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THE UNIVERSITY OF NEW HAVEN TAGLIATELA COLLEGE OF ENGINEERING Department of Electrical & Computer Engineering and Computer Science

MAXIMUM BOTTLENECK ENERGY ROUTING (MBER) PROTOCOL IN WIRELESS SENSOR NETWORKS

A THESIS submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN ELECTRICAL ENGINEERING

BY Mustafa Hamid Saleh Al-Jumaili University of New Haven West Haven, Connecticut, USA April, 2016

This thesis has been approved for The Department of Electrical & Computer Engineering and Computer Science and the College of Graduate Studies by

[Bijan, Karimi, Ph.D.] Thesis Advisor

[James, Marcus, Ph.D.] Committee Member

[Mohsen, Sarraf, Ph.D.] Committee Member

[Ali, Golbazi, Ph.D.] Department Chairperson

[Ronald, S., Harichandra, Ph.D.] Dean of the College

[Daniel, J., May, Ph.D.] Provost ii

ACKNOWLEDGEMENTS First, I would like to thank my advisor, Professor Bijan Karimi for his suggestions, comments, patience, and understanding during this thesis. The enthusiasm he has for his research motivated me to think like a scientest. I am also very grateful to all my professors during this master’s program for their concerns to teach me as best as they could. I thank my friends for their support. I want to thank the financial support from the Higher Committee of Education Development (HCED) in Iraq who gave me this opportunity. Finally, I must express my gratitude to my parents, brothers, and sisters who have always encouraged me to study science. This accomplishment would not have been possible without all of them.

Mustafa

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ABSTRACT

Wireless Sensor Networks need energy efficient protocols for communication and data fusion to consolidate data and prolong the lifetime of the network. In this thesis, an adaptive hierarchical clustering and routing method based on maximum bottleneck energy routing (MBER) from a node to the sink has been proposed. This method favors the routes with the highest energy. MBER minimizes the possibility of a node dying in route of sending a packet to the sink. Due to its hierarchical structure, data fusion can be applied in different forms and at different levels to send more complete data from each node to the sink, depending on the application. It can be adapted to varying transmission ranges for communication among nodes. Compared to some of the famous protocols in the field MBER’s results show that it outperforms them in terms of the overall lifetime of the network and the number of packets sent to the sink.

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TABLE OF CONTENTS ACKNOWLEDGEMENTS ................................................................................................... IV ABSTRACT.............................................................................................................................. V TABLE OF CONTENTS ....................................................................................................... VI LIST OF TABLES .............................................................................................................. VIII LIST OF FIGURES ............................................................................................................... IX LIST OF ACRONYMS ............................................................................................................ X CHAPTER I: INTRODUCTION .............................................................................................2 1.1

INTRODUCTION ..........................................................................................................2

1.2

NETWORK CHARACTERISTICS ..............................................................................4

1.3

SENSOR NODE STRUCTURE .....................................................................................6

1.3.1 PROCESSING UNIT .................................................................................................................................. 6 1.3.1.1 Microcontroller................................................................................................................................... 6 1.3.1.2 Memory.............................................................................................................................................. 7 1.3.2 COMMUNICATION UNIT .............................................................................................................................. 8 1.3.3 SENSING UNIT ............................................................................................................................................ 8 1.3.4 POWER UNIT .............................................................................................................................................. 9

1.4 1.4.1 1.4.2

WIRELESS SENSOR NETWORKS ARCHITECTURES ........................................ 10 FLAT ARCHITECTURE. .......................................................................................................................... 11 HIERARCHICAL ARCHITECTURE ............................................................................................................ 12

