop-free. The algorithm is implemented while the update packets return to the source node.

III SIMULATION ENVIRONMENT The research is carried out using discrete event simulation software known as OPNET (Optimized Network Engineering Tool) Modeler version [7]. The simulation focused on the performance of routing protocols with increased in scalability and mobility. Therefore, two simulation scenarios consisting of 50 nodes and 100 nodes considered. The nodes were randomly placed within certain gap from each other in 1200 x 1200 m campus environment for 50 and 100 nodes respectively. The constant Video traffic was generated in the network explicitly i.e. user defined via Application and Profile Configuration.. Every node in the network was configured to execute GRP and TORA respectively. The simulation time was set to 600s.

Table I. Wireless Parameters B. Traffic Flow Parameters Traffic was generated in the network explicitly by configuring user defined application and profile definition 1) Application Configuration A heavier application traffic flow in the topology was generated which each node will be processing from the respective application server in the network. The application traffic generated was as, Video: High Resolution video 2) Profile Configuration The profile configuration for each application was defined as, Operation Mode: Serial (Ordered) and Start Time: 10 Seconds. In addition, the VIDEO application start time was set to constant 10 seconds of time period.

Fig-2

3). DES Configuration Parameter The DES simulation criterion was configured and was run for total time of 900 seconds. The overall simulation was monitored within the following criteria: • D  uration: 6 minutes (600 seconds) • Seed: 128 • Update Interval: 500000 events. (This specifies how often simulation calculates events/second data.) • S  imulation Kernel: Optimized (‘Optimized’ kernel was chosen because it runs faster than the remaining other two simulation kernel.)

50 Nodes Scenario

A. Wireless Parameters The Wireless parameters were common to all of the four routing protocols as shown in table 1.

IV RESULTS AND ANALYSIS The work attempts to compare the protocols in two scenarios. 50 nodes and 100 nodes.

600

National Conference on Advanced Computing and Communication Technology

1. Delay (sec).

Fig-5 Network Load 40 Nodes Fig-3 Wireless Delay-50 Nodes

Fig. 5 and 6 shows the network load for mesh network for 50 and 100 nodes respectively. For GRP the routing load takes the peak at initial stage of the simulation with the drastic rise and drops down slowly as the simulation progresses. In both scenarios GRP produces the best results in mesh network. On the other hand, TORA produces the worst results in both scenario’s, TORA limits the communication overhead to the node area in order to increase the bandwidth utilization. In addition, due to link reverse algorithm employed with TORA, link failure are localized to certain area of the topology which in return improves the performance of the network.

Fig 3 and 4 shows the overall delay in mesh network for 50 and 100 nodes. Delay means time taken by a packet to go from source to destination. TORA has the lowest Wireless Delay in both scenarios. It introduces spikes in delay of packets as there is.

Fig-4 Wireless Delay-100 Nodes chance for short-lived and long- lived loops. GRP has less delay compare to TORA protocol. Beside the actual delivery of data packets, the delay time is also affected by route discovery 2. Network Load:

Fig-6 Network Load 100 Nodes 3. Throughput (bits/sec)

601

National Conference on Advanced Computing and Communication Technology Result shows that GRP produces the best results in delay and network load, and average results in network throughput. TORA produces worst results in the mesh network. By using quadrant and hierarchical quadrant to optimize routing flooding, routing flooding of GRP is less than that of TORA. VI References [1] Ian F. Akyildiz a, Xudong Wang b, and Weilin Wang b,"Wireless mesh networks: a survey." Compute Networks,2005.

[2] Yan Zhang, Jijun Luo, Honglin Hu, “Wireless Mesh Networking, Architectures Protocols and Standards”, Auerbach Publications

Fig-7 Throughput 40 nodes

[3] Anh-Ngoc Le Dong-Won Kum You-Ze Cho, “ Loadaware routing protocol for multi-radio Wireless Mesh Networks” , in Communications and Electronics, ICCEsecond International Conference, June 2008.

Fig. 6 and 7 shows the throughput for each protocol. Throughput is the main metric of any network. In 50 node scenario TORA produces the best results and GRP is near to TORA. It is clear that GRP has shown increased throughput regardless of the routing load observed during initial routing process. In 100 nodes scenario GRP produces the best results and TORA is near to GRP, it is vice-versa in both scenarios. TORA perform the worst case although it minimizes the control overhead generation by localizing the nodes. This is because unwanted overhead due to its “ Route Adaptation” feature.

[4]

Li Zhiyuan “Geographic routing Protocol and Simulation”2009 Second International Workshop on Computer Science and Engineering

[5] V.D. Park and M.S. Corson, “ TemporallyOrdered Routing Algorithm (TORA) Version 1” , IETS Internet draft (draft-ietfmanet-tora-spec04.txt), July 2001. [6] Elizabeth M. Royer, Santa Barbara, Chai-Keong, “A comprehensive overview about selected Ad Hoc Networking Routing Protocols” March 14, 2003. [7] Li Zhiyuan “ Geographic Routing Protocol and Simulation”, International Workshop on Computer Science and Engineering, IEEE 2009 [8] http://www.opnet.com [9] Aboelela Emad, Ph. D. “Chapter 15 – Mobile Wireless Networks: Network Simulation Experiments Manual” – 2nd Edition, Morgan Kufman Publishers, 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA. 2008 Elsevier Inc., ISBN: 978-0-12-373974-2

Fig-8 Throughput 100 Nodes V. Conclusion In this paper, performance of GRP and TORA was analysed using OPNET modelor 14.0. The protocols were tested using the same parameter.

[10]: E. Comer, Douglas; “Computer Networks and

602

National Conference on Advanced Computing and Communication Technology Internets: with Internet applications - 4th

Prentice Hall Upper Saddle River, N.J.; London,2004-013123627x

ed.”,

603

National Conference on Advanced Computing and Communication Technology | ACCT-10 |

“Performance Analysis of Mobile Ad-Hoc Network Protocols” Amit Verma1, Hari Singh2, Sukhvir Boora3 1

Student M.Tech (CSE) N.C College of Engineering, Israna (Panipat)

2

Asstt. Prof., Deptt. Of CSE, N.C College of Engineering, Israna (Panipat)

3

Associate. Prof., Deptt. Of CSE, N.C College of Engineering, Israna (Panipat)

.

Abstract:-

Mobile ad hoc networks are an emerging and popular technology to the world; however, the benefits of them are actually their fragility either. In scenarios of military operations and catastrophes even when there is no infrastructure available or left there is a need for communication. Due to the specific context the communication systems used in these tactical scenarios need to be as reliable as possible. Thus, the performance of these systems has to be evaluated. In mobile ad-hoc networks, nodes do not rely on any routing infrastructure but relay packets for each other. Thus communication in mobile ad-hoc networks functions properly only if the participating nodes cooperate in routing and forwarding. However, it may be advantageous for individual nodes not to cooperate, for example to save power or to launch security attacks such as denial-ofservice. In this paper, we give an overview of potential vulnerabilities and requirements of mobile ad-hoc networks, and of proposed prevention, detection and reaction mechanisms to thwart attacks.

As the communication systems used in these tactical or disaster area scenarios need to be as reliable as possible, the performance of these systems has to be evaluated. Field-tests in man oeuvres may be the preferred evaluation method. However, they are expensive, as sufficient hardware is needed. Furthermore, the results concerning some characteristics (e.g., scalability) are limited – who can perform Field-tests with several hundreds of devices? Thus, especially for the evaluation of algorithms and protocols, simulation is an alternative. Currently, there are two categories of wireless networks, namely, infrastructure-based wireless networks and mobile ad hoc networks. Only if the fixed configuration portion (infrastructure) has been set up properly, can mobile users exchange information and share the service of the network. To overcome the limitations of such kind of infrastructure, mobile ad hoc networks are presented for mobile users with more flexibility and freedom.

Keywords:- Performance analysis, Mobile Networks, Routing Protocol, 1. Introduction

As tactical networks may also be networks without infrastructure, the individual nodes and there movement characteristics need to be modeled. In this paper we will focus on models that realize the movement of individual nodes (microscopic models). In the literature there are already some surveys on mobility models. However, these surveys are

The goal of this paper is to study whether the advantages of cooperative (peer-to-peer) content distribution as seen in the Internet can carry over in ad hoc networks. To do this, we develop an application layer content distribution scheme and we study its performance extensively. 604

National Conference on Advanced Computing and Communication Technology | ACCT-10 |

exploiting incentive measures or trading confidential information; saving power by selfish behavior; preventing someone else from getting proper service, extracting data to get confidential information, and so on. Routes should be advertised and set up adhering to the routing protocol chosen and should truthfully reflect the knowledge of the topology of the network. By diverting the traffic towards or away from a node, incorrect forwarding, no forwarding at all, or other noncooperative behavior, nodes can attack the network.

quite old or miss a lot of specific models. Furthermore, there is no review concerning the requirements for tactical scenarios. Thus, in this paper we will give a survey on existing mobility models and classify and review these models concerning the requirements of tactical communication systems. 2. Cooperation and Security Issues in Mobile Ad-Hoc Network Mobile ad-hoc networks have properties that increase their vulnerability to attacks. Unreliable wireless links are vulnerable to jamming and by their inherent broadcast nature facilitate eavesdropping. Constraints in bandwidth, computing power, and battery power in mobile devices can lead to application-specific trade-offs between security and resource consumption of the device. Mobility/Dynamics make it hard to detect behavior anomalies such as advertising bogus routes, because routes in this environment change frequently. Selforganization is a key property of ad-hoc networks. They cannot rely on central authorities and infrastructures, e.g. for key management. Latency is inherently increased in wireless multi-hop networks, rendering message exchange for security more expensive. Multiple paths are likely to be available. This property offers an advantage over infrastructure-based local area networks that can be exploited by diversity coding.

3. Basic Idea of Proposed Solutions 3.1 Preventing Mechanism Authentication by ‘imprinting’. Stajano and Anderson authenticate users by ‘imprinting’ in analogy to ducklings acknowledging the first moving subject they see as their mother, but enable the devices to be imprinted several times. Imprinting is realized by accepting a symmetric encryption key from the first device that sends such a key. They neither address routing nor forwarding, however, are user authentication and authorization an important prerequisite for trust in the network layer also in mobile ad-hoc networks. Asynchronous threshold security has been employed by Zhou and Haas together with share refreshing for distributed certification authorities for key management in mobile adhoc networks. They take advantage of inherent redundancies in such networks due to multiple routes to enable diversity coding, allowing for Byzantine failures given by several corrupted nodes or collusions. This approach potentially is a strong prevention mechanism, however, to the best of our knowledge, the impact on the network and the security performance remain to be investigated.

Besides authentication, confidentiality, integrity, availability, access control, and no repudiation being harder to enforce because of the properties of mobile ad-hoc networks, there are also additional requirements such as location confidentiality, cooperation fairness and the absence of traffic diversion. The lack of infrastructure and of an organizational environment of mobile ad-hoc networks offers special opportunities to attackers. Without proper security, it is possible to gain various advantages by malicious behavior: better service than cooperating nodes, monetary benefits by

Incentives to cooperate have been proposed by Butty´an and Hubaux in the form of socalled nuglets that serve as a per-hop payment in every packet or in the form of counters to encourage forwarding. Both nuglets and 605

National Conference on Advanced Computing and Communication Technology | ACCT-10 |

and relies on symmetric cryptography only. It uses a key management protocol called TESLA that relies on synchronized clocks. Simulations have shown that the performance is close to DSR without optimizations. SEAD, Secure Efficient Distance vector routing for mobile ad-hoc networks by Hu, Johnson and Perrig is based on the design of destination-sequenced distance-vector routing (DSDV) and uses one-way hash functions to prevent uncoordinated attackers from creating incorrect routing state in another node. Performance evaluation has shown that SEAD outperforms DSDV-SQ in terms of packet delivery ratio, but SEAD adds overhead and latency to the network.

counters reside in a secure module in each node, are incremented when nodes forward for others and decremented when they send packets for themselves. One of their findings is that, given such a module, increased cooperation is beneficial not only for the entire network but also for individual nodes. Self-organized PGP by using chains of certificates has been developed by Hubaux, Butty´an and Capcun. Several certificate paths can be found by sharing information of nodes that each keep a small part of the certification knowledge, a prerequisite being the assumption that trust is transitive. Localized certification based on the public key infrastructure (PKI) with certification authority and secret-share update functionalities distributed among neighbors have been suggested by Kong, Zerfos, Luo, Lu and Zhang. For threshold secret-sharing and certification nodes need K one-hop neighbors within a given time window. The nodes locally store the system certification revocation list. A simulation showed a good success ratio and tolerable delay.

3.2 Reaction and Detection Intrusion detection for wireless ad-hoc networks has been proposed by Zhang and Lee to complement intrusion-prevention techniques. The authors argue that an architecture for intrusion detection should be distributed and cooperative, using statistical anomaly-detection approaches and integrating intrusion-detection information from several networking layers. They use a majority voting mechanism to classify behavior by consensus. Responses include re-authentication or isolation of compromised nodes. Detection rates and performance penalties remain to be investigated.

SRP, the Secure Routing Protocol by Papadimitratos and Haas, guarantees correct route discovery, so that fabricated, compromised, or replayed route replies are rejected or never reach the route requester. SRP assumes a security association between end-points of a path only, so intermediate nodes do not have to be trusted for the route discovery. This is achieved by requiring that the request along with a unique random query indentifier reach the destination, where a route reply is constructed and a message authentication code is computed over the path and returned to the source. The correctness of the protocol is proven analytically.

Watchdog and pathrater components to mitigate routing misbehavior have been proposed by Marti, Giuli, Lai and Baker. They observed increased throughput in mobile adhoc networks by complementing DSR with a watchdog for detection of denied packet forwarding and a pathrater for trust management and routing policy rating every path used, which enable nodes to avoid malicious nodes in their routes as a reaction. Although this reaction does not punish malicious nodes that do not cooperate and actually relieves them of the burden of forwarding for others while having their messages forwarded, it allows nodes to use

ARIADNE, a secure on-demand routing protocol by Hu, Perrig, and Johnson, prevents attackers from tampering with uncompromised routes consisting of uncompromised nodes. It is based on Dynamic Source Routing (DSR) 606

National Conference on Advanced Computing and Communication Technology | ACCT-10 |

incentives, group-membership and access control, authentication and identity persistence, and trust management.

better paths and thus to increase their throughput. CONFIDANT stands for ‘Cooperation Of Nodes, Fairness In Dynamic Ad-hoc Networks’ and it detects malicious nodes by means of observation or reports about several types of attacks and thus allows nodes to route around misbehaved nodes and to isolate them from the network. Nodes have a monitor for observations, reputation records for first-hand and trusted second-hand observations, trust records to control trust given to received warnings, and a path manager for nodes to adapt their behavior according to reputation. Simulations for “no forwarding” have shown that CONFIDANT can cope well even with half of the network population acting maliciously.

5. References [1]. Vasudevan, S., Kurose, J, Towsley, D. Design and Analysis of a Leader Election Algorithm for Mobile Ad Hoc Networks. Proceedings of the 12th IEEE International Conference on Network Protocols (ICNP) (2004) 350-360 [2] Y. Afek and A. Bremler. Self-stabilizing unidirectional network algorithms by power supply. Chicago Journal of Theoretical Computer Science, December 1998. [3] O. Bayazit, J. Lien, and N. Amato. Better group behaviors in complex environments using global roadmaps. 8th International Conference on the Simulation and Synthesis of living systems (Alife ‘02), Sydney, NSW, Australia, pp. 362370, December 2002.

CORE, a collaborative reputation mechanism proposed by Michiardi and Molva, also has a watchdog component; however it is complemented by a sophisticated reputation mechanism that differentiates between subjective reputation (observations), indirect reputation (positive reports by others), and functional reputation (task-specific behavior), which are weighted for a combined reputation value that is used to make decisions about cooperation or gradual isolation of a node. Reputation values are obtained by regarding nodes as requesters and providers, and comparing the expected result to the actually obtained result of a request. A performance analysis by simulation is stated for future work.

[4] B. DeCleene et al. Secure group communication for Wireless Networks. In proceedings of MILCOM 2001, VA, October 2001 [5] C. Perkins and E. Royer. Ad-hoc On-Demand Distance Vector Routing. In proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications, New Orleans, LA, February 1999,pp. 90-100 [6] W. Heinzelman, A. Chandrakasan and H. Balakrishnan. Energy-Efficient Communication Protocol for Wireless Micro sensor networks. In proceedings of Hawaiian International Conference on Systems Science, January 2000. [7] N. Malpani, J. Welch and N. Vaidya. Leader election Algorithms for Mobile Ad Hoc Networks. In fourth International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications, Boston, MA, August 2000.

4. Conclusion Mobile ad-hoc networks are vulnerable to attacks that differ from those in fixed networks; their properties pose additional requirements to security and cooperation protocols. There are many open research challenges, because by definition mobile adhoc networks are self-organized and have no infrastructure and central authorities. Examples for research questions are selforganized key management, cooperation

[8] N. Lynch. Distributed Algorithms. 1996, Morgan Kaufmann Publishers, Inc. [9] R. Gallager, P. Humblet and P. Humblet and P. Spira. A Distributed Algorithm for Minimum Weight Spanning Trees. In ACM Transactions on 607

National Conference on Advanced Computing and Communication Technology | ACCT-10 | Programming Languages and Systems, vol.4, no.1, pages 66-77, January 1983. [10] D. Peleg. Time Optimal Leader Election in General Networks . In journal of Parallel and Distributed Computing, vol.8, no.1, pages 96-99, January 1990.

608

National Conference on Advanced Computing and Communication Technology

Evolutionary Routing Protocol for Wireless Networks Rani Lecturer CSE Deptt. N.C.College of Engg., Israna [email protected] Mobile: 09813244888

Sitender Malik Assistant Professor CSE Deptt. N.C.College of Engg., Israna [email protected] Mobile: 09813244888

Abstract: Routing protocol problem in communication networks is a major issue to be solved. This paper presents a genetic algorithmic approach to solve this problem. Shortest path routing algorithms are well established problem and addressed by many researchers in different ways. The one of the alternate ways is evolutionary computation, which encompasses three main components- Evolution strategies, Genetic Algorithms and Evolution programs. Genetic Algorithms encode a potential solution to a specific problem on a simple chromosome like data structure and apply recombination operators to these structures so as to preserve critical information. Genetic algorithms maintain a pool of solutions that evolve in parallel over time. During each generation, genetic operators that allow randomized local changes and the exchange of information between solutions are applied to the solutions in the current pool in order to improve them. Genetic algorithms have the ability to escape local minima and communicate information among solutions. However, it is also known that GA-based routing algorithm is not fast enough for real-time computation. We intend to use this huge stochastic optimization tool for Optimum Path Routing Problem. GA may be used for optimization of searching process for optimum path routing in a network for optimization of both the distance and the congestion problem in a network. The proposed GA structure for the problem at hand is encoded in Matlab. Keywords: Genetic algorithms, optimum path routing in networks.

I. INTRODUCTION Routing in a computer network refers to the task of finding a path from a source node to a destination node. Given a particular network, it is very likely that there is more than one path that can be used. In a communication networks, such as the Internet and the Mobile Ad-hoc Networks, routing is a major issue that has a significant impact on the system performance. An ideal routing algorithm should strive to find an optimum path for packet transmission within a short time to satisfy the Quality of Service. We consider mobile ad hoc networks as target systems because they represent new generation wireless networks. Since all the nodes cooperatively maintain network connectivity without the aid of any fixed infrastructure networks, dynamic changes in network topology are possible. An optimum path is to computed that is referred to the minimization of the propagation distance and congestion level of the link. Genetic Algorithms are a family of computational models inspired by evolution. Genetic algorithms are often viewed as function optimizer, although the range of problems to which genetic algorithms have been applied is quite broad. An implementation of a genetic algorithm begins with a

Ravi Lecturer EE Deptt. Govt. Polytechnic, Sonepat [email protected] Mobile:09050905067

population of typically random chromosomes. One then evaluates these structures and allocates reproductive opportunities in such a way that those chromosomes which represent a better solution to the target problem are given more chances to “reproduce” than those chromosomes which are poorer solutions. The “goodness” of a solution is typically defined with respect to the current population. This particular description of a genetic algorithm is intentionally abstract because in some sense, the term genetic algorithm has two meanings. In a strict interpretation, the genetic algorithm refers to a model introduced and investigated by John Holland (1975), [3, 4] and By his student DeJong(1975), [10]. It is still the case that most of the existing theory for genetic algorithms applies either solely or primarily to the model introduced by Holland, as well as variations on what will be referred to in this paper as the canonical genetic algorithm. Recent theoretical advances in modeling genetic algorithms also apply primarily to the canonical genetic algorithm [11]. Genetic algorithms maintain a pool of solutions that evolve in parallel over time. During each generation, genetic operators that allow randomized local changes and the exchange of information between solutions are applied to the solutions in the current pool in order to improve them. The lowest quality solutions are then removed from the pool [1, 5]. Genetic algorithms have the ability to escape local minima and communicate information among solutions. A genetic algorithm with a solution pool containing only a single solution is equivalent to a greedy iterative improvement algorithm. Shortest path routing algorithms are well established, other alternative methods may have their own advantages[14]. One such alternative is to use a GA-based routing algorithm[14, 15]. We intend to use this huge stochastic optimization tool in Optimum Path Routing Problem.