1.5

CLUSTERING IN WSNS ............................................................................................ 14

1.6

WIRELESS SENSOR NETWORK APPLICATIONS ............................................... 16

1.7

PROBLEM STATEMENT .......................................................................................... 16

1.8

ORGANIZATION OF THE THESIS .......................................................................... 17

CHAPTER II: LITERATURE REVIEW .............................................................................. 18 2.1

INTRODUCTION ........................................................................................................ 19

2.2

PROTOCOLS IN WSNS .............................................................................................. 19

2.3

MAC PROTOCOLS..................................................................................................... 21

2.4

ROUTING PROTOCOLS ........................................................................................... 21

2.5

MINIMUM TRANSMISSION ENERGY (MTE) ....................................................... 22

2.6

LOW ENERGY ADAPTIVE CLUSTERING HIERARCHY (LEACH) .................. 23

2.6.1 2.6.2 2.6.3

2.7

SETUP PHASE ................................................................................................................................... 23 STEADY STATE PHASE ....................................................................................................................... 24 ADVANTAGES AND DISADVANTAGES OF LEACH............................................................................... 25

LEACH-CENTRALIZED (LEACH-C) ...................................................................... 26 vi

2.8

MULTI-HOP LEACH (M-LEACH) ........................................................................... 27

2.9

LEACH WITH FIXED CLUSTER (LEACH-F) ........................................................ 27

2.10 POWER EFFICIENT GATHERING IN SENSOR INFORMATION SYSTEMS (PEGASIS) .............................................................................................................................. 28 2.11

HYBRID, ENERGY-EFFICIENT DISTRIBUTED CLUSTERING (HEED)........... 30

2.12

DEEC PROTOCOL ..................................................................................................... 31

CHAPTER III: PROPOSED METHOD................................................................................ 32 3.1

MAXIMUM BOTTLENECK ENERGY ROUTING (MBER) .................................. 33

3.2

ANALYSIS OF ENERGY CONSUMPTION ............................................................. 34

3.3

MBER: THE PROPOSED PROTOCOL .................................................................... 37

3.3.1 3.3.2 3.3.3 3.3.4 3.3.5 3.3.6 3.3.7

SETUP PHASE ................................................................................................................................... 37 CREATION OF THE NETWORK ............................................................................................................ 37 LEVEL DISCOVERY ........................................................................................................................... 37 NEIGHBORHOOD DISCOVERY ............................................................................................................ 38 ROUTE SELECTION ........................................................................................................................... 38 ISOLATED NODES.............................................................................................................................. 41 STEADY STATE PHASE ...................................................................................................................... 41

CHAPTER IV: RESULTS AND ANALYSIS ........................................................................ 42 4.1

SIMULATION .............................................................................................................. 43

4.2

NETWORK LIFETIME .............................................................................................. 45

4.3

ENERGY EFFICIENCY AND PACKET SENT RESULTS ...................................... 46

4.4

DISTRIBUTION ANALYSIS ...................................................................................... 60

CHAPTER V: CONCLUSION AND FUTURE WORK ....................................................... 67 5.1

CONCLUSION ............................................................................................................. 68

5.2

FUTURE WORK ......................................................................................................... 69

REFERENCES ........................................................................................................................ 70 APPENDIX .............................................................................................................................. 74

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LIST OF TABLES Table 1.Parameters for simulation in scenario 1......................................................................... 47 Table 2. Parameters for simulation in scenario 2. ....................................................................... 49 Table 3. Parameters for simulation in scenario 3. ....................................................................... 50 Table 4. Parameters for simulation in scenario 4........................................................................ 52 Table 5. Parameters for simulation in scenario 5. ....................................................................... 53 Table 6. Parameters for simulation in scenario 6. ....................................................................... 55 Table 7. A comparison of protocols performance. ..................................................................... 57 Table 8. Percentage improvement of MBER over other protocols. ............................................. 59 Table 9. Parameters of MBER protocol. .................................................................................... 63

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LIST OF FIGURES Figure 1. 1. Sensor node structure. ..............................................................................................6 Figure 1. 2. Single hop network. ................................................................................................ 11 Figure 1. 3. Flat network architecture. ....................................................................................... 12 Figure 1. 4. Layered architecture of WSNs. ............................................................................... 13 Figure 1. 5. Cluster architecture of WSNs. ................................................................................ 13 Figure 1. 6. Mobile sink architecture. ........................................................................................ 14 Figure 1. 7. A: Single hop clustering architecture, B: Multihop clustering architecture. ............. 15 Figure 1. 8. Multi-tier clustering architecture............................................................................. 15 ……………………………………………………………………………………………………… Figure 2. 1. Routing protocols in wireless sensor networks. ....................................................... 22 Figure 2. 2. LEACH deployment, clusters, cluster heads, alive and dead nodes. ........................ 25 Figure 2. 3. PEGASIS deployment, chain, alive and dead nodes. ............................................... 29 Figure 2. 4. Cluster heads structure in DEEC protocol [42]. ...................................................... 31 ……………………………………………………………………………………………………… Figure 3. 1. MBER structure with uniform random distribution. ............................................... 33 Figure 3. 2. The geometry of MBER network. ........................................................................... 35 Figure 3. 3. Route determination process in MBER. .................................................................. 39 ……………………………………………………………………………………………………… Figure 4. 1. MBER protocol flowchart. ..................................................................................... 44 Figure 4. 2. Energy consumption levels, a dead node with 0% of energy. .................................. 46 Figure 4. 3.Network lifetime, in terms of how fast nodes die, for scenario 1. ............................. 47 Figure 4. 4. Number of packets sent to the sink for scenario 1. .................................................. 48 Figure 4. 5. Network lifetime, in terms of how fast nodes die, for scenario 2. ............................ 49 Figure 4. 6. Number of packets sent to the sink for scenario 2. .................................................. 50 Figure 4. 7. Network lifetime, in terms of how fast nodes die, for scenario 3. ............................ 51 Figure 4. 8. Number of packets sent to the sink for scenario 3. .................................................. 51 Figure 4. 9. Network lifetime, in terms of how fast nodes die, for scenario 4. ............................ 52 Figure 4. 10. Number of packets sent to the sink for scenario 4. ................................................ 53 Figure 4. 11. Network lifetime, in terms of how fast nodes die, for scenario 5. .......................... 54 Figure 4. 12. Number of packets sent to the sink for scenario 5. ................................................ 54 Figure 4. 13. Network Lifetime, Number of packets sent to the sink, for scenario 6. .................. 56 Figure 4. 14. Protocols comparison each with initial of 0.5J per node. ...................................... 58 Figure 4. 15. Protocols comparison each with initial of 1J per node. .......................................... 58 Figure 4. 16. MBER improvement over other protocols. ............................................................ 59 Figure 4. 17. A 3-level network with intermediate circles between consecutive levels. .............. 60 Figure 4. 18. Network lifetime with Rc=5m. ............................................................................. 64 Figure 4. 19. Network lifetime with Rc=10m. ........................................................................... 64 Figure 4. 20. Network lifetime with Rc=23m. ........................................................................... 65 Figure 4. 21. Network lifetime with Rc=40m. ........................................................................... 65 Figure 4. 22. Network lifetime with Rc=50m. ........................................................................... 66 ix

LIST OF ACRONYMS ADC

Analog-to-Digital Converter

ASIC

Application-specific integrated circuit

BS

Base Station

CA

Collision Avoidance

CH

Cluster Head

CPU

Central Processing Unit

CSMA

Carrier Sense Multiple Access

DLL

Data Link Layer

DSP

Digital Signal Processors

FPGA

Field-programmable gate array

ID

Identifier

ISM band

Industrial, Scientific, and Medical radio band

LEACH

Low Energy Adaptive Clustering Hierarchy

MAC

Medium Access Control

MANET

Mobile Adhoc Network

MBER

Maximum Bottleneck Energy Routing

MTE

Minimum Transmission Energy

PEGASIS

Power Efficient Gathering in Sensor Information Systems

QoS

Quality of Service

RAM

Random Access Memory

RF

Radio Frequency

TDMA

Time Division Multiple Access

WSN

Wireless Sensor Network

x

CHAPTER I: INTRODUCTION

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1.1

Introduction A wireless sensor network (WSN) is a network of independent wireless sensor