609

II. GENETIC ALGORITHMS Genetic Algorithms (GA) are stochastic search algorithms that borrow some concepts from nature [1, 3, 4, 5,]. GA maintains a population pool of candidate solutions called strings or chromosomes. Each chromosome p is a collection of building blocks known as genes, which are instantiated with values from a finite domain. . At the start of the algorithm, an initial population is generated. Initial members of the population may be randomly generated, or generated according to some rules. The reproduction operator selects chromosomes from the population to be parents for a new chromosome and enters them into the mating pool. Selection

National Conference on Advanced Computing and Communication Technology of a chromosome for parenthood can range from a totally random process to one that is biased by the chromosome's fitness. The cross-over operator oversees the mating process of two chromosomes. Two parent chromosomes are selected from the mating pool randomly and the cross-over rate, which is a real number between zero and one, determines the probability of producing a new chromosome from the parents. If the mating was performed, a child chromosome is created which inherits complementing genetic material from its parents. The cross-over operator decides what genetic material from each parent is passed onto the child chromosome. The new chromosome produced is entered into the offspring pool. This new chromosome may represent an unexplored point in the search space. The mutation operator takes each chromosome in the offspring pool and randomly changes part of its genetic make-up, i.e. its content. The probability of mutation occurring on any chromosome is determined by the user specified mutation rate. Chromosomes mutated or otherwise, are put back into the offspring pool after the mutation process. Thus each new generation of chromosomes are formed by the action of genetic operators (reproduction, cross-over and mutation) on the older population. Finally, the members of the population pool are compared with those of the offspring pool. The chromosomes are compared via their fitness value to derive a new population, where the weaker chromosomes may be eliminated. In exact, weaker members in the population pool are replaced by the fitter child chromosomes from the offspring pool. The heuristic for assessing the survival of each chromosome into the next generation is called the replacement strategy. The process of reproduction, cross-over, mutation and formation of a new population completes one generation cycle. A GA is left to progress through generations, until certain criteria (such as a fixed number of generations, or a time limit) are met. GAs were initially used for machine learning systems, but it was soon realized that GAs have great potential in function optimization [1]. The steps in a GA coding are given below. A) INITIALIZATION: In any local search algorithm, the way candidate solutions of a problem are represented usually shapes the type of neighborhoods that the search will navigate in. Similarly for GA, the representation of the candidate solutions would affect the choice of cross-over and mutation operators we use in a GA. The representation used in each gene may be real coded or binary coded. The real coding is intended to be used in the problem undertaken in present work. B) SELECTION: This stochastic GA operator selects chromosomes form the current population to form a new population according to their fitness values. The chromosomes with higher fitness values have higher probability of contributing one or more offspring in the next generation. This operator mimics natural selection by Darwinian principal of survival of the fittest. The best chromosomes get more copies, the average stay even, and the worst die off. C) CROSSOVER: strings from the active pool (got after 'selection') could be mated randomly, but since the 'select' operator itself has selected strings from the old population

probabilistically, so the mate selection may not be random now and the crossover operator can directly pick the first pair of chromosomes. But whether to perform crossover over the current pair, the decision rests with probability of crossover pc (also called crossover rate and is a user supplied GA parameter). Crossover rate or probability of crossover is defined to be the probability that two parents will cross over in a single point. There can also be "multi-point crossover" versions of the Genetic Algorithms in which the crossover rate for a pair of parents is the number of points at which crossover takes place. D) MUTATION: Mutation is the occasional (with small probability) random alteration of the allele value of a string position.

III. OPTIMIZED PATH ROUTING PROBLEM GA-based routing algorithm has been found to be more scalable and insensitive to variations in network topologies. However, it is also known that GA-based routing algorithm is not fast enough for real-time computation [14]. Minimum spanning tree (MST) of a graph is an important concept in the communication network design and other network-related problem. Given a graph with cost (or weight) associated with each edge, the MST problem is to find a spanning tree of the graph with minimal total cost. When the graph’s edge costs are fixed and the search is unconstrained, the well-known algorithm of Krushal [12] and Prim [13] can identify MST in times that are polynomial in the number of nodes [15]. We intend to use this huge stochastic optimization tool Optimum Path Routing Problem. GA may be used for optimization of searching process for optimum path routing in a network for optimization of both the distance and the congestion problem in a network. Congestion problem in a network is not treated for in reference [14], which we intend to take care of in the proposed GA structure for the problem at hand. a) Algorithm for optimum path Routing: Real values for genes are used in the coding for this problem. The chromosome size is variable for each chromosome and each chromosome represents a probable route having some distance and total congestion in the path. Number of nodes is fixed in the network with each node having a congestion factor associated to it having value between 0 and 1; 0 represents a totally free node while a 1 represents a totally congested node. The following algorithm is used to encode the proposed GA for this problem: i) Select the nodes with their x and y coordinates and associated congestion factors. ii) Designate initial and final nodes. iii) Initialize the initial population having each chromosome, with its first gene as the starting node and last gene as the terminating node, so as each chromosome represents a probable path with varying number of nodes encountered in each path. iv) Evaluate the fitness of the population by the objective

610

National Conference on Advanced Computing and Communication Technology function, which calculates the distances between nodes from the starting node to terminating node and also sums up the congestion factors of all the nodes in the path. Objective function assigns fitness to each chromosome by way of calculating the total path distance and the total congestion factor of the path represented by the chromosome. v) Perform roulette Wheel selection on the population. vi) Perform crossover on the new population obtained after selection, with a probability of crossover 0.8, which may be increased or decreased for faster convergence of GA. vii) Perform Mutation with a probability of mutation between 0.001 to 0.003. Probability of mutation may be varied for faster convergence of GA. viii) Evaluate the fitness of the population by the objective function. ix) Check convergence of GA, stopping citation may average fitness or predefined number of runs for the GA. If stopping criterion is met, stop the GA else go to (v), Iterations continued till stopping criterion is met. x) Display the optimum path with coordinates and corresponding congestion factors of the nodes. The possible optimum path is one which is having minimum distance as well as the congestion factor is to be minimized through the path, a path with less congestion but having relatively larger distance may be selected as per the objective function, which takes care of both distance as well as the congestion in the path.

4.5 4 3.5 3 2.5 2 1.5 1 0.5 0

10 9

2

3

4

5

6

7

8

9

10

Fig. 2. Convergence curve for GA.

7

6

5

4

3

2

1

IV. RESULTS AND DISCUSSIONS The present application of GA is programmed in MATLAB. Variable length chromosomes with real coding are used. For the particular example taken here, a population size of 10 is taken. The algorithm explained in previous section is programmed for a network of total 56 nodes as shown in Fig. 1, each node assigned some congestion factor whose value is between ‘0’ and ‘1’. A ‘0’ congestion node means a totally congestion free node, while a ‘1’ congestion means a totally congested node. The present GA application takes care for the distance as well as the total congestion encountered in a path. Every chromosome has multiple genes corresponding to the number of nodes in the path represented by that node, and each gene in the chromosome is having three values(xcoordinate, y-coordinate and congestion factor).

1

0

1

2

3

4

5

6

7

8

9

10

Fig. 3. Optimized path

The test run of GA for the present example of network of 49 nodes given, the optimum path as shown in Fig. 3. The starting node is taken as S=[0.0,5.0,0.1]; and the ending node is E=[10.0,2.0,0.1]. The Algorithm selected a path which optimizes the distance between the starting and ending node alongwith minimizing the total congestion on the path. The optimum path found by GA in present example is S=[0.0,5.0,0.1]; [3.0,6.0,0.5]; [3.0,7.0,0.09]; [5.0,1.0,0.3]; [9.0,5.0,0.7]; E=[10.0,2.0,0.1]. The nodes are selected by the algorithm for optimizing the distance along with the congestion on the path. A shorter path with higher congestion may be neglected while longer path with lesser congestion may be selected.

8

V. CONCLUSIONS AND FUTURE DIRECTIONS

7 6 5 4 3 2 1 0

0

1

2

3

4

5

6

7

8

9

10

The GA is an optimization tool suitable for vast search spaces. We have developed this application of GA using Matlab. This application can be programmed for online environment, where congestion factors of the nodes may be changing with time according to the traffic conditions in the network. Other Intelligent tools may be probed in synergism with GA for online applications.

Fig. 1. Network consisting of 49 nodes

611

National Conference on Advanced Computing and Communication Technology [8]

REFERENCES [1]

[2]

[3] [4] [5] [6]

[7]

D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, 1989. A. Neubauer, “The circular schema theorem for genetic algorithms and two-point crossover,” in Proc. of Genetic Algorithms in Engineering Systems: Innovations and Applications, pp. 209–214, Sept. 1997. Holland J.H. (1975) Adaptation in Natural & Artificial Systems. Ann. Arbor: The Uni. of Michigan press. Holland J.H. (1962.), “Outline for a logical theory of adaptive systems” J. Assoc. Computer. Mach., vol.3. Pp.297-314. Michalewicz Z. (1992), Genetic Algorithms + Data Structures = Evolution Programs. Berlin: Springer – Verlag. Back T.(1992) “The interaction of mutation rate, selection & self adaptationin genetic algorithm,” in parallel problem solving from nature2. Manner R et.al., Eds., Amesterdam, The Neatherland: Elsevier. Back T. et.al, (1997) “Evolutionary computation: comments on the history & current state”, IEEE Transactions on Evolutionary Computations, vol 1, No. 1.

[9] [10]

[11] [12]

[13] [14]

[15]

612

De Jong K. A. (1975), “An analysis of the behavior of a class of genetic adaptive system”, Ph. D. Dissertation, Univ. of Michigan, Ann Arbor, Diss. Abstr. Int Davis L, Ed., (1996) Handbook of Genetic Algorithms, New York: Van Norstand Reinhold. De Jong K. A. (1992), “Are Genetic Algorithms Functions Optimizers?” in Parallel Problem Solving From Nature 2. Amsterdam, the Netherlands: Elsevier. Vose. M(1993), Modeling Simple Genetic Algorithms. Foundations of Genetic Algorithms -2- D. Whitley., ed., Morgan Kaufmann_ pp 63-73. J. B. Kruskal, “On the Shortes Spanning Tree of a Graph and the Traveling salesman Problem..” Amer. Math. Soc., vol. 7, pp. 48-50, 1956. R. Prim, “Shortest Connection Networks and Some Generalization.” Bell Syst. Tech. J., vol. 36, pp. 1389-1401,1957. Salman Yousof et.al. “A Parallel Genetic Algorithm for Shortest Path Routing problem.” 2009 International Conference on Future Computer and Communication. Lixia Hanr et.al., “A Novel Genetic Algorithm for Degree-Constrained Minimum Spanning Tree Problem” IJCSNS International Journal of Computer Science and Network Security, VOL.6 No.7A, July 2006.

National Conference on Advanced Computing and Communication Technology

Upcoming 4G and wireless network security aspect: A current wireless security issue Deepak1, Student M.Tech, Deptt. Of ECE, Vaish College of Engineering, Rohtak Ajit Singh2, Student M.Tech, Deptt. of CSE, The Technological Institute of Textile and Sciences, Bhiwani Vikas3, Student B.Tech, Deptt. of ECE, University Institute of Engineering and Technology, Rohtak Rajiv Sharma4, (Asstt. Prof. & H.O.D), Deptt. of ECE, Vaish College of Engineering, Rohtak Email: [email protected], [email protected], [email protected], 4

[email protected] system(1,2). Such a system does not yet exist, nor will it exist in today’s market without standardization. Fourth-generation wireless needs to be standardized throughout the United States due to its enticing advantages to both users and providers(3). Wireless Network Security Aspect: The original cellular phone network in the United States was called the Analog Mobile Phone System (AMPS). It was developed by AT&T and launched in 1983. AMPS operated in the 800 MHz range, from 824-849 MHz and 869-894 MHz. The base station in a particular cell kept a record of which voice subchannel pairs were in use. Though usable, this system included a number of security flaws. Because each phone transmitted (like any radio transmitter) in the clear on its own frequency, the phones in this system “were almost comically vulnerable to security attacks” (Riezenman 2000, 40). The crime of service theft plagued cellular service providers, as individuals with radio scanners could “sniff” the cellular frequencies and obtain the phone identification numbers necessary to “clone” a phone (4). The abuser could then use this cloned phone to make free telephone calls that would be charged to the legitimate user’s account. Unfortunately, punitive legislation was not enough to solve the problem; a new standard was needed. To create a new standard, engineers needed to start a new, examining each part of the current system.

Abstract: An introduction to upcoming 4G and its advantage over the existing technology, also the barriers in its launching with a hardware model are presented. This paper also comprises of wireless network security issue with information security objectives and threats in 4G. Keywords: 4G, wireless network security, comparison of 4G with earlier generaton, current security aspects Introduction: Introducing 4G technology: Consumers demand more from their technology. Whether it be a television, cellular phone, or refrigerator, the latest technology purchase must have new features. With the advent of the Internet, the most-wanted feature is better, faster access to information. Cellular subscribers pay extra on top of their basic bills for such features as instant messaging, stock quotes, and even Internet access right on their phones. But that is far from the limit of features; manufacturers entice customers to buy new phones with photo and even video capability. It is no longer a quantum leap to envision a time when access to all necessary information — the power of a personal computer — sits in the palm of one’s hand. To support such a powerful system, we need pervasive, highspeed wireless connectivity. A number of technologies currently exist to provide users with high-speed digital wireless connectivity; Bluetooth and 802.11 are examples. These two standards provide very highspeed network connections over short distances, typically in the tens of meters. Meanwhile, cellular providers seek to increase speed on their long-range wireless networks. The goal is the same: long-range, highspeed wireless, which for the purposes of this report will be called 4G, for fourth-generation wireless

Evolution of 4G: “Any sufficiently advanced technology is indistinguishable from magic.” Arthur C. Clarke First generation (1G) wireless telecommunications – the brick-like analog phones that are now collector’s items - introduced the cellular architecture that is still being offered by most wireless companies today. Second generation (2G) wireless supported more users within 613

National Conference on Advanced Computing and Communication Technology it wirelessly in a fully mobile environment. Let’s define “4G” as “wireless ad hoc peer-to-peer networking.” 4G technology is significant because users joining the network add mobile routers to the network infrastructure. Because users carry much of the network with them, network capacity and coverage is dynamically shifted to accommodate changing user patterns. As people congregate and create pockets of high demand, they also create additional routes for each other, thus enabling additional access to network capacity.(4) Users will automatically hop away from congested routes to less congested routes. This permits the network to dynamically and automatically selfbalance capacity, and increase network utilization. And there is also the 80/20 rule. With traditional wireless networks, about 80% of the cost is for site acquisition and installation, and just 20% is for the technology. Rising land and labor costs means installation costs tend to rise over time, subjecting the service providers’ business models to some challenging issues in the out years. With wireless peerto-peer networking, however, about 80% of the cost is the technology and only 20% is the installation. Because technology costs tend to decline over time, a current viable business model should only become more profitable over time. The devices will get cheaper, and service providers will reach economies of scale sooner because they will be able to pass on the infrastructure savings to consumers, which will further increase the rate of penetration.(1)

a cell by using digital technology, which allowed many callers to use the same multiplexed channel. But 2G was still primarily meant for voice communications, not data, except some very low datarate features, like short messaging service (SMS). Socalled 2.5G allowed carriers to increase data rates with a software upgrade at the base transceivers stations (BTS), as long as consumers purchased new phones too. Third generation (3G) wireless offers the promise of greater bandwidth, basically bigger data pipes to users, which will allow them to send and receive more information. All of these architectures, however, are still cellular. The cellular architecture is sometimes referred to as a “star architecture” or “star topology” or “spoke and hub,” because users within that cell access a common, centralized BTS. The advantage is that given enough time and money, carriers can build nationwide networks, which most of the big carriers have done. Some of the disadvantages include a singular point of failure, no load balancing, and spectral inefficiencies. The single biggest disadvantage to cellular networks going forward is that as data rates increase, output power will have to increase, or the size of the cells will have to decrease to support those higher data rates. Since significant increases in output power scare both consumers and regulators, it is far more likely that we will see significantly smaller cells. This will further reduce the return on investment in already fragile 3G business plans(3). Fourth generation (4G) wireless was originally conceived by the Defense Advanced Research Projects Agency (DARPA), the same organization that developed the wired Internet. It is not surprising, then, that DARPA chose the same distributed architecture for the wireless Internet that had proven so successful in the wired Internet. Although experts and policymakers have yet to agree on all the aspects of 4G wireless, two characteristics have emerged as all but certain components of 4G: end-to-end Internet Protocol (IP), and peer-to-peer networking. An all IP network makes sense because consumers will want to use the same data applications they are used to in wired networks. Peer-to-peer networks, where every device is both a transceiver and a router/repeater for other devices in the network, eliminates this spoke-and-hub weakness of cellular architectures, because the elimination of a single node does not disable the network. The final definition of “4G” will have to include something as simple as this: if a consumer can do it at home or in the office while wired to the Internet, that consumer must be able to do

4G Hardware: Ultra Wide Band Networks: Ultra Wideband technology, or UWB, is an advanced transmission technology that can be used in the implementation of a 4G network. The secret to UWB is that it is typically detected as noise. This highly specific kind of noise does not cause interference with current radio frequency devices, but can be decoded by another device that recognizes UWB and can reassemble it back into a signal. Since the signal is disguised as noise, it can use any part of the frequency spectrum, which means that it can use frequencies that are currently in use by other radio frequency devices (6 ). An Ultra Wideband device works by emitting a series of short, low powered electrical pulses that are not directed at one particular frequency but rather are spread across the entire spectrum (5). As seen in Figure 6, Ultra Wideband uses a frequency of between 3.1 to 10.6 GHz. The pulse can be called “shaped 614

National Conference on Advanced Computing and Communication Technology noise” because it is not flat, but curves across the spectrum. On the other hand, actual noise would look the same across a range of frequencies it has no shape. For this reason, regular noise that may have the same frequency as the pulse itself does not cancel out the pulse. Interference would have to spread across the spectrum uniformly to obscure the pulse. UWB provides greater bandwidth as much as 60 megabits per second, which is 6 times faster than today’s wireless networks.