nodes. It consists of a number of sensor nodes that are deployed over an area of interest in a specific manner. These sensors work with each other to measure some physical phenomenon and send the gathered information to a base station. Sensor networks are different from traditional networks in several aspects. Sensor networks have severe energy constraints, redundant low data rates, and many-to-one flows. The structure of wireless sensor networks basically consists of two types of nodes, the base station node (the sink), mostly located at the middle of the network, and the normal nodes (the nodes) deployed randomly or based on some distribution functions over the network area. The nodes sense the physical phenomenon and send the measured data toward the sink using different types of routing algorithms. Wireless communication progress has driven advancement in wireless sensor networks. WSNs consist of small devices, cooperating with each other to collect information in a specific geographical area. These small devices are the sensors, called nodes and consist of four main parts: Central Processing Unit (CPU), Sensor, Battery, and Transceiver. The sensor node size varies depending on its application. In some cases, such as military or surveillance applications, the sensor node size might be microscopic. Its cost will be different from node to node, depending on its characteristics such as processing speed, memory size, and batteries [1]. WSNs are widely used in many applications in the commercial and industrial areas, such as environment observation, process monitoring, habitat 2

monitoring, health condition tracking, and surveillance [2, 3]. Because of the increasing of its applications significance, several topics in a WSN have been the focus of the researchers' interest, such as the power consumption, routing protocols and algorithms, data gathering and aggregation, the number of hops from source to destination, clustering, localization, and quality of service for both mobile and stationary nodes [4]. Due to the recent increase in the use of wireless sensor networks, the problem of energy constraints has become more important in terms of battery lifetime limitations. Since the nodes’ activities completely depend on the energy, this has become a major concern in wireless sensor networks. If a node has depleted all of its energy, it will be considered as a dead node. This failure can interrupt the application or the entire system [5]. To avoid some serious problems caused by dead nodes, several solutions have been considered to conserve the energy of the sensor nodes and keep a balanced consumption of energy in the network. Some steps can be taken to save energy in a WSN such as scheduling the nodes’ state (i.e., idling, transmitting, receiving, or sleeping), changing the transmission range between the sensing nodes, using efficient routing and data collecting methods, and avoiding the handling of unwanted data as in the case of overhearing [6]. The only source of energy for the nodes in a WSN is the battery. Interacting with other nodes or sensing activities consumes a lot of energy in terms of processing the data and transmitting the collected information to the sink. In some applications such as surveillance applications, it is undesirable or difficult to replace batteries that have been drained of energy. Many researchers are trying to come up with power-aware protocols for WSNs in order to overcome such energy efficiency problems. In this thesis, a routing 3

protocol is proposed to decrease node’s energy consumption and increase network lifetime, where the nodes have the choice to select the route towards the Base Station (BS) based on the highest bottleneck energy of the available routes as will be define in section 3.3.4.

1.2

Network Characteristics A WSN normally consists of a large number of multifunctional sensor nodes,

physically small in size, yet equipped with embedded microprocessors, sensors, and radio transceivers. Beside their sensing capability, they have communication and data processing capabilities. They communicate over a short distance through a wireless medium and work together to accomplish a common task. Different from traditional wireless communication networks, wireless sensor networks have unique limitations and characteristics [7]. • Dense Node Deployment. Wireless sensor nodes are usually densely deployed in a field of interest. Sensor nodes in a sensor network can number in the hundreds, more than that in a Mobile Adhoc Network (MANET). • Severe Energy, Computation, and Storage Constraints. Due to their small size, wireless sensor nodes are highly limited in energy, computation, and storage capacities. • Battery-Powered Sensor Nodes. Wireless sensor nodes are usually powered by batteries. In some situations, they are deployed in a hostile or harsh environment, where it is very difficult or even impossible to change or recharge their batteries.

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• Self-Configurable. Wireless sensor nodes are usually randomly deployed without careful planning and engineering. Once deployed, sensor nodes have to independently configure themselves into a communication network. • Application Specific. WSNs are application specific. Since a sensor network is usually designed and deployed for a specific application, their application design might require a change of the network with the various applications. • Unreliable Sensor Nodes. As wireless sensor nodes are usually deployed in hard-to-reach environments and operate without human attendance, they are prone to physical damages or failures. • Frequent Topology Change. Network topology changes frequently as a result of damage in the network, addition, node failure, energy depletion, or channel fading. • Many - to - One Traffic Pattern. In most wireless sensor network applications, the many - to - one traffic pattern causes energy consumption as the data sensed by sensor nodes flows from multiple sensor nodes to a particular base station. • Data Redundancy. Sensor nodes are densely deployed in an area of interest and collaborate to accomplish a common sense task. Thus, multiple sensor nodes sense a certain level of correlation or redundancy in the data.

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1.3

Sensor Node Structure A sensor node consists of four parts, which are: a battery as a power unit; a

microcontroller as a processing unit; a sensing unit, such as sensor and Analog to Digital Convertor (ADC); and a Radio Frequency (RF) transceiver as a communication unit. These parts will be discussed in details in the next sections. Figure 1.1 shows this structure [4].

Figure 1. 1. Sensor node structure.

1.3.1 Processing Unit A microcontroller and a memory are the main components of a sensor processing unit. 1.3.1.1 Microcontroller It is estimated that up to twenty five percent of the total power budget in a sensor node may be consumed by controllers in their active mode and a higher fraction in idle mode [8]. The controller performs different tasks, such as data 6

processing and functionality control of other segments of the sensor node. While the most widely used controller is a microcontroller, different choices can be utilized as a controller in WSNs: field-programmable gate arrays (FPGAs), general purpose desktop microprocessors, application-specific integrated circuits (ASICs), and digital signal processors. The frequent choice in embedded systems is the microcontroller, because of its low cost, low power consumption, ease of programming, and adaptability to connect to other devices. FPGAs can be reprogrammed and reconfigured based on requirements, yet these take more energy and time than desired [9]. Since general purpose microprocessors usually have a higher power consumption than a microcontroller, they are often considered an inappropriate choice for a sensor [10]. Because of their different functionality per device, ASICs are also not a common choice for WSNs. Digital Signal Processors (DSPs) might be selected for broadband wireless communication applications, but in WSNs the modulation process of wireless communication is often easy. Therefore, the advantages of DSPs are usually less significant to wireless sensor nodes [11].

1.3.1.2 Memory From an energy perspective, the most relevant types of memory are the on-chip memory of a microcontroller and flash memory. In WSNs, off-chip Random Access Memory (RAM) is rarely used. Flash memories are used in WSNs due to their low cost and large storage capacity. Based on the purpose of the storage, there are two categories of memeories in WSNs: user memory and 7

program memory. The former is used for storing application related or personal data, while the latter is used for programming the device. In general, memory requirements are very application dependent [12].

1.3.2 Communication Unit Sensor nodes frequently make use of the Industrial, Scientific, and Medical radio band (ISM band), which provides free radio, global availability, and spectrum allocation. Three possible choices of wireless transmission media in WSNs are optical communication (laser), infrared, and Radio Frequency (RF). Although lasers require less energy, they are sensitive to atmospheric conditions and need line-of-sight for communication. Infrared, similar to lasers, needs no antenna, but its broadcasting capacity is limited. Radio frequency-based communication needs an antenna, yet it is the most relevant in fitting many of the WSNs applications [13]. License-free communication frequencies are commonly used in WSNs: 173, 433, 868, and 915 MHz; and 2.4 GHz [14]. The functionality of both transmitter and receiver can be combined into a single device called a transceiver. The operational states of a transceiver are transmit, receive, idle, and sleep [15].