Fig2: Adaptive array antenna Advantages of 4G: In a fourth-generation wireless system, cellular providers have the opportunity to offer data access to a wide variety of devices. The cellular network would become a data network on which cellular phones could operate — as well as any other data device. Sending data over the cell phone network is a lucrative business. In the information age, access to data is the “killer app” that drives the market. The most telling example is growth of the Internet over the last 10 years. Wireless networks provide a unique twist to this product: mobility. This concept is already beginning a revolution in wireless networking, with instant access to the Internet from anywhere. Barriers to Progress This begs the question: Why are cellular providers not moving to 4G instead of 3G? A marketplace like the cellular industry can be modeled as a game. There are three basic paths the game can take: Nobody makes the conversion to 4G:: All end up upgrading to 2.5G and 3G services. The upgrades are incremental, and don’t require a complete reworking of the system, so they are fairly cheap — the equipment required is already developed and in mass production in other places in the world. Everyone makes the conversion to 4G: The equipment and technology needed for 4G will be cheap, because of all of the cellular manufacturers investing in it. Cellular providers will market additional services to its customers. Some of the players make the conversion to 4G: Because not all of the players have chosen 4G, the equipment will be more expensive than the second scenario. Even though converters will be able to sell more services to their customers, it will not be enough to cover the higher costs of converting to 4G. Therefore, if a player chooses the 4G strategy, but nobody else follows suit, that player will be at a significant disadvantage. No cellular provider has incentive to move to 4G unless all providers move to 4G. An outside agent — the national government — must standardize on 4G as the

Fig1: Switched beam antenna It also uses significantly less power, since it transmits pulses instead of a continuous signal. UWB uses all frequencies from high to low, thereby passing through objects like the sea or layers of rock. Nevertheless, because of the weakness of the UWB signal, special antennas are needed to tune and aim the signal. Smart Antennas Multiple “smart antennas” can be employed to help find, tune, and turn up signal information. Since the antennas can both “listen” and “talk,” a smart antenna can send signals back in the same direction that they came from. This means that the antenna system cannot only hear many times louder, but can also respond more loudly and directly as well (7). There are two types of smart antennas: Switched Beam Antennas (as seen in Figure 1) have fixed beams of transmission, and can switch from one predefined beam to another when the user with the phone moves throughout the sector Adaptive Array Antennas (as seen in Figure 2) represent the most advanced smart antenna approach to date using a variety of new signal processing algorithms to locate and track the user, minimize interference, and maximize intended signal reception (7). Smart antennas can thereby: • Optimize available power • Increase base station range and coverage • Reuse available spectrum • Increase bandwidth • Lengthen battery life of wireless devices

615

National Conference on Advanced Computing and Communication Technology • To ensure that the resources and services provided to users are adequately protected against misuse or misappropriation. • To ensure that the security features are compatible with world-wide availability... • To ensure that the security features are adequately standardized to ensure world-wide interoperability and roaming between different providers. • To ensure that the level of protection afforded to users and providers of services is considered to be better than that provided in contemporary fixed and mobile networks... • To ensure that the implementation of security features and mechanisms can be extended and enhanced as required by new threats and services. • To ensure that security features enable new ‘ecommerce’ services and other advanced applications(9) These goals will help to direct security efforts, especially when the system is faced with specific threats.

wireless standard for the United States. Of course, legitimate concerns can be posed to the idea of implementing 4G nationwide.The lesson was learned during the use of the first generation of cellular phones in the United States: If a standard is to be set nationwide, it must be secure.(7,9)

Security Analysis Information Security Model: Before seeking to design and implement wireless security, however, one first needs to understand what this elusive concept of security really means. In this case, wireless security is really a combination of wireless channel security (security of the radio transmission) and network security (security of the wired network through which the data flows). These collectively can be referred to as “wireless network security” (8). But this still does not explain the security aspect. In a digital realm, security almost always means “information security.” Therefore, we can use the information security model proposed by the National Security Telecommunications and Information Systems Security Committee (NSTISSC), as seen in Figure 2: Along the top edge of the cube are the three states information can be in, while the rows on the left side of the cube are the information characteristics that the security policy should provide. The columns on the right side of the cube detail the three broad categories of security measures that can be pursued to protect the information. The cube is thus split into 27 smaller cubes, each of which must be examined for risks and solutions in any extensive security audit. This document, on the other hand, is not meant to contain such an audit, but rather to present the major issues of wireless security, the objectives of future wireless technology, and the security measures needed to reach those goals.

Conclusion: The above topics merely comprise a brief overview of some of the issues involved in wireless handheld device security. They by no means define a complete security solution for 4G wireless security. Rather, these topics serve as examples of some of the more prominent security problems that currently exist or may exist in future wireless systems(2) A more thorough security analysis is needed before a 4G wireless system can be implemented. As mobile handheld devices become more complex, new layers of technological abstraction will be added. Thus, while lower layers may be fairly secure, software at a higher layer may introduce vulnerabilities, or vice-versa. Future cellular wireless devices will be known for their software applications, which will provide innovative new features to the user. Unfortunately, these applications will likely introduce new security holes, leading to more attacks on the application level.The greatest risk comes from the application layer, either from faulty applications themselves or viruses downloaded from the network(6). Consumers demand that software and hardware be user-friendly and perform well. Indeed, it seems part of our culture that customers expect the highest quality and the greatest features from what they buy. The cellular telephone industry, which now includes a myriad of wireless devices, is no exception.

Objectives: The first step in analyzing cellular wireless security is to identify the security objectives. These are the goals that the security policy and corresponding technology should achieve. Howard, Walker, and Wright, of the British company Vodafone, created objectives for 3G wireless that are applicable to 4G as well: • To ensure that information generated by or relating to a user is adequately protected against misuse or misappropriation.

616

National Conference on Advanced Computing and Communication Technology Computing (2001): 172–177. 9.). Howard, P., M. Walker, and T. Wright. “Towards a coherent approach to third generation system security.” 3G Mobile Communication Technologies (2001): 21–27.

Meanwhile, competition in the industry is heating up. Providers are slashing prices while scrambling for the needed infrastructure to provide the latest features as incentives, often turning to various 3G solutions. Unfortunately, this will only serve to bewilder customers in an already confusing market. Customers want the features delivered to them, simple and straightforward. If the U.S. government wants to help, the best way to help all parties is to enforce 4G as the next wireless standard. The software that consumers desire is already in wide use. The transmission hardware to take it wireless is ready to go. And we have the security practices to make sure it all works safely. The government need only push in the right direction; the FCC need only standardize 4G in order to make the transition economically viable for all involved. This is need that demands a solution. Today’s wired society is going wireless, and it has a problem. 4G is the answer.

References: 1.) Cefriel. “4th Generation Networks (4G).” Cefriel. 6 April 2003. 2.) Wang, Jiangzhou. Broadband Wireless Communications: 3G, 4G and Wireless LAN. Boston: Kluwer Academic Publishers, 2001. 3.)Al-Muhtadi, J., D. Mickunas, and R. Campbell. “A lightweight reconfigurable security mechanism for 3G/4G mobile devices.” IEEE Wireless Communications 9.2 (2002):60–65. 4,) Riezenman, M.J. “Cellular security: better, but foes still lurk..” IEEE Spectrum 37.6 (2000): 39–42. 5.) Butcher, Mike. “UWB: widening the possibilities for wireless.” New Media Age. 5 April 2003. 6.) Cravotta, Nicholas. “Ultrawideband: the next wireless panacea?.” EDN.com. 5 April 2003. 7.).ArrayComm. “IEC: Smart Antenna Systems.” International Engineering Consortium(2003). 6 April 2003. 8.) Russell, S.F. “Wireless network security for users.” Information Technology: Coding and

`

617

National Conference on Advanced Computing and Communication Technology

Performance Analysis of IEEE 802.11, 802.15 AND 802.16 Standards Poonam1, Sanjeev Khambra2 M.Tech (CSE), 2Assistant Professor Department of Computer Science & Engineering Guru Jambheshwar University of Science & Technology, Hisar (Haryana) - India 1

[email protected], [email protected]

1

2

Abstract - The explosive growth of the Internet

802.11, IEEE 802.15 and IEEE 802.16 This technology has produced a number of affordable Wireless solutions that are growing in popularity with the organizations for sophisticated applications, where more mobility is required. Performance analysis of any system quantifies the service it delivers which help to justify the feasibility of its operation for intended purposes. In this paper, the analysis of IEEE 802.11, IEEE 802.15 and IEEE 802.16 are done using GLoMoSim. We have investigated the effect of node movement. The rest of the paper is organized as follow. Section 2 is an overview about the IEEE 802.11, 802.15 and 802.16. Section 3 includes the simulations as well as the results obtained. Finally, Chapter 4 has the general conclusion of the work.

over the last decade has led to an increasing demand for high-speed, ubiquitous Internet access. Broadband Wireless technologies are increasingly gaining popularity by the successful global deployment of the Wireless Personal Area Networks (Bluetooth- IEEE 802.15.1), Wireless Local Area Networks (WiFi- IEEE 802.11), and Wireless Metropolitan Area Networks (WiMAXIEEE 802.16). In this paper we using the GLoMoSim(Global Mobile Information System simulator) to analysis the performance of IEEE 802.11,802.15 and 802.16 Standards by varying mobility of the nodes. The performance metrics measured in this study inclusive of End to end delay, packet loss ratio and throughput. The results showed how mobility might affect the performance of IEEE 802.11, 802.15 and 802.16.

Keywords : IEEE 802.11, 802.15, 802.16, GL oM oSim, M obility

II. OVERVIEW I. INTRODUCTION

A. IEEE 802.11 In 1997, the Institute of Electrical and Electronic Engineers (IEEE) created the first WLAN standard. They called it 802.11 after the name of the group formed to oversee its development. WLAN required different management and physical features which was addressed in the OSI (Open System Interconnect) through changes in Data Link layers and PHY (Physical) layer. The upper networking layers are common for all the IEEE 802.x protocols, but the DLL(Data Link Layer) and PHY layers are different. Medium Access Control (MAC) sub layer of DLL determine how to access the medium and sent data by doing the required setup for PHY.PHY handles data transmission and reception among stations [2].

Increased use of mobile devices within the organization, and increase in worker mobility, has fuelled the demand for Wireless networks. Initially, the technology was slow, expensive and reserved for mobile situations or hostile environments, where cabling was impractical or impossible. With the maturing of industry standards and the deployment of lightweight Wireless devices alter the need of hardware software co-design to overcome the problems of present Wireless scenario. Wireless technology has come of age, which enables two or more computers to communicate using standard network protocols. Wireless networking does not require any fixed infrastructure and cabling [1]. This technology is propelled the emergence of cross-vendor industry standards such as IEEE

618

National Conference on Advanced Computing and Communication Technology In this paper we concentrate on 802.11b. The 802.11b standard is currently the most widely used one released in September 1999. It offers a maximum raw data rate of 11 Mbps (6 Mbps in practice) and a reach of up to 300 meters in an open environment. It uses the 2.4 GHz frequency range, with 3 radio channels available. The 802.11b use HRDSSS (High RateDirect Sequence Spread Spectrum) technique. An 802.11 LAN is subdivided into cells, with each cell referred to as a Basic Service Set (BSS). Each BSS is controlled by a base station which is referred to as an Access point (AP). Several APs can be connected to as an common backbone. When this occurs, the backbone is referred to as Distribution System (DS). The upper layers of the OSI Reference Model are referred to as an Extended Service Set (ESS)[3]. IEEE802.11b use the Carrier Sense Multiple Access and Collision Avoidance (CSMA/CA) for contention , Request-To-Send (RTS/CTS) mechanism to accommodate the hidden terminal problem ,and an optional mechanism called point coordination function (PCF) to support real time applications.

time maximum 7 are communicating Two piconets can be connected through a common Bluetooth devices (a gateway or bridge) to form a scatternet. These interconnected piconets within the scatternet form a backbone for the Mobile Area Network (MANET), and can enable devices which are not directly communicating with each other, or which are out of range of another device, to exchange data through several hops in the scatternet. The biggest Scatternet can link up to 10 piconets. Within a piconet the communication between master and slaves nodes is achieved using a TDD (Time Division Duplex) protocol. The master node (MN) is the administrators of overall network, beside MN assign the time slots. If the Piconet’s MN is turned off, the slave nodes negotiate who will be the new master node. Also this process is transparent to the users [6]. C. IEEE 802.16 802.16a, also known as WiMAX, is a wireless networking standard that offers greater range and bandwidth than the Wi-Fi family of standards, which includes 802.11a, 802.11b and 802.11g. While Wi-Fi is intended to provide coverage over relatively small areas, such as in offices or hot spots, WiMAX can transfer around 70M bit/sec over a distance of 30 miles (48 kilometers) to thousands of users from a single base station. Approved in January, 2003, 802.16a provides wireless, last-mile broadband access over the frequency bands below 11 GHz to connect homes, businesses and wireless LAN hot spots. 802.16a greatly improves non-line-ofsight performance, and it is the most appropriate technology available when obstacles such as trees and buildings are present. Stations can be mounted on homes or buildings rather than towers on mountains. With throughput up to 75M bit/sec, the wireless standard gives companies another way to get business-quality broadband service[7]. While it could take several months for a carrier to provision a T-1 line, service providers could provision wireless service in a matter of days. 802.16a provides flexibility not possible with wired services, such as high-speed backhaul for events such as trade shows, with hundreds or even thousands of 802.11 hot-spot users. On-demand connectivity also could benefit businesses such as construction companies that have sporadic or nomadic connectivity needs. The 802.16e extension to 802.16a introduces nomadic

B. IEEE 802.15 The IEEE 802.15.1 standard is the basis for the Bluetooth wireless communication technology. It is designed as a short range, low power connectivity solution for small peripheral, portable, and electronic devices like mobile phone, PDAs, printer, keyboard etc [4]. Bluetooth is a wireless telecommunications technology designed to eliminate cables in connections among electronic devices. Bluetooth most general characteristics are: short range (less than 100 meters), operates in star topology and transfer rate up to 3 Mbps in version 2.+EDR (Enhanced Data Rate), ratified in November 2004.Bluetooth use the unlicensed Industrial, Scientific, and Medical (ISM) band around the 2.4 GHz frequency range. With a transmitting power of 1 mW can reach a distance range up to 10 meters and with 100 mW up to 100 meters. Bluetooth allows instantaneous connection among devices found in the same operation area. Bluetooth protocols establish a session in a transparent way to the user. The devices found in this operation area form a network known as piconet. In Bluetooth system the basic unit is piconet[5]. A Bluetooth piconet consists of 1 master and 7 active slave device (all notes must be within 10 meter range. There can be 255 parked nodes in the single piconet but at any

619

National Conference on Advanced Computing and Communication Technology capabilities that let users connect while roaming outside their home service areas. The technology also offers privacy and Triple-DES encryption features to support secure transmissions and authentication. In a typical enterprise deployment, laptop and desktop computers are connected via wired Ethernet or 802.11 (Wi-Fi) access points located throughout the campus. An 802.16a directional antenna provides the connection from the business to a service provider's cell tower. Even if there is no line of sight between the antenna and the tower, signal still can be received after it reflects off buildings or other obstructions and reaches the tower indirectly[8]. At the base station, 802.16a technology correctly interprets the information even though reflections distort the radio frequency signal. Backhaul to the Internet is then provided via wireless 802.16 point-to-point links or by traditional wired backhaul such as DS3 and OCX. 802.16a technology also provides low latency for delay-sensitive services such as circuit-switched voice traffic or voice over IP, optimized transport for video, and prioritization of data traffic. This is especially important for businesses that want voice in addition to data services from their broadband service provider.

This section describes the scenario with all the network parameter which is used for simulation. Parameter

IEEE 802.11.b

IEEE 802.15.1

IEEE 802.16.a

Simulation time Terrain Dimensions Number of Nodes Traffic Model Node Placement

10M 1200, 1200 20

10M 1200,120 0 20

10M 1200,120 0 20

CBR Uniform

CBR Uniform

CBR Uniform

Mobility

0-30 (m/s)

0-30(m/s)

0-30(m/s)

MAC-Protocol

802.11

802.15

802.16

Routing Protocol Tx-Power Bandwidth Radio Frequency

DSR 15 6000000 2.4 e9

DSR 4 2000000 2.4 e9

DSR 43 12000000 2.5 e9

B. Result In this we will analyze the performance of IEEE 802.11,802.15 and 802.16 standards by changing the parameter node mobility. Then we will analyze the effect of Changing Mobility on the performance of these technologies. The three metrics that are used in the evaluation of this are the average end-to-end delay, throughput and packet-loss ratio.

III. SIMULATION AND RESULT ANALYSIS The purpose of our simulations is to analysis the performance of IEEE 802.11,802.15 and 802.16 .The simulations have been performed using the GloMoSim (Global Mobile information systems Simulator).This simulator models the OSI seven layer network architecture and includes models of IP routing and UDP. GloMoSim provides a scalable simulation environment for large wireless and wired communication networks[9]. GloMoSim uses a parallel discrete-event simulation capability provided by Parsec. Parsec is a C-based simulation language, developed by the Parallel Computing laboratory at UCLA, for sequential and parallel execution of discrete-event simulation models. It can also be used as a parallel programming language. GloMoSim simulates networks with up to thousand nodes linked by a heterogeneous communications capability that includes multicast, asymmetric communications using direct satellite broadcasts, multi-hop wireless communications using ad-hoc networking, and traditional Internet protocols.

1) Average end-to-end delay The delay is the total latency experienced by a packet to traverse the network from the source to the destination. At the network layer, the end-toend packet latency is the sum of processing delay, packetization, transmission delay, queuing delay, and propagation delay. The end-to-end delay of a path is the summation of the node delay at each node plus the link delay at each link on the path. In wireless link, the propagation delays are very small and almost equal for each hop on the path. The queuing delay and MAC delay are considered as two main factors that accumulated the node’s delay. The end-to-end delay increases as the node speed increases. Higher mobility causes more links broken and frequent re-routing and thus causes larger end-to-end delay. The end-to-end delay in IEEE 802.11.b and IEEE 802.15.1 is more then end-to-end delay in IEEE 802.16.a

A. Simulation scenarios

620

National Conference on Advanced Computing and Communication Technology 3) Packer-loss ratio 0.10

The loss ratio of packets is defined as the ratio between the lost packets and all the sending packets. The Fig.3 shows the effect of increasing node mobility on packet loss of IEEE 802.11.b, 802.15.1 and 802.16.a. It can be seen that increase in node speed results in increase in the packet loss in both the IEEE 802.15.1and IEEE 802.11.b due to more link breaks. The increase in packet loss for IEEE 802.15.1 and IEEE 802.11.b is more than that in IEEE 802.16.a

Average end-to-end delay

0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00 0-5

5-10

10-15

15-20

20-25

25-30

Mobility

1.00 0.90

IEEE 802.11.b

IEEE 802.16.a

0.80 Packet-loss ratio

IEEE 802.15.1

Figure1.

2) Throughput

IEEE 802.15.1

Throughput

15-20

20-25

25-30

IEEE 802.11.b

IEEE 802.16.a

IV. CONCLUSION The use of a particular Wireless Network depends upon factors like size of the network, load, mobility requirements etc. This paper has

presented a performance analysis of the IEEE 802.11.b, IEEE802.15.1 and IEEE 802.16.a standards using GloMoSim by varying mobility of the nodes. In summary, it can be said that for robust scenario where mobility is high, nodes are dense, area is large, the amount of traffic is more and network is for longer period, IEEE 802.16.a performs better among all studied wireless network. For the normal situations where a network is of general nature with moderate traffic and moderate mobility IEEE 802.11.b would be the right choice as it delivers more packets at the destination with lowest routing overheads. For low mobility and less number of nodes, IEEE 802.15.1is preferable. Results indicate that the performance of IEEE 802.16.a is the best among all compared wireless standard.

4000 2000 0 25-30

Mobility IEEE 802.11.b

10-15

Figure3.

6000

IEEE 802.15.1

5-10

Mobility

8000

20-25

0.30

0-5

10000

15-20

0.40

0.00

12000

10-15

0.50

0.10

14000

5-10

0.60

0.20

The amount of data transferred from one place to another or processed in a specified amount of time. Throughputs are measured in kbps, Mbps and Gbps. This data may be delivered over a physical or logical link, or pass through a certain network node. Throughput performance with respect to mobility is plotted in figure2. From the figure2, it is clear that as mobility increases the throughput decreases in case of IEEE 802.11b, due to less support of mobility the curve is going downward. The throughput of IEEE 802.15.1 is decrease with respect to mobility. The throughput of IEEE 802.16.a is constant because it easily support mobility rate up to fifty km per hour.

0-5

0.70

IEEE 802.16.a

Figure2.

621

National Conference on Advanced Computing and Communication Technology V. REFERENCE

[1] Sanjeev Dhawan, “Analogy of Promising Wireless bTechnologies on Different Frequencies: Bluetooth, WiFi, and WiMAX”, Proc. In The 2nd International Conference on wireless Broadband and Ultra Wideband Communication ,AusWireless, pp.14-22,27-30Aug,2007. [2]Abu Nasser M.Abdullah,Haja Moinudeen,Wajdi AlKhateeb, “Scalability and Performance Analysis of IEEE 802.11a”, pp.1626-1629,CCECE/CCGEI,Saskatoon,May 2005. [3]Antonis Athanasopoulos, Evangelos Topalis, Christos Antonopoulos, Stavros Koubias, “Evaluation Analysis of the Performance of IEEE 802.11b and IEEE 802.11g Standards”, Proc. of the International Conference on Networking, International Conference on System and International Conference on Mobile Communications and Learning Technologies,Washington,DC,USA,pp.141-146,2329April,2006. [4] Youquan Zheng and Zhenming Feng, “Simplifications of the Bluetooth Radio Devices”,Proc. IEEE 4th International Workshop on Networked Appliances, Gaithersburg, MD, USA, pp.107-115,2002 [5]Juan Ivan Nieto Hipolito, Norma Candolfi Arballo, Jose Antonio Michel-Macarty, Elitania Jimienez Garcia, “Bluetooth Performance Analysis in Wireless Personal Area Networks”, IEEE conference on Electronics,Robotics and Automotive Mechanics,, Cuernavaca, Morelos, Mexico, pp.38-43,22-25September,2009. [6] Abdelshakour Abuzneid, sarosh Patel, Viqar U.Mohammed, Varun Kumar Godula, “Multiplexing Overlays on Bluetooth”,Novel Algorithm and Techniques in Telecommunications, Automation and Industrial Electronics,Netherlands,pp.375-383,Friday,August 15,2008. [7] Dr.Huzur Saran, Ashish Sangwan, Shashwat sehgal, “Performance Analysis of the IEEE standard 802.16”. [8] WiMAX.Retrieve:http://en.wikipedia.org/wiki/WiMax [9]xiang Zeng, Rajive Bagrodia, Mario Gerla, “GloMoSim: A Library for Parallel Simulation of Large-scale Wireless Networks”.

622

National Conference on Advanced Computing and Communication Technology

619

Design & Implementation of MANET Using MAC with Different Slot Time Keshav Jindal, M.Tech (CS) student, PDM College of Engg. Bhadurgarh. [email protected] (9253445223) Mohd Shahid, A.P in IT Deptt, PDM COLLEGE OF ENGINEERING (Bahadurgarh) [email protected] Baharat Bhushan , A.P in CSE Deptt, PDM COLLEGE OF ENGINEERING (Bahadurgarh) [email protected]

Abstract— IEEE 802.11 MAC protocol has been the standard for Wireless LANs, and also adopted in many network simulation packages for wireless multi-hop ad hoc networks.MAC is defined to proper access to the channel and that is also responsible for throughput and fairness in the network. IEEE 802.11 MAC protocol has been the standard for Wireless LANs, and also adopted in many network simulation packages for wireless multi-hop ad hoc networks. While IEEE 802.3 MAC Protocol had been standardized for Wired LAN. In this paper we worked on assessment and evaluation of legacy MAC & wireless MAC aiming at College Campus Area. We simulated using ns2 and concluded an adaptive performance model best suited for College Campus Area for networking in terms of throughput and delay. We created performance measurement model of Wireless local area network for large no. of mobile nodes that take part, move and communicate one another in a WLAN i.e. in a typical scenario of a college Classroom or College’s conference hall where each person is equipped with a Lap Top and simulate our models taking varying time slot from 30 to 20, 15 & 10 micro sec. for getting optimum key point for such WLANs. Keywords: ad hoc network, wired, wireless LAN, mobility, radio frequency,

I. INTRODUCTION The needs of accessing information while moving around make mobile technologies very demanding and preferred by a lot of users. In fact, when we talk about mobility, the closest term that comes to our mind is “Wireless Network” which is any network system that provides users with both mobility and flexibility in accessing information. Because of the needs for mobile communication, wireless network has become very popular. Unlike the wired Local Area Network, IEEE 802.11, one of the most popular WLAN does not require a physical connection from the client to be connected to the network because the data is transmitted and received over the air [1]. In ad hoc networks, communications are done over wireless media between stations directly in a peer to peer fashion

Without the help of wired base stations or access points. The nodes are self-organizing, autonomous and mobile and act as hosts, routers, transmitters, receivers or intermediate hops The scope of MANETs is tremendous; it is one of the emerging fields, which will prove to be very useful in the near future [2]. However, there are many problems encountered in the upper protocol layers in IEEE 802.11 wireless networks. The packet delay greatly increases when there are serious collisions due to the heavy traffic. Packets may be dropped either by the buffer overflow or by the MAC layer contentions. Besides in infrastructure network in wireless Technologies there is access points where there Security is main Concern. By default, a wireless network access point is open to anyone within in range with the proper equipment and if the router or access point is configured to distribute IP addresses via DHCP (Dynamic host configuration protocol), anyone equipped with a wireless enabled laptop or PDA can use that one freely. Older wireless routers/access points have two basic security methods: MAC address filtering and Wired Equivalent Privacy (WEP). Both MAC and WEP offer only very basic security, and the risks are associated with them. Even Newer versions of wireless routers/access points make use of 2 additional security methods. The first is the Wireless Application Protocol (WAP), of which there are several variations. A router/access point may also support the Remote Authentication Dial In User Service (RADIUS), a protocol that works in conjunction with Network Operating Systems such as Windows, UNIX or Linux servers and is used for larger networks. But yet a lot of security measures are required to be done. In this paper we study the characteristics & performance of Mac Layer with regard to IEEE 802.11 MAC protocol and 802.3 MAC protocol [3-4] from the point of view of COLLEGE AREA NETWORK. That means our Area includes no hilly region or such where lying of fiber optic cable is altogether unrealistic. On the basis of that we concluded that if we ignore the one time heavy investment in setting up fiber optic wired network at University Campus, we on the one hand would be able to solve the problem of security which are

619

620

National Conference on Advanced Computing and Communication Technology inherent in the wireless scenario and would get also higher throughput, fair delay and less packet losses as we already discussed the problems of TCP which was actually meant for wired network. To Support our vision we conducted our simulation using ns2. We simulated & evaluated MAC that means old legacy and new Protocol with TCP and checked throughput, fairness & Performance restricting to the environment of College Campus Area. We created Performance model for noting down better throughput and less delay if one chose to select WLAN for CAN.

1 and 2 Mbit/s) : • Frequency Hopping Spread Spectrum in the 2.4 GHz Band • Direct Sequence Spread Spectrum in the 2.4 GHz Band, • InfraRed 802.11 802.11 MAC

Frequency Hopping

2. RELATED WORK Many Papers have been published relating to performance of wireless Lan based on Mac Protocol In which probability distribution of the MAC layer packet service time (i.e., the time interval between the time instant a packet starts for transmission and the time instant that the packet either is acknowledged for correct reception by the intended receiver or is dropped) has been characterized[2]. Different types of traffic such as video, voice and data has been taken into account that means performance evaluation DCF vs. EDCF has been done[6]. Paper on QoS that is IEEE 802.11e has also been published by different authors.[2,6 7]. From the network perspective, QoS refers to the service quality or service level that the network offers to applications or users in terms of network QoS parameters, including: latency or delay of packets traveling across the network, reliability of packet transmission, and throughput. From the application/user perspective QoS generally refers to the application quality as perceived by the user. That is, the presentation quality of the video, the responsiveness of interactive voice, and the sound quality of streaming audio. However improved Performance of wireless LAN has been thought and simulated from by improving the MAC from old legacy to IEEE 802.11e but to the best of our knowledge and belief no one thought to create a Model particularly for University Campus Area or Area which comes in between the Wired Local Area Network and Wide Area Network. So we Create Performance Model for Campus Area Network based on MAC Protocol, we change slot time[1] to see the optimum point where the model performance would be the best in terms of throughput and delay. 3. Background MAC (Media Access Control) The 802.11 family uses a MAC layer known as CSMA/CA (Carrier Sense Multiple Access/Collision Avoidance) while Classic Ethernet uses CSMA/CD - collision detection). CSMA/CA is, like all Ethernet protocols, peer-to-peer (there is no requirement for a master station). As any 802.x protocol, the 802.11 protocol covers the MAC and Physical Layer, the Standard currently defines a single MAC which interacts with three PHYs (all of them running at

DATA LINK LAYAER

Direct Sequence Spread Spectrum

Infra Red

PHYSICAL LAYER

The MAC Layer defines two different access methods, 1. Distributed Coordination Function and 2. Point Coordination Function The Basic Access Method: CSMA/CA The basic access mechanism, called Distributed Coordination Function, is basically a Carrier Sense Multiple Access with Collision Avoidance mechanism (usually known as CSMA/CA). CSMA protocols are well known in the industry, where the most popular is the Ethernet, which is a CSMA/CD protocol (CD standing for Collision Detection). A CSMA protocol works as follows: A Wireless node that wants to transmit performs the following sequence: 1. Listen on the desired channel. 2. If channel is idle (no active transmitters) it sends a packet. 3. If channel is busy (an active transmitter) node waits until transmission stops then a further CONTENTION period. (The Contention period is a random period after every transmit on every node and statistically allows every node equal access to the media. To allow tx to rx turn around the contention time is slotted 50 micro sec for FH and 20 micro sec for DS systems). If the channel is still idle at the end of the CONTENTION period the node transmits its packet otherwise it repeats the process defined in 3 above until it gets a free channel. These kind of protocols are very effective when the medium is not heavily loaded, since it allows stations to transmit with minimum delay, but there is always a chance of stations transmitting at the same time (collision), caused by the fact that the stations sensed the medium free and decided to transmit at once. These collision situations must be identified, so the MAC layer can retransmit the packet by itself and not by upper layers, which would cause significant delay. In the Ethernet case this collision is recognized by the transmitting stations which go to a retransmission phase based on an exponential random backoff algorithm. While these Collision Detection mechanisms are a good idea on a wired LAN, they cannot be used on a Wireless LAN

620

National Conference on Advanced Computing and Communication Technology environment, because of two main reasons: 1. Implementing a Collision Detection Mechanism would require the implementation of a Full Duplex radio, capable of transmitting and receiving at once, an approach that would increase the price significantly. 2. On a Wireless environment we cannot assume that all stations hear each other (which is the basic assumption of the Collision Detection scheme), and the fact that a station willing to transmit and senses the medium free, doesn’t necessarily mean that the medium is free around the receiver area in order to overcome these problems, the 802.11 uses a Collision Avoidance mechanism together with a Positive Acknowledge scheme, as follows: A station willing to transmit senses the medium, if the medium is busy then it defers. If the medium is free for a specified time (called DIFS, Distributed Inter Frame Space, in the standard) then the station is allowed to transmit, the receiving station will check the CRC of the received packet and send an acknowledgment packet (ACK). Receipt of the acknowledgment will indicate the transmitter that no collision occurred. If the sender does not receive the acknowledgment then it will retransmit the fragment until it gets acknowledged or thrown away after a given number of retransmissions. Slot time is the time it takes for a packet to travel the maximum theoretical distance between two nodes in a network. Collision detection protocols always wait for a minimum of slot time before transmitting; allowing any packet that was being sent over the channel at the same time to which(channel) the waiting node requested to send, to reach the waiting node.If the slot time were less it would mean that the nodes waiting to send a packet would wait for a small time before transmission. If the slot time were set to a large value, it would mean that they would have to wait for a longer period of time. From this we can conclude that smaller slot time would mean more collisions and longer slot time would mean lesser collisions. Setting the slot time to an optimum value is important. While we would not want to set it to a value too small, we would also not want to set it to a value bigger than necessary. That would mean that the nodes would have to wait for an unnecessarily long period of time[1]. Time slots are divided into multiple frames, and there are several types of InterFrame Spacing (IFS) slots. In increasing order of length, they are the Short IFS (SIFS), Point Coordination Function IFS (PIFS), DCF IFS (DIFS), and Extended IFS (EIFS). The node waits for the medium to be free for a combination of these different times before it actually transmits. Different types of packets can require the medium to be free for a different number or type of IFS. For instance, in ad hoc mode, if the medium is free after a node has waited for DIFS, it can transmit a queued packet. Otherwise, if the medium is still busy, a backoff timer is initiated. The initial backoff “10” value of the timer is chosen randomly from between 0 and CW-1, where CW is the width of the contention window, in terms of time slots. After an unsuccessful transmission attempt, another backoff is performed with a doubled size of CW as

621

decided by a Binary Exponential Backoff (BEB) algorithm. Each time the medium is idle after DIFS, the timer is decremented. When the timer expires, the packet is transmitted. After each successful transmission, another random backoff (known as “postbackoff”) is performed by the transmission-completing node. A control packet such as RTS, CTS, or ACK is transmitted after the medium has been free for SIFS. 4. Simulation Results We create the model using mac protocol IEEE 802.3 and IEEE 802.11 in peer to peer fashion and concluded that throughput of old legacy mac is always more far than the new mac Protcol (figure1)

Figure 1: Throughput of old legacy MAC and New MAC in peer to peer fashion Then we create another model now this time no. of nodes are increased, we varied the packet size. The Simulation Time, the no. of nodes, the packet size, traffic type were the same for both ieee 802.3 old legacy mac and ieee 802.11 new mac. and got the same result that old legacy mac gives rise to throughput if compared to new mac. Now we create a performance Model in which different parameters were taken as follows: We create a separate file for movement of nodes. The different parameters chosen were as follows : No. of nodes: 100, pause time : 2.00 sec., moving max speed: 10.00 m/s, Topology boundary max x: 500.00, max y: 500.00 and initial position were as follows $node_(0) set X_ 130.438757275991 $node_(0) set Y_ 139.623985169872 $node_(0) set Z_ 0.000000000000 $node_(1) set X_ 428.221660566075 $node_(1) set Y_ 7.964065916959 $node_(1) set Z_ 0.000000000000 … $node_(99) set X_ 353.582387567762 $node_(99) set Y_ 124.185311452147 $node_(99) set Z_ 0.000000000000

621

622

National Conference on Advanced Computing and Communication Technology $ns_ at 2.000000000000 "$node_(0) setdest 349.538592902019 119.186864535061 0.051098892146" … $ns_ at 2.000000000000 "$node_(99) setdest 208.687573649691 175.900926135339 3.203277244764" For Communication we choose the following parameters : nodes: 100, max conn: 40, send rate: 0.37593984962406013, seed: 1.0 e.g. # 1 connecting to 2 at time 2.5568388786897245 set udp_(0) [new Agent/UDP] $ns_ attach-agent $node_(1) $udp_(0) set null_(0) [new Agent/Null] $ns_ attach-agent $node_(2) $null_(0) set cbr_(0) [new Application/Traffic/CBR] $cbr_(0) set packetSize_ 512 $cbr_(0) set interval_ 0.37593984962406013 $cbr_(0) set random_ 1 $cbr_(0) set maxpkts_ 10000 $cbr_(0) attach-agent $udp_(0) $ns_ connect $udp_(0) $null_(0) $ns_ at 2.5568388786897245 "$cbr_(0) start" … # 44 connecting to 45 at time 141.0795085137149 set udp_(39) [new Agent/UDP] $ns_ attach-agent $node_(44) $udp_(39) set null_(39) [new Agent/Null] $ns_ attach-agent $node_(45) $null_(39) set cbr_(39) [new Application/Traffic/CBR] $cbr_(39) set packetSize_ 512 $cbr_(39) set interval_ 0.37593984962406013 $cbr_(39) set random_ 1 $cbr_(39) set maxpkts_ 10000 $cbr_(39) attach-agent $udp_(39) $ns_ connect $udp_(39) $null_(39) $ns_ at 141.0795085137149 "$cbr_(39) start" Total sources/connections: 25/40

Micro sec.

average(delay),

10 15 20 30

0.176715 0.18272 0.175573 0.165745

Figure 2: Scattered node within a CAN We simulate our performance model by varying slot time from 20 micro sec. to 15, 12 & 10. we got the following delay.

Figure 3: Average end to end delay We conclude that the delay at 30 micro sec is lowest but this conclusion is not of any use until and unless we compare our result with average throughput. That is comparing result with delay only & taking varied slot time for performance is not enough. The following table shows the combine result of throughput and average delay with varied slot time.

622

Slot time Micro sec

Average (delay)

Average (throughput)

Result Per 1000

30

1657.45

3644.1112

2198.62515

20

1755.73

4510.5842

2569.06486

15

1827.2

5112.4264

2797.95665

10

1767.15

5071.0332

2869.61109

National Conference on Advanced Computing and Communication Technology

623

Mac provides equal access of channels to all types of traffic. Besides there are other problems of 802.11 Mac protocol such as packet delay and packet drops when traffic goes up resulting in poorly utilization of n/w capacity. So IEEE 802.11e may also be evaluated and examined comprising with IEEE 802.11 in near future.

References: [1]

M Mahbubur Rahman “Tunable Protocol for Mobile Ad Hoc Network” Daffodil International University”, Bangladesh, 2007.

Figure 4: Throughputs vs end to end delays

[2]

The result shows that average delay at 15 micro sec. is highest and throughput is highest. & average delay at 30 micro sec. is lowest and throughput is also lowest. That means slot time can be considered altogether at that time. At 20 & 10 Micro sec. there is close competition where the difference of both delay and throughput is medium. The lowest delay in our result is at 30 micro sec. but throughput is not highest. It has low throughput than throughput at 20 as well as at 10 micro sec. The Highest throughput is at 15 micro sec. It seems that the optimum point is at either 20 or 10 micro sec. But when we compare the result by taking the delay equal to all in per thousand, the picture becomes clear and we get the optimum point which is 10 micro sec. for our performance model.

[3]

Zhai, H. Fang. Y, “Performance of Wireless LANs based on IEEE 802.11 MAC Protocol” Emanuel Puschita, Tudor Palade, L.Chira “Performance Evaluation of DCF vs EDCF data link layer access Mechanism for WLAN ” Puthal “Quality of Service Provisioning with modified IEEE 802.11 MAC Protocol” NIT, Rourkela,2008. TommiLarsson “A Study of EDCA and DCF in Multihop Ad-Hoc Networks” Master Thesis, CTU,2008. Jianping Li “Time Slot Assignment for Maximum Bandwidth in a Mobile Ad-Hoc Network” International Symposium on Wireless, USA, 2007. Mohd Izhar & Amit Prkash Singh “Effectively implementing CSMA/CA (IEEE 802.11) Protocol”, 2009. Sam De Silva, “Using TCP Effectively in Mobile Adhoc Wireless Networks with Rate Adaptation” Report, 2007. IsmahansiBinti Ismail, “Study of Enhanced DCF(EDCF) in Multimedia Application.”Master thesis. Malaysia, 2005. Guillermo Alonso Pequeno Javier Rocha Rivera, “Extension to MAC 802.11 for performance improvement in MANET”. Master Thesis,2007. Pasupuleti “Throughput and Delay Evaluation of a Proposed-DCF MAC Protocol for WLAN” IIIT, Bangalore. P. Ellich “Fingerprinting 802.11Devices”California. Mohd Izuan, MohdSaad “Performance Analysis of Random-Based Mobility Models in MANET Routing Protocol “Journal of Scientific Research,2009. Page(s):444-454. Changhua He “Security Analysis and Improvements for IEEE 802.11i” Stanford. IEEE std. 802.11, 802.11a, 802.11b-1999, Part 11 Wireless LAN MAC and PHY Layer Specification.

5. Conclusion and Future work Through this paper we aimed to know the performance of Mac Protocol in three different aspects keeping in mind the three different version of Mac Protocols, standardized and specified by the IEEE. Firstly we evaluated and examined the IEEE 802.3 MAC Protocol. Secondly we took for examination IEEE 802.11 MAC Protocol standardized for wireless LAN. We Compared it with it legacy one. IEEE 802.11e has been kept in third Category, a lot of work on which has been done. IEEE 802.11 deals with the Quality of Service. We conducted simulation and Conclude that IEEE 802.3 Mac Protocol can be effective than 802.11 but limited to our Campus Area Network. The reason is clearly drawn theoretically that wired nodes which are taking parts in the network are stationeries. The network is therefore static in nature. While wireless nodes are mobile moving as well as stationery and the topography of wireless network keep on changing that means they are dynamic in nature. That is why throughput of wired network is always good than the wireless one. Also there are other points of consideration which mac 802.11 more effective than old legacy. To make the Mac Protocol more effective, IEEE standardizes 802.11e on November 2003. Which differentiate traffic such voice, video and data The Voice, video are delay sensitive while data is understood delay tolerant while 802.11 623

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12] [13]

[14] [15]

National Conference on Advanced Computing and Communication Technology

Intruder Safe Routing using Alternate path in ad hoc networks Sumedha Student, M.Tech (CSE) Vaish College of Engineering, Rohtak, Haryana, India [email protected]

Mr. Deepak Gupta Asstt. Professor, IT Department Vaish College of Engineering, Rohtak, Haryana, India [email protected] of each other are called neighbors. Neighbors can send directly to each other. However, when a node needs to send data to another nonneigh boring node, the data is routed through a sequence of multiple hops, with intermediate nodes acting as routers.

Abstract A mobile ad hoc network (MANET) is a collection of autonomous nodes or terminals which communicate with each other by forming a multihop radio network without the aid of any established infrastructure or centralized administration such as a base station. The ad-hoc network provides lack of secure boundaries. The Central point of intruder attack is the algorithmic way of implementation by the user to transfer data over the network. In a simple ad-hoc network, we generally use the shortest Path algorithm to transfer data between nodes .But this transmission gives the no. of problems: (1) Intruder attack (2) Centralized Load .The consequences are less security and less efficiency. We are working to search an Intruder Safe path that is not shortest but closer to the shortest. Keywords: MANET , Security, Alternate Path

I. INTRODUCTION A mobile ad hoc network (MANET) sometimes called a mobile mesh network is a wireless network, comprised of mobile computing devices (nodes) that use wireless transmission for communication and do not rely on any central coordinator. Mobile nodes that are within each other’s radio range communicate directly via wireless links, while those far apart rely on other nodes to relay messages as routers. In MANETs communication between nodes is done through the wireless medium. Because nodes are mobile and may join or leave the network , MANETs have a dynamic topology. Nodes that are in transmission range

Ad hoc networks are subject to various kinds of attacks. Wireless communication links can be eavesdropped on without noticeable effort and communication protocols on all layers are vulnerable to specific attacks. In contrast to wire-line networks, known attacks like masquerading, man-in-the-middle, and

624

National Conference on Advanced Computing and Communication Technology •

replaying of messages can easily be carried out. A central issue concerning the design of any service in ad hoc networks is not to rely on any centralized entities, because such entities would obviously be easy to attack, and their reachability could not be guaranteed at all times for all participants of the network. Therefore, it is not possible to implement a centralized, trusted entity for managing public keys of the participants as performed in local area networks or the Internet. Instead, a distributed solution must be found.

“Route request” packet contains a route record yielding the sequence of hops taken. • “Route reply” is generated when the request reaches the either the destination or an intermediate node that has a valid route to the destination; route records is placed in the reply message. Route maintenance. When a node detects a broken link while trying to forward a packet to the next hop, it sends a route error (RERR) message back to the source containing the link in error. When an RERR message is received, all routes containing the link in error are deleted at that node.

1.1 UNIPATH ROUTING IN MANET In unipath routing, only a single route is used between a source and destination node. The Protocols which are used in routing are helpful in finding and maintaining routes between source and destination nodes. Two types of classes of ad hoc routing protocols are table-based and on-demand protocols: (a) Table Based Protocols: Each node maintains a routing table containing routes to all nodes in the network. Nodes must periodically exchange messages with routing information to keep routing tables up-to-date. Therefore, routes between nodes are computed and stored, even when they are not needed. (b) On Demand Protocols: Nodes only compute routes when they are needed. Ondemand protocols consist of the following two main phases: 1. Route discovery is the process of finding a route between two nodes. 2. Route maintenance is the process of repairing a broken route or finding a new route in the presence of a route failure. Two of the most widely used protocols are the Dynamic Source Routing (DSR) and the Ad hoc On-demand Distance Vector (AODV) protocols. AODV and DSR are both ondemand protocols. Dynamic Source Routing. DSR is an ondemand routing protocol for ad hoc networks. Based on source routing, Designed for the scenario where traffic flows from few source nodes to few destination nodes. Route discovery. It checks whether it has the valid route entry in its cache. If no ,broadcasts a route request packet. Each intermediate node checks whether it knows of a route to the destination. If NO, adds its own address to the route record of the route packet and forwards the packet along its outgoing links.

Ad Hoc on Demand Distance vector. AODV is an on-demand routing protocol for ad hoc networks. AODV uses hop-by-hop routing by maintaining routing table entries at intermediate nodes. Route Discovery. The route discovery process is initiated when a source needs a route to a destination and it does not have a route in its routing table. To initiate route discovery, the source floods the network with a RREQ packet specifying the destination for which the route is requested. When a node receives an RREQ packet, it checks to see whether it is the destination or whether it has a route to the destination. If either case is true, the node generates an RREP packet, which is sent back to the source along the reverse path. When the source node receives the first RREP, it can begin sending data to the destination. Route Maintenance. When a node detects a broken link while attempting to forward a packet to the next hop, it generates a RERR packet that is sent to all sources using the broken link. The RERR packet erases all routes using the link along the way. If a source receives a RERR packet and a route to the destination is still required, it initiates a new route discovery process. 1.2 MULTIPATH ROUTING IN MANETs Standard routing protocols in ad hoc wireless networks, such as AODV and DSR, are mainly intended to discover a single route between a source and destination node. Multipath routing consists of finding multiple routes between a source and destination node. 1.2.1 Route Discovery and Maintenance. Route discovery and route maintenance consists of finding multiple routes between a source and destination node. Multipath routing protocols can attempt to find node disjoint,

625

National Conference on Advanced Computing and Communication Technology link disjoint, or non-disjoint routes. Node disjoint routes, also known as totally disjoint routes, have no nodes or links in common. Link disjoint routes have no links in common, but may have nodes in common. Non-disjoint routes can have nodes and links in common. From a fault tolerance perspective, more reliable paths should be selected to reduce the chance of routes failures. Path selection also plays an important role for QoS routing. In QoS routing, only a subset of paths that together satisfies the QoS requirement is selected. 1.2.2 SPLIT MULTIPATH ROUTING. Split Multipath Routing (SMR) proposed is an on-demand multipath source routing protocol. SMR is similar to DSR, and is used to construct maximally disjoint paths. Unlike DSR, intermediate nodes do not keep a route cache, and therefore, do not reply to RREQs. This is to allow the destination to receive all the routes so that it can select the maximally disjoint paths. Maximally disjoint paths have as few links or nodes in common as possible. Duplicate RREQs are not necessarily discarded.

III.

OBJECTIVES

Due to insecure nature of the wireless link, adhoc networks require a security oriented approach. Also any node can join or leave the network at any time. This is security breach as the joining node can be a malicious node and can have unwilling effects on the network performance. So it is very important to authenticate the joining nodes. That’s why we do the following: 1) To develop an algorithm to prevent from the intruder attack while transferring the data. 2) To design a path that is safe from the intruder attack i.e. we are designing a highly efficient algorithm. 3) To find an alternative route through which the packets can be routed to control the congestion 4) To detect malicious types of attacks in the ad hoc environment and then apply suitable algorithm specific for that purpose. IV. METHODS USED •

II. SECURITY ISSUES IN AD HOC As the data is transmitted over the adhoc there is no centralized manager for the adhoc network, because of this the chances of Intruder attach increase. The Attack can be in case of Unipath routing or in multipath, Even the topology is dynamic still it has many flaws in terms of security. The Intruder attack is on the algorithmic approach of data transfer. Some of the common attacks on security are:• Attacks using modification- False Sequence number Malicious nodes can cause redirection of network traffic and DoS attacks by altering control message fields. In AODV, any node may divert traffic through itself by advertising a route to a node with a desti_sequence_num greater than the authentic value. • Attacks using modification – False hop counts. AODV uses the hop count field to determine a shortest path Malicious nodes can set hop count to zero. DSR uses source routes in data packets DoS attack can be launched in DSR by altering the source routes in the packet headers • Attacks using modification Tunneling A tunneling attack is where two or more nodes may collaborate to encapsulate messages between them.

•

Language requirements: The .net platform is used for developing an algorithm. Architectural requirements: In the routing mechanism used the route taken to receiving node is shortest path and malicious resides in the route. The scheme is as that each node must find alternate path to the destination node which is not the shortest path. Start

Find shortest path from i to n

Represent the node as a block node.

Find alternate path that does not involve Blocked node Transfer data from alternate path End

626

National Conference on Advanced Computing and Communication Technology • •

1. Finding the shortest route to the destination 2. Finding the alternative that does not involve any of the nodes that lies on the way to the shortest path. After that getting the acknowledgement from that path about the ongoing communication

V. SECURE ALGORITHM

ALTERNATE

The complete Simulation is done in .net Environment. Table shows the various simulation result. The results for the various topologies are being summarized in the table. The results are shown up to 20 nodes with each result having the different topology and different source and destination

PATH DISTANCE GRAPH

Path A to B (A, n. a, b) /* A is the Adjacency matrix representation of given network, n is the no of nodes and a, b are two nodes between we have to transfer data*/ { Step 1:- Give the range of the network node and set all other elements that are outside the range to 0. Step 2:- Find the Neighbour of Each node of network starting from node a to node b. Step 3:- Find the shortest path from source to destination and store it in an array called array[ ]. Step 4:- Search the neighbour list and pick a random node from the list and put that node in the array. Step 5:- compare the random node with all the elements of the shortest path array. If the array [top] element matches with any of the elements in the list then make the entry corresponding to that node in neighbour array. Step 6:- Compare the neighbour list of the generated node with all the elements of array otherwise, Pick a random node from the list and put it in the array }

Time Graph

VI. RESULTS An example where shortest path exist showing the shortest path and its distance, alternate path its distance and time to find the source and destination for shortest and alternate paths There are maximum 20 numbers of nodes Shortest Dist. path 602(3,7,1 ) 973(7,3) 72(6,15,2 ) 718(3,2) 425(7,16, 15,6)

Time in sec

1st Alternate path dist.

2nd Alt. path dist.

749(3,14,2,1)

1st Alt. time 175

775(3,8,1)

2nd Alt. time 236

834 68 616

1296(7,12,3) 167(6,16,2)

856 570

1498(7,10,3) 248(6,7,14,2)

686 115

232 950

804(3,16,4,2) 606(7,17,3,6)

75 436

987(3,14,2) 711(7,11,6)

232 637

627

VII. CONCLUSION Importance of MANET cannot be denied as the world of computing is getting portable and compact. Unlike wired networks, MANET pose a number of challenges to security solutions due to their unpredictable topology,

National Conference on Advanced Computing and Communication Technology wireless shared medium, heterogeneous resources and stringent resource constraints etc. The Security research area is still open as many of the provided solutions are designed keeping a limited size scenario and limited kind of attacks and vulnerabilities. We are providing the solution for the problems where we can save the adhoc network from the active attack of Intruders that are on the basis of algorithmic implementations. Generally the path selected for data transfer in adhoc network is the shortest path because of this intruder attack is also in same area. We have generated such a path in which no node from the shortest path will be included. It will give a secure and efficient approach of data transmission in adhoc network in uni cast.

AODV for Ad Hoc Networks”, Proceedings of International Symposium on Intelligent Signal Processing and Communication System, 2007 pp. 435-438 [7] Mohammad Ilyas, “The Hand Book of Ad Hoc Wireless Network”, CRC Press LLC [8] S.-B. Lee, Y.-H. Choi, ARMS: An authenticated routing message in sensor networks, Secure Mobile Ad-hoc Networks and Sensors Workshop (MADNES’05), Lecture Notes in Computer Science, Springer, September 2005 [9] E. Shi, A. Perrig, Designing secure sensor networks, wireless communications, IEEE 11 (6) (2004) 38–43 [10] S. Zhu, S. Setia, S. Jajodia , LEAP: efficient security mechanisms for large-scale distributed sensor networks, ACM Conference on Computer and Communications Security (CCS’03), October 2003 [11] A.D. Wood, J.A. Stankovic, Denial of service in sensor networks, IEEE Computer 35 (10) (2002) 54–62. [12] A. Perrig, R. Szewczyk, V. Wen, D. Culler, J.D. Tygar, SPINS: Security Protocols for Sensor Networks, International Conference on Mobile Computing and Networking (MobiCom 2001), 2001, pp. 189–199.

VIII. ACKNOWLEDGEMENT I would like to acknowledge and extend my heartfelt gratitude to Mr. Deepak Gupta, Assistant Professor (Department of computer Science), Vaish College of Engg. Rohtak, for his vital encouragement and support. All Computer Science Department faculty members and staff. Most especially to my family and to God, who made all things possible.

REFERENCES [1] L. Zhou and Z. J. Haas, “Securing ad hoc networks,” IEEE Network, vol. 13, no. 6, pp. . 24-30.1999 [2] The idea of utilizing threshold cryptography to distribute trust in ad hoc network was [proposed by L. Zhou and Z. J. Haas. Securing ad hoc networks. IEEE network Magazine.13 (6):2430.November/December 2007 [3] J.Kong, P.Zerfos. Providing robust and ubiquitous security support for mobile ad hoc networks. In proceedings of the 9th International Conference on Networks Protocols, December [4] D. Remondo , “Tutorial of Wireless Ad Hoc Networks”, HET-NETs 2004 [5] P. Papadimitratos, Z. Haas, “Secure Routing for Mobile Ad Hoc Network”, In Proceedings of the SCS Communication Networks and Distributed Systems Modelling and Simulation Conference (CNDS), 2002 pp. 1-13 [6] Liu Jinghua, Geng Peng, Qiu Yingqiang , Feng Gui, “A Secure Routing Mechanism in

628

National Conference on Advanced Computing and Communication Technology

Wireless Sensor Networks and Security Challenges Abhishek Yadav1, Devender Singh Mann2, Devesh Kushwaha2 1. ECE Deptt. , VIT , Meerut ([email protected]) 2. Student, ECE Final Year, IIMT Engineering College, Meerut

Abstract—Wireless Sensor networks (WSN) is an emerging technology and have great potential to be employed in critical situations like battlefields and commercial applications such as building, traffic surveillance, habitat monitoring and smart homes and many more scenarios. One of the major challenges wireless sensor networks face today is security. While the deployment of sensor nodes in an unattended environment makes the networks vulnerable to a variety of potential attacks, the inherent power and memory limitations of sensor nodes makes conventional security solutions unfeasible. The sensing technology combined with processing power and wireless communication makes it profitable for being exploited in great quantity in future. The wireless communication technology also acquires various types of security threats. This paper discusses a wide variety of attacks in WSN and their classification mechanisms and different securities available to handle them including the challenges faced.

• Home Healthcare: In such applications, privacy protection is essential. Only authorized users should be able to query and monitor the network. This paper includes has been organized in following sections. Section 2 gives the information about the security goals in Wireless Sensor Networks. Security attacks and their classification are discussed in section 3. Section 4 discusses about the various security mechanisms. Major challenges faced are given in Section 5 followed by the conclusion section. II. SECURITY GOALS FOR SENSOR NETWORKS As the sensor networks can also operate in an adhoc manner the security goals cover both those of the traditional networks and goals suited to the unique constraints of adhoc sensor networks. The security goals are classified as primary and secondary [5]. The primary goals are known as standard security goals such as Confidentiality, Integrity, Authentication and Availability (CIAA). The secondary goals are Data Freshness, SelfOrganization, Time Synchronization and Secure Localization.

Keywords-Wireless Sensor Network; Security Goal; Security Attacks; Defensive mechanisms; Challenges I. INTRODUCTION Basically, sensor networks are application dependent. Popular wireless sensor network applications include wildlife monitoring, bushfire response, military command, intelligent communications, industrial quality control, observation of critical infrastructures, smart buildings, distributed robotics, traffic monitoring, examining human heart rates etc. Majority of the sensor network are deployed in hostile environments with active intelligent opposition. Hence security is a crucial issue. Less obvious but just as important security dependent applications include:

III. ATTACKS ON SENSOR NETWORKS Wireless Sensor networks are vulnerable to security attacks due to the broadcast nature of the transmission medium. Furthermore, wireless sensor networks have an additional vulnerability because nodes are often placed in a hostile or dangerous environment where they are not physically protected. Basically attacks are classified as active attacks and passive attacks. [4] Figure1 shows the classification of attacks under general categories and Figure 2 shows the attacks classification on WSN.

• Disasters: In many disaster scenarios, especially those induced by terrorist activities, it may be necessary to protect the location of casualties from unauthorized disclosure • Public Safety: In applications where chemical, biological or other environmental threats are monitored, it is vital that the availability of the network is never threatened. Attacks causing false alarms may lead to panic responses or even worse total disregard for the signals.

A. Passive Attacks The monitoring and listening of the communication channel by unauthorized attackers are known as passive attack. The Attacks against privacy is passive in nature. 1) Attacks against Privacy

629

National Conference on Advanced Computing and Communication Technology The unauthorized attackers monitors, listens to and modifies the data stream in the communication channel are known as active attack. The following attacks are active in nature. Routing Attacks in Sensor Networks, Denial of Service Attacks, Node Subversion, Node Malfunction, Node Outage, Physical Attacks, Message Corruption, False Node, Node Replication Attacks, Passive Information Gathering etc.

The main privacy problem is not that sensor networks enable the collection of information. Rather, sensor networks intensify the privacy problem because they make large volumes of information easily available through remote access. Hence, adversaries need not be physically present to maintain surveillance. They can gather information at low-risk in anonymous manner. Some of the more common attacks [8] against sensor privacy are: • Monitor and Eavesdropping: When the traffic conveys the control information about the sensor network configuration, which contains potentially more detailed information than accessible through the location server, the eavesdropping can act effectively against the privacy protection.

IV. SECURITY MECHANISM The security mechanisms are actually used to detect, prevent and recover from the security attacks. A wide variety of security schemes can be invented to counter malicious attacks and these can be categorized as high level and low-level. Figure 3 shows the order of security mechanisms.

• Traffic Analysis: Even when the messages transferred are encrypted, it still leaves a high possibility analysis of the communication patterns.

A. Low-Level Mechanism Low-level security primitives for securing sensor networks includes, Key establishment and trust setup, Secrecy and authentication, Privacy Robustness to communication denial of service, Secure routing, Resilience to node capture etc.

• Camouflage Adversaries: One can insert their node or compromise the nodes to hide in the sensor network. After that these nodes can copy as a normal node to attract the packets, then misroute the packets, conducting the privacy analysis. B. Active Attacks

Figure 1. General Classification of Security Attacks

630

National Conference on Advanced Computing and Communication Technology

Figure 2. Classification of Security Attacks on WSN

Figure3: Security mechanisms Like other traditional networks, the sensor networks have also force privacy concerns. Initially the sensor networks are deployed for legitimate purpose might subsequently be used in unanticipated ways. Providing awareness of the presence of sensor nodes and data acquisition is particularly important. [1]

2) Secrecy and authentication. Most of the sensor network applications require protection against eavesdropping, injection, and modification of packets. Cryptography is the standard defense. Remarkable system trade-offs arise when incorporating cryptography into sensor networks. For point-to-point communication[12], end-to-end cryptography achieves a high level of security but requires that keys be set up among all end points and be incompatible with passive participation and local broadcast. [6]

4) Robustness to communication denial of service An adversary attempts to disrupt the network’s operation by broadcasting a high-energy signal. If the transmission is powerful enough, the entire system’s communication could be jammed. More sophisticated attacks are also possible; the

3) Privacy

631

National Conference on Advanced Computing and Communication Technology 3) Secure data aggregation One advantage of a wireless sensor network is the fine grain sensing that large and dense sets of nodes can provide. The sensed values must be aggregated to avoid overwhelming amounts of traffic back to the base station. For example, the system may average the temperature of a geographic region, combine sensor values to compute the location and velocity of a moving object, or aggregate data to avoid false alarms in real-world event detection. Depending on the architecture of the wireless sensor network, aggregation may take place in many places in the network. All aggregation locations must be secured. [6]

adversary might inhibit communication by violating the 802.11 medium access control (MAC) protocol by, say, transmitting while a neighbor is also transmitting or by continuously requesting channel access with a request-to send signal.[1] 5) Secure routing Routing and data forwarding is a crucial service for enabling communication in sensor networks. Unfortunately, current routing protocols suffer from many security vulnerabilities. For example, an attacker might launch denial of-service attacks on the routing protocol, preventing communication.[2] 6) Resilience to node capture One of the most challenging issues in sensor networks is resiliency against node capture attacks. In most applications, sensor nodes are likely to be placed in locations easily accessible to attackers. Such exposure raises the possibility that an attacker might capture sensor nodes, extract cryptographic secrets, modify their programming, or replace them with malicious nodes under the control of the attacker. [1]

V. CHALLENGES OF SENSOR NETWORKS A wireless sensor network is a special network which has many constraint compared to a traditional computer network. A. Wireless Medium The wireless medium is inherently less secure because its broadcast nature makes eavesdropping simple. Any transmission can easily be intercepted, altered, or replayed by an adversary. [7]

B. High-Level Mechanism High-level security mechanisms for securing sensor networks, includes secure group management, intrusion detection, and secure data aggregation.

B. Ad-Hoc Deployment The ad-hoc nature of sensor networks means no structure can be statically defined. The network topology is always subject to changes due to node failure, addition, or mobility. Nodes may be deployed by airdrop, so nothing is known of the topology prior to deployment. Since nodes may fail or be replaced the network must support selfconfiguration. Security schemes must be able to operate within this dynamic environment.[3]

1) Secure group management Each and every node in a wireless sensor network is limited in its computing and communication capabilities. However, interesting in-network data aggregation and analysis can be performed by groups of nodes. For example, a group of nodes might be responsible for jointly tracking a vehicle through the network. Consequently, secure protocols for group management are required, securely admitting new group members and supporting secure group communication. The outcome of the group key computation is normally transmitted to a base station. The output must be authenticated to ensure it comes from a valid group. [1]

C. Hostile Environment The next challenging factor is the hostile environment in which sensor nodes function. Since nodes may be in a hostile environment, attackers can easily gain physical access to the devices. D. Resource Scarcity The extreme resource limitations of sensor devices pose considerable challenges to resourcehungry security mechanisms. [5]

2) Intrusion detection Wireless sensor networks are susceptible to many forms of intrusion. Wireless sensor networks require a solution that is fully distributed and inexpensive in terms of communication, energy, and memory requirements. The use of secure groups may be a promising approach for decentralized intrusion detection.[1]

E. Immense Scale Simply networking tens to hundreds of thousands of nodes has proven to be a substantial task. Providing security over such a network is equally challenging. Security mechanisms must be scalable to very large networks while maintaining high computation and communication efficiency.

632

National Conference on Advanced Computing and Communication Technology summarizes the attacks and their classifications in wireless sensor networks and also an attempt has been made to explore the security mechanism widely used to handle those attacks. The challenges of Wireless Sensor Networks are also briefly discussed. This survey will hopefully motivate future researchers to come up with smarter and more robust security mechanisms and make their network safer.

F. Unreliable Communication Certainly, unreliable communication is another threat to sensor security. The security of the network relies heavily on a defined protocol, which in turn depends on communication.[5] • Unreliable Transfer: Normally the packet-based routing of the sensor network is connectionless and thus inherently unreliable. • Conflicts: Even if the channel is reliable, the communication may still be unreliable. This is due to the broadcast nature of the wireless sensor network. • Latency: The multi-hop routing, network congestion and node processing can lead to greater latency in the network, thus making it difficult to achieve synchronization among sensor nodes.

REFERENCES [1] Adrian Perrig, John Stankovic, David Wagner, “Security in Wireless Sensor Networks” Communications of the ACM, Page53-57, year 2004 [2] Al-Sakib Khan Pathan, Hyung-Woo Lee, Choong Seon Hong, “Security in Wireless Sensor Networks: Issues and Challenges”, International conference on Advanced Computing Technologies, Page1043-1045, year 2006 [3] Chris Karlof, David Wagner, “Secure Routing in Wireless Sensor Networks: Attacks and Countermeasures”, AdHoc Networks (elsevier), Page: 299-302, year 2003 [4] Ian F. Akykildiz, Weilian Su, Yogesh Sankarasubramaniam, and Erdal Cayirci, “A Survey on Sensor Networks”, IEEE Communication Magazine, year 2002 [5] John Paul Walters, Zhengqiang Liang, Weisong Shi, Vipin Chaudhary, “Wireless Sensor Network Security: A Survey”, Security in Distributed, Grid and Pervasive Computing Yang Xiao (Eds), Page3-5, 10-15, year 2006 [6] Pathan, A.S.K.; Hyung-Woo Lee; Choong Seon Hong, “Security in wireless sensor networks: issues and challenges” Advanced Communication Technology (ICACT), Page(s):6, year 2006 [7] Tahir Naeem, Kok-Keong Loo, Common Security Issues and Challenges in Wireless Sensor Networks and IEEE 802.11 Wireless Mesh Networks, International Journal of Digital Content Technology and its Applications, Page 89-90 Volume 3, Number 1, year 2009 [8] Undercoffer, J., Avancha, S., Joshi, A. and Pinkston, J. “Security for sensor networks”. In Proceedings of the CADIP Research Symposium, University of Maryland, Baltimore County, USA, year 2002 http://www.cs.sfu.ca/~angiez/personal/paper/sensor-ids.pdf [9] Zia, T.; Zomaya, A., “Security Issues in Wireless Sensor Networks”, Systems and Networks Communications (ICSNC) Page(s):40 – 40, year 2006 [10] Xiangqian Chen, Kia Makki, Kang Yen, and Niki Pissinou, Sensor Network Security: A Survey, IEEE Communications Surveys & Tutorials, vol. 11, no. 2,page(s): 52-62, year 2009 [11] Culler, D. E and Hong, W., “Wireless Sensor Networks”, Communication of the ACM, Vol. 47, No. 6, June 2004, pp. 30-33. [12] D. Djenouri, L. Khelladi, and N. Badache, “A Survey of Security Issues in Mobile ad hoc and Sensor Networks,” IEEE Commun. Surveys Tutorials, vol. 7, pp. 2–28, year 2005.

G. Unattended Operation Depending on the function of the particular sensor network, the sensor nodes may be left unattended for long periods of time. There are three main cautions to unattended sensor nodes [5]: • Exposure to Physical Attacks: The sensor may be deployed in an environment open to adversaries, bad weather, and so on. The probability that a sensor suffers a physical attack in such an environment is therefore much higher than the typical PCs, which is located in a secure place and mainly faces attacks from a network. • Managed Remotely: Remote management of a sensor network makes it virtually impossible to detect physical tampering and physical maintenance issues. • No Central Management Point: A sensor network should be a distributed network without a central management point. This will increase the vitality of the sensor network. However, if designed incorrectly, it will make the network organization difficult, inefficient, and fragile. Perhaps most importantly, the longer that a sensor is left unattended the more likely that an adversary has compromised the node.

BIOGRAPHIES Abhishek Yadav has received B.E. and M.E in Electronics and Communication discipline. He has more about 10 years of experience. He has served in public sector also. He is the author of about one dozen books also. He has published more then 25 apers in national and international conferences. Email: [email protected]

VI. CONCLUSION The deployment of sensor nodes in an unattended environment makes the networks vulnerable. Wireless sensor networks are increasingly being used in military, environmental, health and commercial applications. Sensor networks are inherently different from traditional wired networks as well as wireless ad-hoc networks. Security is an important feature for the deployment of Wireless Sensor Networks. This paper

Devender Singh Mann is final year student of engineering (ECE) at IIMT Engineering College, Meerut. His area of interest includes microprocessor, Communication and Artificial intelligence.

633

National Conference on Advanced Computing and Communication Technology Devesh Kushwaha is final year student of engineering (ECE) at IIMT Engineering College, Meerut. His area of interest includes microprocessor, Communication, Switching theory and Artificial intelligence.

634

National Conference on Advanced Computing and Communication Technology

635

EVALUATE THE PERFORMANCE OF DIFFERENT ROUTING PROTOCOL IN MANET Rajiv Munjal, M.Tech (CS) student, PDM College of Engg. Bhadurgarh. Rahul Yadav, A.P in CSE Deptt, PDM COLLEGE OF ENGINEERING (Bahadurgarh) Mohd Shahid, A.P in IT Deptt, PDM COLLEGE OF ENGINEERING (Bahadurgarh)

Abstract— My project entitle is – “Evaluate the performance of different routing protocol in MANET”. Mobile Ad-hoc networks are characterized by a lack of infrastructure, and by a random and quickly changing network topology; thus the need for a robust dynamic routing protocol that can accommodate such an environment. Consequently, many routing algorithms have come in to existence to satisfy the needs of communications in such networks. This paper presents a performance comparison between two categories of routing protocols, table-driven (Proactive) and on-demand (Reactive) routing protocols, this two categories were shown by using two different routing protocols, first is the DSDV (Destination Sequenced Distance-Vector) from the Proactive family and the second is the AODV (Ad Hoc OnDemand Distance Vector) from the Reactive family. Both protocols were simulated by using NS-2[2] package. Both routing protocols were compared in terms of average throughput, packets delivery ratio and average delay, while same number of nodes and by using the Trace file. Although DSDV perfectly scales to small networks with low node speeds, AODV is preferred due to its more efficient use of bandwidth. Keywords: Mobile Ad-Hoc, Routing, DSDV, AODV, Performance, Comparisons, NS-2, Average Throughput. Average delay, Packet loss ratio

I. INTRODUCTION Mobile Ad Hoc Network (MANET) is a collection of communication devices or nodes that communicate without any fixed infrastructure and pre-determined organization of available links. The nodes in MANET themselves are responsible for dynamically discovering other nodes to communicate. MANET is a self-configuring network of mobile nodes connected by wireless links the union of which forms an arbitrary topology. The nodes are free to move randomly and organize themselves arbitrarily; thus, the network's wireless topology may change rapidly and changeably. MANETs are usually set up in situations of emergency for temporary operations or simply if there are no resources to set up elaborate networks. These types of networks operate in the absence of any fixed infrastructure, which makes them easy to deploy, at the same time however, due to the absence of any fixed infrastructure, it becomes difficult to make use of the

existing routing techniques for network services, and this poses a number of challenges in ensuring the security of the communication, something that is not easily done as many of the demands of network security conflict with the demands of mobile networks, mainly due to the nature of the mobile devices (e.g. low power consumption, low processing load). A mobile ad-hoc network (MANET) is a network composed of mobile nodes mainly characterized by the absence of any centralized coordination or fixed infrastructure, which makes any node in the network act as a potential router. MANETs are also characterized by a dynamic, random and rapidly changing topology. This makes the classical routing algorithms fail to perform correctly, since they are not robust enough to accommodate such a changing environment. Consequently, more and more research is being conducted to find optimal routing algorithms that would be able to accommodate for such networks [1] [2] [3]. Our objective in this paper is to carry out a performance comparison between two routing protocols, namely, AODV (Ad hoc On Demand Distance Vector) and DSDV (Destination Sequenced Distance Vector). While both routing protocols use sequence numbers to prevent routing loops and to ensure the freshness of routing information, AODV and DSDV differ drastically in the fact that they belong to two different routing families. Namely, AODV is a reactive protocol (routes are only generated on demand, in order to reduce routing loads), and DSDV is a proactive protocol with frequent updates of routing tables regardless of need. 1.1 Properties of MANET MANET have the following special features that should be considered in designing solutions for this kind of networks. Dynamic Topology: Due to the node mobility, the topology of mobile multi-hop ad hoc networks changes continuously and unpredictably. The link connectivity among the terminals of the network dynamically varies in an arbitrary manner and is based on the proximity of one node to another node. It is also subjected to frequent disconnection during node’s mobility. MANET should adapt to the traffic and propagation conditions as well as to the mobility patterns of the mobile network nodes. The mobile nodes in the network dynamically establish routing among themselves as they move about, forming their own network on the fly. Moreover, a user in the MANET may not only operate within the ad hoc network, but may require

635

National Conference on Advanced Computing and Communication Technology access to a public fixed network. Bandwidth: MANETs have significantly lower bandwidth capacity in comparison with fixed networks. The used air interface has higher bit error rates, which aggravates the expected link quality. Current technologies suitable for the realization of MANETs are IEEE 802.11 with bandwidth up to 54Mbps and Bluetooth providing bandwidth of 1Mbps. The nature of high bit-error rates of wireless connection might be more profound in a MANET. One end-to-end path can be shared by several sessions. The channel over which the terminals communicate is subjected to noise, fading and interference, and has less bandwidth than a wired network. In some scenarios, the path between any pair of users can traverse multiple wireless links and the links themselves can be heterogeneous. Energy: All mobile devices will get their energy from batteries, which is a scarce resource. Therefore the energy conservation plays an important role in MANETs. This important resource has to be used very efficiently. One of the most important system design criteria for optimization may be energy conservation. Security: The nodes and the information in MANETs are exposed to the same threats like in other networks. Additionally to these classical threats, in MANETs there are special threats, e.g. denial of service attacks. Also mobility implies higher security risks than static operation because portable devices may be stolen or their traffic may insecurely cross wireless links. Eavesdropping, spoofing and denial of service attacks should be considered. Autonomous: No centralized administration entity is required to manage the operation of the different mobile nodes. In MANET, each mobile terminal is an autonomous node, which may function as both a host and a router. So usually endpoints and switches are indistinguishable in MANET. Distributed Operation: Since there is no background network for the central control of the network operations, the control and management of the network is distributed among the terminals, The nodes involved in a MANET should collaborate among themselves and each node acts as a relay as needed, to implement functions e.g. security and routing. Multi-hop Routing: Basic types of ad hoc routing algorithms can be single-hop and multi-hop, based on different link layer attributes and routing protocols. Single-hop MANET is simple in comparison with multi-hop MANET in terms of structures and implementation. When delivering data packets from a source to its destination out of the direct wireless transmission range, the packets should be forwarded via one or more intermediate nodes. Light-Weight Terminals: In most cases, the MANET nodes are mobile devices with less CPU processing capability, small memory size and low power storage. Infrastructure-less and Self Operated: A mobile ad hoc network includes several advantages over traditional wireless networks, including: ease of deployment, speed of deployment and decreased dependence on a fixed infrastructure. MANET is attractive because it provides an instant network formation without the presence of fixed base stations and system administrators.

636

2. Description of Protocol 2.1 Proactive and Reactive Routing Protocol [1][8] 2.1.1Proactive Routing Protocol These protocols are also referred to as Table Driven Routing Protocols. These protocols are extensions of the wired network routing protocols. They maintain the global topology information in the form of tables at every node. These tables are updated frequently in order to maintain consistent and accurate network state information. In proactive routing protocols, nodes continuously search for routing information with in a network, so that when a route is needed, the route is already known. Periodically floods the network to reconstruct the routing table e.g. DSDV (Destination Sequenced Distance Vector) routing protocol. Destination-Sequenced Distance Vector Routing (DSDV) The Destination Sequenced Distance Vector Routing protocol (DSDV) is a table-driven algorithm based on the classical Bellman-Ford routing mechanism. The improvements made to the Bellman-Ford algorithm include freedom from loops in routing tables. DSDV is a distance vector routing protocol. Each node has a routing table that indicates for each destination, which is the next hop and number of hops to the destination. Each node periodically broadcasts routing updates. A sequence number is used to tag each route. It shows the freshness of the route, a route with higher sequence number is more favorable. In addition, among two routes with the same sequence number, the one with fewer hops is more favorable. If a node detects that a route to a destination has broken, then its hop number is set to infinity and its sequence number updated (increased) but assigned an odd number, even numbers correspond to sequence numbers of connected paths. 2.2 Reactive Routing Protocol These protocols are also referred to as On Demand Driven or the source initiated routing protocol. It is the second category under ad hoc mobile routing protocols. For these types of protocols, it creates routes only when desired by source nodes. When a node requires a route to destination, it initiates route discovery process within the network. This process completes once one route is found or all possible route permutations are examined. Once a route is discovered and established, it is maintained by route maintenance procedure until either destination becomes inaccessible along every path from source or route is no longer desired. Ad hoc On-demand Distance Vector Routing (AODV) Ad hoc On-demand Distance Vector Routing (AODV) [10] is an improvement on the DSDV [9]. AODV minimizes the number of broadcasts by creating routes on-demand as opposed to DSDV that maintains the list of all the routes. To find a path to the destination, the source broadcasts a route request packet. The neighbors in turn broadcast the packet to their neighbors till it reaches an intermediate node that has a recent route information about the destination or till it reaches the destination. A node discards a route request packet that it has already seen. The route request packet uses sequence numbers to ensure that the routes are loop free and to make sure that if the intermediate nodes reply to route requests, they reply with the latest information only. When a node forwards a

636

637

National Conference on Advanced Computing and Communication Technology route request packet to its neighbors, it also records in its tables the node from which the first copy of the request came. This information is used to construct the reverse path for the route reply packet. AODV uses only symmetric links because the route reply packet follows the reverse path of route request packet. As the route reply packet traverses back to the source, he nodes along the path enter the forward route into their tables. If the source moves then it can reinitiate route discovery to the destination. If one of the intermediate nodes move then the moved nodes neighbor realizes the link failure and sends a link failure notification to its upstream neighbors and so on till it reaches the source upon which the source can reinitiate route discovery if needed.

Due to the fact that changes in paths were avoided, there were no losses so the window will start high. And also the round trip time (needed to increase the window by one unit) is longer since a direct path is not used here. We thus see that finding a shorter path results in a better TCP performance. .

3. Simulation Both AODV and DSDV were tested by changing the routing protocol as AODV and DSDV and simulated by using Network Simulator (ns-2)[2]. The algorithms were tested using by taking routing protocol as DSDV and AODV by taking the same number of nodes. We performed two different experimental models, in both the experiments, the movement speed of the nodes was taken as 3m/s , 5m/s and 5m/s. The nodes movement was governed by a random and continuous model. The pause time was taken as 200 sec. The simulation environment consisted of a 500m x 400m region where nodes were randomly moving with a constant average speed. The nodes are equipped with omni-directional antennas. The simulation spanned over a period of 150 seconds, with a single TCP connection established between only two nodes at time t = 10 seconds. For each protocol, we calculated three performance criteria by using awk script: -Average throughput,

-Average delay, - Packet delivery ratio

- Packet Loss Rate 4. Simulation Results First we will discuss the simulation results of the two different experiments conducted for each protocol and for different routing protocols. Then, we will choose a specific case from each simulation with different routing protocols to perform the comparison between the AODV and DSDV.

4.1 AODV Results The simulation with the same parameters as before is repeated with AODV. When performing the simulation, we observe five phases of operation. In the first, the nodes are too far away and there is no connectivity between the source and the destination. During phase 2 the connection between nodes 0 and 5 use nodes 2, and 4 as a relay and the packets drop will start, whereas in the 3rd phase, there is a direct path between node 0 and 5, in the case of AODV routing protocol the 4th is not there and the connection between the source and the destination will continue up to the last phase, and this case helped to increase the number of the packets received , in the last phase the nodes 1 and 5 will use node 2 as a rely. It had throughout a long single phase in which the same five phases was used, in which nodes 2, and 4 relayed the packets.

Figure 1: Throughput AODV with default window size Routing Protocol AODV

Average (delay) 0.127173

Avg. Throughput 67681.7

Packet Sent 1620

Packet Received 1510

Packet Delivery Ratio AODV Packet delivery ratio (%) =received packets /sent packets *100 =1510/1620*100=93.20% Packet L oss Rate Loss (%) = (1-received packet/sent packet) *100 = (1-1510/1620)*100=6.83 4.2 DSDV Results When performing the simulation of DSDV, we observe five phases of operation. In the first, the nodes are too far away and there is no connectivity between the source and the destination. During phase 2 the connection between nodes 0 and 5 use nodes 2, and 4 as a relay and the packets drop will start, whereas in the 3rd phase, there is a direct path between node 0 and 5, in the 4th phase the source and the destination nodes will use the node 3 as a rely, in the last phase the nodes 1 and 5 will use node 2 as a rely. At the beginning the nodes are too far away and a connection cannot be set. The first TCP signaling packet is transmitted at time 10 but the connection cannot be opened. After 20 seconds (time out) the connection between node 0 and node 5 will start and the packets will start passing between them. After 40 seconds node 0 will be closer to node 5, so the number of packets are increased between them. After 60 seconds, nodes 0 and 5 will be closer to each other so there is a direct connection established between them and the maximum value for packets received will be in this period (between 60-to-120 second). After that the distance between the source and the destination will increase and the mobiles get further apart till the direct link brakes and the packets drop will happen. The routing protocol is too slow to react and to create an alternative route.

637

638

National Conference on Advanced Computing and Communication Technology

Figure 4: Comparative throughput between DSDV and AODV with window size 2000 for standard TCP Routing Protocol DSDV

Average (delay) 0.130477

Avg. Throughput 92837.71

Packet Sent 1366

Packet Received 1216

AODV

0.127173

67681.7

1620

1510

Routing Protocol DSDV AODV

Figure 1: Throughput DSDV with default window size Routing Protocol DSDV

Average (delay) 0.130477

Avg. Throughput 92837.71

Packet Sent 1366

Packet Delivery Ratio (%) 89.01 93.20

Packet L oss Rate L oss (% ) 10.99 6.83

Packet Loss Ratio AODV results in lower packet drop than DSDV. This is due to the extensive routing information exchanged between the nodes at regular intervals providing a correct, up to date route at all times. Also, no additional packet drop is noticed as speed increases, since the routing updates become more frequent, making the packet drop rates almost unaffected. This feature is not present in DSDV. Since the routes are only generated upon request, a route may become outdated by the time the route request is generated and the route reply would arrive. The packets transmitted during this transient period run the risk of being dropped by the network.

Packet Received 1216

Packet Delivery Ratio DSDV Packet delivery ratio (%) = (received packets /sent packets) *100 =1216/1366*100=89.01% Packet Loss Rate Loss (%) = (1-received packet/sent packet) *100 = (1-13895/13961)*100=10.99

5. Compar ison We have compared the performance of DSDV (Destination Sequenced Distance-Vector) from the AODV (Ad-hoc OnDemand Distance Vector) from the Reactive family. We used simulation model to demonstrate the performance characteristics of these protocols as in figure 3 and figure 4.

We also create a simulation model in which we take different parameters. We create a separate file for movement of nodes. The different parameters chosen were as follows: No. of nodes: 6, pause time: 200.00 sec., moving max speed: 10.00 m/s, Topology boundary max x: 500.00, max y: For Communication we choose the following parameters: First of all we set the channel type,radio-propogation model, network interface type , MAC type , interface queue type ,link layer type number of nodes and taking the x & y topography as 500x400 And the time of simulation end

AWK script to extract the network Avg. throughput and Avg. Delay by using new-trace file

Figure 3: Comparative throughput between DSDV and AODV with default window size

Raw data can be parsed after each simulation, trace files recording the traffic and node movements generated. These files need to be parsed for extracting the information needed to measure the performance metrics. The trace file format looks like: s -t 10.267662078 -Hs 0 -Hd -1 -Ni 0 -Nx 5.00 –Ny 5.00 -Nz 0.00 -Ne -1.000000 -Nl RTR -Nw — -Ma 0 -Md 0 -Ms 0 -Mt 0 -Ii 20 -Is 0.255 -Id -1.255 -It This row of the trace file means: A packet was sent (s) at time (t) 10.267662078 sec, from source node (Hs) 0 to destination node (Hd) 1. The source node id (Ni) is 0, it’s x-co-ordinate (Nx) is 5.00, it’s ycoordinate (Ny) is 5.00, it’s z-co-ordinate (Nz) is 0.00, it’s energy level (Ne) is 1.000000, the trace level (Nl) is RTR and the node event (Nw) is blank. The MAC level information is given by duration (Ma) 0, destination Ethernet address (Md) 0,the source Ethernet address (Ms) is 0 and Ethernet type (Mt) is 0.

638

National Conference on Advanced Computing and Communication Technology The IP packet level information like packet id (Ii), source address. source port number is given by (Is) while the destination address. Destination port number is (Id). A awk program was developed to parse the output trace file and print out required data.

6. Conclusion and future work In this paper we have compared the performance of DSDV (Destination Sequenced Distance-Vector) from the Proactive family with the second type is AODV (Ad-hoc On-Demand Distance Vector) from the Reactive family. We used a simulation model to demonstrate the performance of these protocols. By simulating we can argue that if delay and packet loss ratio is our main criteria than AODV can be our best choice but if throughput is our main parameters for selection then DSDV gives better results compare to others because its throughput is best among others. However, DSDV perfectly scales to a small network with low node speeds. In this case, the simplicity of DSDV is preferred over the other more complex techniques without sacrificing the performance. While we focus only on the network throughput, and the delay, it would also be interesting to consider other metrics like power consumption, the number of hops to route the packet, fault tolerance, minimizing the number of control packets etc. In the future, complex simulations could be carried out to gain a more in depth performance analysis of the ad-hoc wireless networks and enhancing the performance and also for proposing new protocols and new algorithms to solve some of ad-hoc routing protocol problems.

639

[6] E. Royer and C. Toh, “A review of current routing protocols for ad-hoc mobile wireless networks,” IEEE Personal Communications, apr 1999. [7] M. Mauve, J. Widmer, and H. Hartenstein, “A survey on positionbased routing in mobile ad hoc networks,” IEEE Network Magazine, 15(6):30–39, nov 2001. [8] P. Misra, “Routing protocols for ad hoc mobile wireless networks.” [Online]. Available: http://www.cse.ohiostate. edu/˜jain/cis788-99/ftp/adhoc routing/index.html [9] C. Perkins and P. Bhagwat, “Highly dynamic destinationsequenced distance-vector routing (DSDV) for mobile computers,” in ACM SIGCOMM’94 Conference on Communications Architectures, Protocols and Applications, 1994, pp. 234–244. [10] C.E. Perkins, E.M. Royer & S. Das, Ad Hoc On Demand Distance Vector (AODV) Routing, IETF Internet draft, draft-ietfmanet-aodv-08.txt, March 2001

References: [1]S. Murthy and J. J. Garcia-Luna-Aceves, “An efficient routing protocol for wireless networks,” Mobile Networks and Applications, vol. 1, no. 2, pp. 183–197, 1996. [2] The Network Simulator-ns-2, http://WWW.isi.edu/nsnam/ns [3] S. Corson and J. Macker, “ Mobile Ad hoc Networking (MANET): Routing Protocol Performance Issues and Evaluation Considerations,” NetwoWorking Group RFC 2501, pp. 2–3, Jan. 1999. [4] Ayman Kayssi & Rima Khalaf, “Performance comparison of the aodv and dsdv routing protocols in mobile ad hoc networks” [5] A. Penttinen, “Research on ad hoc networking: Current activity and future directions.”

639

National Conference on Advanced Computing and Communication Technology

640

Implementing of CSMA/CA (IEEE 802.11) Protocol in DCF & EDCF Keshav Jindal, M.Tech (CS) student, PDM College of Engg. Bhadurgarh. [email protected] Mohd Shahid, A.P in IT Deptt, PDM COLLEGE OF ENGINEERING (Bahadurgarh) [email protected] Baharat Bhushan , A.P in CSE Deptt, PDM COLLEGE OF ENGINEERING (Bahadurgarh) [email protected] 802.11 medium access control (MAC) sub-layer in order to support Quality of Service (QoS) [1][5]. The new 802.11e MAC will expand the 802.11 application domain by, for example, enabling such applications as voice and video services. The mandatory part of the current 802.11 MAC is called the distributed coordination function (DCF), which is based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). A new component of the upcoming 802.11e MAC is called the Enhanced DCF (EDCF), which is the enhanced version of the legacy DCF. The EDCF provides differentiated channel access to frames of different priorities as labeled by the higher layer. IEEE 802.11 IEEE Standards documents are developed within the IEEE Societies and the Standards Coordinating Committees of the IEEE Standards Association (IEEE-SA) Standards Board [2]. In 1997, the IEEE adopted the first standard for WLANs and revised in 1999 after that it keep on providing its different version for higher throughput, highly reliable data delivery, QOS & continuous network connection time to time[3], notable among them are 802.11b, g & e. CSMA/CA IEEE 802.11 standard offers three different physical layer implementations; each of them corresponds to a kind of technology that has been commonly used to implement WLAN systems. The MAC layer is exactly the same for each implementation, which defines the exact operation of the CSMA/CA protocol [5] In this paper we Study CSMA/CA, analyze its access algorithm and examine implementation of CSMA/CA in various IEEE 802.11 Standards (Such as 802.11 a, b, g & e) for wireless LAN from first version to the version being developed by IEEE currently published till date.

Abstract— IEEE 802.11 MAC layer supports two main protocols: DCF (Distributed Coordination Function) and EDCF (Enhanced Distributed Coordination Function). During the evaluation of EDCF, the performance of various access categories was the determining factor. The DCF in IEEE 802.11 is based on CSMA with Collision Avoidance (CSMA/CA) protocol, which can be seen as a combination of the CSMA and MACA (Multiple Access Collision Avoidance) schemes. The protocol uses the RTS– CTS–DATA–ACK sequence for data transmission. The protocol uses not only physical carrier sensing, but it also introduces the novel concept of virtual carrier sensing. EDCF (Enhanced DCF) is also based on CSMA/CA which emerged because of some Drawbacks of DCF. EDCF is supposed to provide better performance enhancement for real time traffic as compared to DCF. EDCA is a contention-based channel access function that operates concurrently with HCF Controlled Channel Access (HCCA) that is based on a polling mechanism, which is controlled by the Hybrid Coordinator (HC). EDCA is used only during CP while HCCA can be used in both CP and CFP, however 802.11e standard recommends using HCCA during CP only, mainly due to the complexity in implementing polling mechanism for both Quality of Service and best effort traffic concurrently. Than means there are different CSMA/CA IEEE 802.11-based implementations. This Paper is aiming at study, assessment, evaluation & Implementation of 802.11 CSMA/CA based protocol included in 802.11 a b, g & e standards. Keywords: ad hoc network, EDCA, QOS, mobility, radio frequency,

I. INTRODUCTION In recent years, IEEE 802.11 WLAN [1] has emerged as a prevailing technology for the (indoor) broadband wireless access. Today, IEEE 802.11 can be considered a wireless version of Ethernet by virtue of supporting a best-effort service (not guaranteeing any service level to users/applications). The IEEE 802.11 Working Group is currently defining a new supplement to the existing legacy

2. RELATED WORK Many Papers have been published relating to study of different CSMA/CA IEEE 802.11-based implementations but they can not be said absolute as new version of 802.11 is approved time to time by IEEE as per current demand, these new version have no place in these published paper[4-7] where comparison is either in-between any 2 versions. 640

National Conference on Advanced Computing and Communication Technology

641

(ACK) frame is sent by the receiver to the sender for every successful reception of a frame. The ACK frame is transmitted after a short IFS (SIFS), which is shorter than the DIFS. As the SIFS is shorter than DIFS, the transmission of ACK frame is protected from other station’s contention. The CW size is initially assigned CW min and if a frame is lost i.e. no ACK frame is received for it, the CW size is doubled, with an upper bound of CWmax and another attempt with back-off is performed. After each successful transmission, the CW value is reset to CWmin.All of the MAC parameters including SIFS, DIFS, Slot Time, CWmin, and CWmax are dependent on the underlying physical layer (PHY) [8]. DIFS is determined by SIFS+2*SlotTime, irrespective of the PHY.

Needless to say that there was only data to communicate in previous years so usage of DCF was seen as enough, later on real time traffic comprised of video, voice & Data were started to exchange with QOS(i.e. Enhanced DCF required) and now Ip Multimedia System is the need of time. So we make a comparative study of all of them in this single paper to accommodate all from DCF to EDCA which are based on carrier sense. 3. Background M AC PROTOCOL S Distributed Coordination Function (DCF) is the currently used protocol that comes with an optional Point coordination Function (PCF) Protocol. Enhanced Distributed Coordination Function (EDCF) is the future protocol that promises to provide the QoS. The explanation of these protocols is as follows: 3.1 Distributed Coordination Function (DCF) DCF is the basic and mandatory MAC mechanism of legacy IEEE 802.11 [1] WLANs. It is based on carrier sense multiple access with collision avoidance (CSMA/CA). Working of DCF is explained in this section as it is the basis for the Enhanced Distributed Channel Access (EDCA), which we discuss in this paper. The 802.11 MAC works with a single first-in-first-out (FIFO) transmission queue [10]. The CSMA/CA constitutes a distributed MAC based on a local assessment of the channel status, i.e. whether the channel is busy or idle. If the channel is busy, the MAC waits until the medium becomes idle, then defers for an extra time interval, called the DCF Inter-frame Space (DIFS). If the channel stays idle during the DIFS deference, the MAC then starts the back-off process by selecting a random back-off counter (or BC).For each slot time interval, during which the medium stays idle, the random BC is decremented. If a certain station does not get access to the medium in the first cycle, it stops its back-off counter, waits for the channel to be idle again for DIFS and starts the counter again. As soon as the counter expires, the station accesses the medium. Hence the deferred stations don’t choose a randomized back-off counter again, but continue to count down. Stations that have waited longer have the advantage over stations that have just entered, in that they only have to wait for the remainder of their backoff counter from the previous cycle(s). Each station maintains a contention window (CW), which is used to select the random backoff counter. The BC is determined as a random integer drawn from a uniform distribution over the interval [0, CW].The larger the contention window is the greater is the resolution power of the randomized scheme. It is less likely to choose the same random BC using a large CW .However, under a light load; a small CW ensures shorter access delays .The timing of DCF channel access is illustrated in Fig. 1. An acknowledgement

Fig.1 Timing relationship for DCF 3.2 Enhanced Distributed Coordination Function (EDCF) EDCF is designed to provide prioritized QoS by enhancing the contention-based DCF. It provides differentiated, distributed access to the wireless medium for QoS stations (QSTAs) using 8 different user priorities (UPs).Before entering the MAC layer, each data packet received from the higher layer is assigned a specific user priority value. How to tag a priority value for each packet is an implementation issue. The EDCA mechanism defines four different first-in first-out (FIFO) queues, called access categories (ACs) that provide support for the delivery of traffic with UPs at the QSTAs. Each data packet from the higher layer along with a specific user priority value should be mapped into a corresponding AC according to table II. Note the relative priority of 0 is placed between 2 and 3.This relative prioritization is rooted from IEEE 802.1d bridge specification [5]. Different kinds of applications (e.g., background traffic, best effort traffic, video traffic, and voice traffic) can be directed into different ACs. For each AC, an enhanced variant of the DCF, called an enhanced distributed coordination function (EDCF), contends for TXOPs using a set of EDCF parameters from the EDCF Parameter Set element or from the default values for the parameters when no 641

National Conference on Advanced Computing and Communication Technology

642

• IEEE 802.11i: Supplementary to the MAC layer to improve security. It will apply to 802.11 physical standards a,b and g. It provides an alternative to WEP with new encryption methods and authentication procedures. IEEE 802.1x forms a key part of 802.11i. of 2003. • IEEE 802.11x: A framework for regulating access control of client stations to a network via the use of extensible authentication methods. It forms a key part of the important 802.11i proposals for enhanced security. It applies to 802.11 physical standards a, b and g. • IEEE 802.1p: A standard for traffic class and dynamic multicast filtering. It provides a method to differentiate traffic streams in priority classes in support of quality of service offering. It forms a key part of the 802.11e proposals for QoS at the MAC level. This applies to 802.11 physical standards a, b and g.

EDCF Parameter Set element is received from the QAP of the QBSS with which the QSTA is associated. IEEE standardized the various version time to time from its version dated 1997 to till date. We summaries [2] them as follows: • IEEE 802.11a: A physical layer standard for WLANs in the 5GHz radio band. It specifies eight available radio channels. Maximum link rate is of 54-Mbps per channel. • IEEE 802.11b: A physical layer standard for WLANs in the 2.4 GHz radio band. It specifies three available radio channels. Maximum link rate is of 11-Mbps per channel • IEEE 802.11d: 802.11d is supplementary to the Media Access Control layer in 802.11 to promote worldwide use of 802.11 WLANs. It will allow access points to communicate information on the permissible radio channels with acceptable power levels for user devices. The 802.11 standards cannot legally operate in some countries; the purpose of 11d is to add features and restrictions to allow WLANs to operate within the rules of these countries. • IEEE 802.11e: Supplementary to the MAC layer to provide QoS support for LAN applications. It will apply to 802.11 physical standards a, b and g. The purpose is to provide classes of service with managed levels of QoS for data, voice and video applications. • IEEE 802.11f: This is a “recommended practice” document that aims to achieve radio access point interoperability within a multi-vendor WLAN network. The standard defines the registration of access points within a network and the interchange of information between access points when a user is handed over from one access point to another. • IEEE 802.11g: A physical layer standard for WLANs in the 2.4 GHz and 5 GHz radio band. It specifies three available radio channels. The maximum link rate is 54Mbps per channel compared with 11 Mbps for 11b. The 802.11g standard uses OFDM modulation but, for backward compatibility with 11b, it also supports complementary code keying (CCK) modulation and, as an option for faster link rates, allows packet binary convolution coding (PBCC) modulation. • IEEE 802.11h: This standard is supplementary to the MAC layer to comply with European regulations for 5 GHz WLANs. European radio regulations for the 5 GHz band require products to have transmission power control (TPC) and dynamic frequency selection (DFS). TPC limits the transmitted power to the minimum needed to reach the furthest user. DFS selects the radio channel at the access point to minimize interference with other systems, particularly radar.

CSMA/CA Shared broadcast channels are often used in local area networks (LANs). Both wired 802.3 Ethernet network and wireless 802.11 LAN network must coordinate transmissions onto the shared communication channel. In case of 802.3 Ethernet, the shared channel is the shared wire whereas in case of wireless 802.11 LAN the shared channel is the radio frequency. The media access control (MAC) protocol coordinates the transmission. The media access control of IEEE 802.11 is using carrier sense multiple access with collision avoidance (CSMA/CA) as the fundamental access. In 802.11(figure1) carrier sense (CS) is performed both at physical layer (physical carrier sensing), and at the MAC layer (virtual carrier sensing). Although the IEEE 802.11 standard belongs to the same standard family as wired 802.3 Ethernet, it has a significantly different media access protocol. While Collision Detection works well on Ethernet, they cannot be used in wireless LAN. The 802.11 standard includes a basic Distributed Coordination Function (DCF). The DCF is the fundamental access method used to support asynchronous data transfer on the best effort basis. As specified in standards, the DCF must be supported by all the stations in a basic service set (BSS). The DCF is based on CSMA/CA. [8] There are two techniques used for packet transmitting in DCF. The default one is a two-way handshaking mechanism, also called basic access method. The destination station transmits a positive acknowledgement (ACK) message to signal a successful packet transmission. The other optional mechanism is a four-way handshaking access method, which uses the request-to-send/clear-to-send (RTS/CTS) technique to reserve the channel before data transmission.

642

National Conference on Advanced Computing and Communication Technology

643

nodes and 4 destination nodes. Each source node is transmitting with a different priority; Node 0 is given a higher priority than Node 1, which is given also a higher priority than Node 2. Node 2, in its turn, is given a higher priority than Node 3. Each source is a Constant Bit Rate source over UDP. The size of a transmitted packet is 512 bytes.

Figure 4: Throughput using EDCA 802.11e Transmission rate of a node is 550Kbps. Assume that all nodes are separated by 100m from one Access Point (AP). To develop infrastructure scenario the NOAH routing protocol was used. The 802.11 physical layers in the scenarios is based on the 802.11b standard, with a data rate of 2Mbps. Two separate simulation scenarios (DCF,EDCA) were conducted, each simulation lasted for 80 sec. Nodes starts transmitting gradational, Node 0 starts transmitting at time 1.4 sec, Node 1 starts transmitting at time 10 sec, Node 2 starts transmitting at time 20 sec, and Node 3 starts transmitting at time 30 sec.

Figure 1 : Flow Diagram of CSMA/CA Protocol 4.

Comparison Result

 Simulation Result This simulation scenario reveals the performance of the DCF and the EDCA in terms of throughput, end-to-end packet latency, and packet loss rate. The objective of implementing this scenario is twofold, first to compare the performance of the new QoS-enabled MAC mechanism (EDCA) and the legacy MAC (DCF). And second to test the used EDCA module by comparing our results with others published researches used the same module.

Figure 5: Delay by DCF With the Same Topography Delay was taken into account by using DCF and EDCA. Figure 5 and 6 shows the same.  DCF 802.11 MAC design is to distribute the transmission capacity fairly among the stations.  DCF does not support QoS, and thus dose not optimize the transmission of real-time traffic such as voice and video.

Figure 3: Throughput Using DCF 802.11 First we analyze the throughput of DCF and EDCA. The simulation topology consists of 8 mobile nodes: 4 source 643

National Conference on Advanced Computing and Communication Technology

644

maximum delay it has to introduce during the connection. It still needs further research and simulation work to provide real QoS to IEEE 802.11 wireless networks. Voice and video call quality is a very interesting field and much have yet to be accomplished by increasing the bandwidth of both wired and wireless connections. But the bandwidth is very hard to be greatly improved because of the interference and fading’s, and also more and more developed application level service consumes lots of the network resources. So it is really critical to have real QoS to give differentiated service and guarantee the performance in cooperation of both MAC layer and network layer functions. It will be interesting to see where the research will take in this area in the future.

Figure 6: Delay EDCA 802.11e  As the number of transmitting nodes increase in the transmission region of the WLAN network, the overall performance experiences heavy oscillations and reduction of the obtained throughput, as plotted in Figure6. In context Figure 5 plots the packet drop rate, and Figure 6 plots the packet delay, both plots reflect the dramatic behavior as more nodes are sharing the network resources.  In general, these simulation scenarios show the poor performance of DCF 802.11 in heavily loaded networks. They also reveal how priorities, in EDCA 802.11e, can guarantee a relatively constant and reliable behavior even in heavily congested channel conditions. Following observations are obtained from simulation for EDCA 802.11e:  Channel resources are divided relatively to the priorities assigned to different streams; therefore streams with higher priorities are protected against those of lower priorities. This way IEEE 802.11e, can guarantee a relatively constant and reliable behavior even in heavily congested channel conditions.  Due to the relative high values of the contention parameters (AIFS, and CWmin) assigned to the AC_BK, it suffers a long period of channel access delay. Reflecting in a very low throughput, and high delay.

References: [1] Prado “IEEE 802.11e Contention-Based Channel Access (EDCF) Performance Evaluation” [2] IEEE std. 802.11, 802.11a, 802.11b-1999, Part 11 Wireless LAN MAC and PHY Layer Specification [3] Mustafa Ergen, “IEEE 802.11 Tutorial” Department of Electrical Engineering and Computer Science, University of California Berkeley [4] Crow, B.P.; Widjaja, I.; Kim, L.G.; Sakai, P.T., “IEEE 802.11 Wireless Local Area Networks” Volume 35, Issue 9, Sep 1997 Page(s):116 – 126 [5] Tien-Shin Ho; Kwang-Cheng Chen, “Performance analysis of IEEE 802.11 CSMA/CA medium access control protocol” [6]Stefan Mangold, Sunghyun Choi, Peter May, Ole Klein, Guido Hiertz, and Lothar Stibor, “IEEE 802.11e Wireless LAN for Quality of Service,” in Proc. European Wireless ’02, Florence, Italy, February 2002. [7] Miquel Oliver, Ana Escudero, “Study of different CSMA/CA IEEE 802.11-based implementations” [8] Timo Holopainen, IEEE 802.11 CSMA/CA Medium Access Protocol. [9] ISMAHANI BINTI ISMAIL, “STUDY OF ENHANCED DCF (EDCF) IN MULTIMEDIA APPLICATION” [10] Emanuel Puºchiþã1, Tudor Palade, Ligia Chira, “Performance Evaluation of DCF vs. EDCF Data Link Layer Access Mechanisms for Wireless LAN Scenarios: QoS Perspective” [11] Kevin Fall, Kannan Varadhan, “The ns Manual (formerly ns Notes and Documentation)”, January 6, 2009 [12] Asma Abdalla Mustafa Elmangosh, “Quality of Service Provisioning within IMS-WLAN Interworking [13] Tommi Larsson Yusheng Liu,” A Study of EDCA and DCF in Multihop Ad Hoc Networks” [14] IEEE Std. 802.11-1999, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications, Reference number ISO/IEC 8802-11.1999.

5. Conclusion and future work This project presented a performance study of EDCF versus DCF access mechanisms in IEEE 802.11 Wireless LANs. From the results this far, it can be concluded that EDCF is the better choice than DCF because it can provide differentiated channel accesses for different traffic types. With the observed delay performance, it is expected that EDCF can support real-time applications with voice and video traffic with a reasonable quality of service. The Same is true for its advanced EDCA 802.11e. But EDCA is still not perfect [13]. The EDCA algorithm is also based on random access technology, and cannot guarantee a quality level for high-priority traffic, for example how much minimum throughput it must provide or how much 644

Design of Middleware for Effective Battery Power Utilization in Mobile Devices Lalit Kumar1, Sunil Kumar2, Kuldeep Kumar1 1

M.Tech (CSE), 2Assistant Professor Department of Computer Science & Engineering Guru Jambheshwar University of Science & Technology, Hisar (Haryana) – India [email protected], [email protected], [email protected]

applications, greater the power consumed. However, all the applications that are running in the mobile devices do not consume the same amount of power all the time. Some applications demands higher power for their optimum performance while some require low. Even when peak performance is required by the whole system certain applications like video clips, MMS, games etc consume more power as compared to the SMS, time/date, alarm application. So determining the system wide power consumption and effectively managing the power consumption is highly application dependant. In a dynamic power management the system moves from one system state to other system state in accordance with the application requirement thus saving the power while managing the QoS (quality of service).

Abstract: Advances in mobile computing have presented new challenges for the designers of mobile devices and its operating system. One such challenge is to reduce power consumption of mobile devices to prolong battery life. Several factors such as processor, memory, backlight, keyboard, display contribute significantly towards power consumption for a mobile device. In this paper we propose an adaptive middleware based approach that provides low system power state on these devices that consumes less power when the system does not require the full performance of devices. The system continuously decides what state is best suited to current requirement and changes appropriately.

1. Introduction 2. Related work

The advent of mobile computing has introduced new challenges for the designer of computers and computer operating systems. One such significant challenge is to reduce power consumption in ways that adversely affect the user the least. As Mobile devices have battery as the main source of power and power consumption is therefore the limiting factor for the handheld mobile devices. Unfortunately battery technology has been improving at only a modest pace in terms of increased capacity per unit weight and volume. However if we can succeed in reducing the basic consumption of individual components of mobile computing devices, we have the luxury of either reducing the battery dimensions while retaining the original charge characteristics, or increasing the battery charge time while retaining the original battery dimensions or combinations of both. However, higher the performance required by the

Reducing the power consumption for low power mobile devices always remains a significant research challenge. Several researches have been done in this area. The power optimizing techniques proposed in [1] focus on saving energy by controlling frequency and supply voltage. In fact if the CPU clock frequency and supply voltage can be controlled, linear and quadratic savings in power can be realized. [2] saves the processor energy by adjusting the processor speed and voltage in intervals. The processor speed is determined by Predicting future workload, based on the workload over the proceeding interval. [3] Dynamic voltage scaling attempts to reduce power consumption of mobile devices. Certain other techniques saves the energy by controlling soft real time scheduling of applications based on periodic tasks/jobs submitted

645

by applications [4, 5]. While techniques based on application QoS achieve power savings by utilizing application performance at various QoS levels [6]. Our approach tries to optimize the power consumption of a system. Our middleware provides low power states on which the system operates. In this way when the system does not require the full performance of the devices, it can switch to the low system power states.

3. Energy aware middleware Our proposed middleware for effective utilization of the battery power in mobile devices is as shown in Figure 1.

The middleware will take input from the user/application and pass this information to the power management services provided by OS. The middleware reduces the system-wide energy consumption by providing multiple system operating states. Applications/Users are allowed to specify the power requirement for the devices they require during the process of their execution. For ex, instead of moving the devices and the system for all the applications to either one of these states 1. On 2. User Idle 3. System Idle 4. Suspend. We will be having a number of states as per the requirements of application.

4. Planned Implementation Work

The system architecture consists of hardware which supports multiple device operating points, an operating system which provides power management services for ex. Windows CE Power Manager, our proposed middleware, and user interface which requests for system as well as device level power requirements.

We will be using the Windows CE as the operating system used for the mobile devices as it exposes the source code and can be customized. The windows CE operating system has a module called as ‘Power Manager’ that acts as an interface between devices, applications and defined OS’s system and device power states. The default ‘Power manager’ in Windows CE defines four system power states i.e. On, UserIdle, SystemIdle and Suspend.

Our idea is: (1) to have better control on the power consumption of the devices by extending the number of system power states supported by the OS; and (2) to compute optimized operating point of the system based on the run time requirements of the applications.

4.1 Experimental Experiment Results

Application/User

Proposed Middleware

MMiddleware Operating System Figure 1

Set

Up

and

We have added support for power management to a backlight device driver to reduce power consumption on target devices. Our proposed middleware strives for optimizing the system power by introducing a new system power state to the default power state (management functionality) provided by win CE Power Manager. We have modified and optimized the PM module in Win CE to introduce or generate this new state. The PM in Windows CE provides API’s for the functionalities such as changing the device and system power states, receiving notifications about the power events, system suspend/resume, power relationship, etc. We have added a backlight driver bkl1. The new state added for this backlight driver is ‘on_lalit1’. When the user specifies the requirement for system power state to be set to ‘on_lalit1’, the middleware in turn signals that back light should be diminished.

Screenshot 1

This screenshot is when we have started pmmon.exe and it is monitoring system power state in the background. It prints messages whenever the device

transitions from one power state to another. E.g. messages for transition to ‘useridle’ and ‘on’ states can be seen above.

Screenshot 2 In Screenshot 2 we have run ‘pmreq.exe’ program with arguments ‘bkl1: 1’ to set the minimum power requirement of our pseudo backlight driver (device name “bkl1:”) to D1 (numerical value 1). This program uses the SetPowerRequirement API to set the power requirement. The program waits after calling this API and the power requirement remains set for that duration. The requirement will be removed when we kill the program later (using CtrlC).

We can see that the system has determined a lower system power state (‘on_lalit1’) based on the current requirement and has notified all devices about it. The power level for Backlight driver needs to be changed and the OS has sent a message to this driver to set its power state to D1. We can see a message “***Lalit: Dimming backlight” in the screenshot above. Also, the system power state has changed from ‘on’ to ‘on_lalit1’.

Screenshot 3 In Screenshot 3 we have killed the ‘pmreq.exe’ by hitting ‘Ctrl-C’. This will release the power requirement set on backlight driver. The system will now again compute the system power state and determine that backlight driver needs to run at power level D0. The new power state will be ‘on’ and we

can see a transition from ‘on_lalit1’ power state to ‘on’ power state in the above screenshot. Also, we see a message “***Lalit: Backlight at full power” when the backlight driver comes back to full power (D0 power level).

References 5. Yuan W. and Nahrstedt K..,” Energy-efficient soft real-time CPU scheduling for mobile multimedia systems” In Proceedings of the nineteenth ACM symposium on Operating systems principles, Oct 2003 Boltan Landing, NY

1. Weiser M.,Welch B.,Damers A.and Shenkar S. “Scheduling for Reduced CPU Energy”,USENIX Symposium on Operating System Design and Implementation”,Nov 1994,ps:13-23 2. K. Govil, E. Chan, and H.Wasserman, “Comparing Algorithms for Dynamic SpeedSetting of a Low-Power CPU,”In Proceedings of.ACM Int'l Conf. on Mobile Computing and Networking, Nov.1995,ps 13-25,

6. Pillai P.,Huang h. and Shin K.J,”Energy Aware Quality of service adaptation” Technical Report CSE –TR-479-03,University of Michijan,2003 7. IBM and Monta Vista Software, “Dynamic Power Management for Embedded Systems”, http://www.research.ibm.com/projects/dpm.html ,November 2002

3. Pillai P. and Shin K.G., “Real-time dynamic voltage scaling for low-power embedded operating systems”, In Proceedings of the eighteenth ACM symposium on Operating systems principles, 2001,ps:89-102 4. Yuan W. and Nahrstedt K..,” Integration of dynamic voltage scaling and soft real-time scheduling for open mobile systems”, In Proceedings of the 12th international workshop on Network and operating systems support for digital audio and video, May 2002, Miami Beach, Florida

650

8

L. Feeney and M. Nilsson, “Investigating the Energy Consumption of a Wireless Network Interface in an Ad Hoc Networking Environment”, In Proc. 20th IEEE Conf. Computer Comm., pp. 1548-1557, 2001.

9

E. Shih, P. Bahl and M.J. Sinclair, “Wake on Wireless: An Event Driven Energy Saving Strategy for Battery Operated Devices”, In Proc. Eighth Int"l Conf. Mobile Computing and Networking, pp. 160-171, 2002.

National Conference on Advanced Computing and Communication Technology

Inter-Carrier Interference Self Cancellation Scheme for OFDM Mobile Systems Sarika Pal 1 , Narendra Pal2 , Department of Electronics and Communication Enginnering

1

Krishna Institute of Engineering and Technology Ghaziabad, UttarPradesh,India [email protected] 2

Department of Electronics and Communication Enginnering

Raj Kumar Goel Engineering College, Pilakhua Ghaziabad, , UttarPradesh,India [email protected]

OFDM is special case of multi-carrier communication system, where single data stream is modulated over number of lower sub-carriers. A well known problem of OFDM is its sensitivity to frequency offset between transmitted and received signals, which may be causes by Doppler shift in the frequency channel, or by the difference between transmitter and receiver local oscillator frequencies. This carrier frequency offset causes loss of orthogonality between sub carriers and the signals transmitted on each carrier are not independent of each other leading to inter carrier interference ICI.[3,4] Researchers have proposed various methods to combat ICI in OFDM systems. The existing approaches to reduce ICI are frequency domain equalization as in [5], time domain windowing as in [5] and the ICI self cancellation scheme as in [7]. In this paper an improved ICI self cancellation scheme is proposed. In this scheme redundant data is transmitted on to adjacent sub-carriers such that the ICI between adjacent sub-carrier cancels out at the receiver. This paper is organized as follows. Section 2 describes the standard OFDM system. In section 3 the concept of ICI is analytically developed. Section 4 describes the self cancellation scheme. Section 5 describes the comparison between SC technique and standard OFDM system in terms of BER performance.

Abstract This paper studies an efficient ICI Cancellation scheme. The scheme works in two very simple steps. At the transmitter side, one data symbol is modulated on to a group of adjacent sub carriers with a group of weighting coefficient. The weighting coefficients are designed such that the ICI caused by the channel frequency errors can be minimized. At the receiver side, by linearly combining the received signal on these sub-carriers with proposed coefficients, the residual ICI contained in the received signal can then be further reduced Although the redundant modulation causes a reduction in bandwidth efficiency, it can be compensated, for example by using large signal alphabet sizes. Simulation shows that OFDM system using proposed ICI self cancellation scheme perform much better than standard system while having same bandwidth efficiency in multi-path mobile radio channels with large Doppler frequencies. Keywords- OFDM, Inter-carrier interference, Interference coefficients, carrier to interference ratio, BER, QAM

1. Introduction OFDM is an emerging modulation scheme in the current broadband mobile communication system due to high spectral efficiency and robustness to multipath interference[10]. The suitable choice seems to OFDM which is special of multi carrier communication system. OFDM communication system can be seen as either a modulation or multiplexing technique[1,2].

2. OFDM system model In an OFDM system, the input bit stream is multiplexed in to N symbol streams, each with symbol period T and each symbol stream is used to modulate parallel synchronous sub carriers as in [12].

651

National Conference on Advanced Computing and Communication Technology

The sub-carriers are spaced by 1/NTs in frequency, thus they are orthogonal over interval (0 to Ts).

(ISI) where adjacent symbols overlap each other. This is prevented in OFDM by insertion of cyclic prefix between successive OFDM symbols. This cyclic prefix is discarded at the receiver to cancel out ISI. It is due to robustness of OFDM to ISI and multi-path distortion that it has been considered for various wireless applications.

3. Analysis of inter-carrier Interference The main disadvantage of OFDM, however, is its susceptibility to small differences in frequency at the transmitter and the receiver, normally referred to as frequency offset. This frequency offset can be caused by Doppler shift due to relative motion between the transmitter and the receiver, or by the difference between the frequency of the local oscillator at the

Fig.1. Baseband OFDM transceiver system

A typical discrete time baseband OFDM transceiver system is shown in figure 1.First a serial to parallel converter (S/P) groups stream of input bits from the source encoder in to groups of log 2 M bits, where M is the alphabet of the size of the digital modulation scheme employed on each sub-carrier. A total of N such symbols, X m are created . Then the N symbols are mapped to the bins of an inverse fast Fourier transform (IFFT).These IFFT bins corresponds to the orthogonal subcarriers in the OFDM symbol. Therefore OFDM symbol can be expressed as

1 x ( n) = N

N −1

∑X

m =0

m

e

j 2πnm N

Fig.2. Frequency offset model

transmitter and receiver. In this paper frequency offset is modeled as a multiplicative factor introduced in the channel as shown in the figure. The received signal is given by

(1)

y ( n) = x ( n) e

Y ( m) = ∑ y ( n) e

− j 2πnm N

Y (k ) = X (k ).S (0) +

+ W ( m)

+ w(n)

(3)

Where ε is the normalized frequency offset, and is given by ∆fNTs.∆f .is the frequency difference between transmitted and received carrier frequencies and T s is sub-carrier symbol period . w(n) is AWGN introduced in the channel. The effect of this frequency on this received symbol stream can be understood by considering the received symbol Y(k) on kth sub carrier.

Where X m ’s are the baseband symbols on each sub carrier. The digital-to-analog (D/A) converters then creates an analog time domain signal which is transmitted through the channel. At the receiver, the signal is converted back to discrete N point sequence y(n), corresponding to each sub-carrier. This discrete signal is demodulated using Fast Fourier Transform (FFT) operation at the receiver. The demodulated stream is then given by N −1

j 2πnε N

N −1

∑ X (l )S (l − k ) + n(k ) (4)

l = 0 ,l ≠ k

(2)

k = 0,1,2............N − 1

n =0

where N is the total number of sub-carriers, X(k) is the transmitted symbol for kth sub-carrier, n k is FFT of w(n), and S(l-k) are complex coefficient for the ICI components in the received signal. The ICI components are the interfering signals transmitted on subcarriers other than kth sub-carriers. The complex coefficient are given by

Where W(m) corresponds to FFT of the samples of w(n), which is additive white Gaussian noise introduced in the channel. The high speed data rates for OFDM are accomplished by simultaneous transmission of data at lower rate on each of the orthogonal sub-carriers. Because of the low data rate transmission distortion in the received signal introduced by multi-path delay in the channel is not as significant as compared to single carrier high data rate system. Multi-path distortion can also cause inter-symbol interference

S (l − k ) =

652

1 sin(π (l + ε − k )) exp( jπ (1 − )(l + ε − k )) (5) N sin(π (l + ε − k ) / N ) N

National Conference on Advanced Computing and Communication Technology

The carrier to interference (CIR) is the ratio of signal power to the power in the interference component. It serves as indication of good quality and is given below. The derivation assumes that the standard transmitted data has zero mean and the symbols transmitted on different sub-carriers are statistically independent.

CIR =

S (k ) N −1

2

∑ S (l − k )

= 2

l = 0 ,l ≠ k

4. ICI SCHEME

S (0) N −1

2

∑ S (l )

(6) 2

Subcarrier index k Fig.4 Comparison of S (l − k ) , S ' (l − k ) & S ' ' (l − k ) for

l =0

N =64 and ε=0.4

SELF-CANCELLATION

Figure 4 shows a comparison between and

This scheme was introduced by Yuping Zhao and Sven Gustav Häggmann in 2001 in [13] to combat and suppress ICI in OFDM. The main idea is modulate input data symbol on to group of subcarriers with predefined coefficients such that the generated ICI signal within that group cancel each other, hence the name self-cancellation.

S ' (l − k )

S (l − k ) on logarithmic scale. It is seen that

S ' (l − k )