1.3.3 Sensing Unit One sensor or a set of them are used in the sensing unit as hardware devices that produce a measured response to a change in a physical condition, such as pressure or temperature. A continual analog signal is produced by the sensor. This analog signal is digitized by an ADC and sent to the processing unit for further processing. This sensor 8

node should be autonomous and small in size. They further should consume extremely low energy, operate in high volumetric densities, and adapt to the environment. Since sensor nodes are typically small electronic devices, only a limited power source can be equipped with them. Sensors can be divided into two categories: passive and active. The passive can also be classified into omnidirectional sensors and narrow-beam sensors. Passive sensors measure data without actually manipulating the environment. They are self-powered sensors; their energy is needed only to amplify their analog signal, for example, temperature sensors. Active sensors, such as a radar or sonar sensor, actively probe the environment and they require continuous energy from a power source [16]. Passive narrow-beam sensors are designed to focus their measurement on a well-defined notion of direction, similar to a camera. Passive omnidirectional sensors are designed to work in all directions; thus, their measurements have no notion of direction involved. The overall theoretical work on WSNs has used passive, omnidirectional sensors. Each sensor has a certain area of coverage, mostly a circle, for which it can accurately and reliably report the particular quantity that it is observing [17].

1.3.4 Power Unit WSNs are a suitable solution when it is difficult or impossible to run a main supply to the sensor nodes. An important aspect in the development of WSNs is ensuring that there is always enough energy available to power the system. The sensor node consumes power for physical sensing, data processing and communicating. Sensor power is stored either in batteries or capacitors [18]. Indeed, the main source of power supply 9

for sensor nodes are batteries, both rechargeable and non-rechargeable. Another way to classify batteries is according to the electrochemical material used, such as NiZn (nickelzinc), NiCd (nickel-cadmium), NiMH (nickel-metal hydride), and lithium-ion [19].

1.4

Wireless Sensor Networks Architectures A WSN contains a number of sensor nodes where that number depends on the

area or region that needs to be covered. A base station or data sink at the center of the network or among the sensor nodes receives the data from the nodes and sends it through the Internet to a user. These sinks act like servers or gateways to send the data outside of the WSN. Sending data from the sensor nodes to the sink can be done by a single hop or a long distance radio transmission as in Figure 1.2, which is costly because a lot energy is consumed. The communication consumes more energy than the sensing does. For example, sending one bit of data 100 m away to a receiver needs 3000 instructions. The data traffic should be increased in order to reduce energy [20]. Because it is the same process for sending one bit or a large number of bits at a time.

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Sink

Figure 1. 2. Single hop network.

A multihop or a short distance communication is useful because the sensor nodes are located close to each other. In a multihop, the sensor nodes receive the data and send it along to the next node until it reaches the sink. The multihop architecture can be divided into two types, flat architecture and hierarchical architecture [21].

1.4.1 Flat Architecture. In this approach, all nodes are given the same role in performing a sensing job. They are all peers. Because of the huge number of nodes, it is not feasible to assign a global identifier to each node in this WSN. For this reason, a data centric routing is used. Data centric routing means that the sink sends a query to all the nodes in the area via flooding, and only the sensor node that has data matching that query will respond. Nodes communicate with the sink through a multihop path and use their peer nodes as relays, as in Figure 1.3 [4]. 11

Sink

Figure 1. 3. Flat network architecture.

1.4.2 Hierarchical Architecture Wireless sensor network’s hierarchical architecture is classified into three general architectural categories, namely, layered architecture, cluster architecture, and sensor nodes with mobile sink node architecture. Basic WSN architecture consists of three things. The base station (the data sink) which is the controller and data collector where all sensor nodes in the network send their data. Sensor nodes act as the source of information, and they may also pass messages to each other. Cluster heads are sensor nodes selected to act as collectors of information in their vicinity and send it to the sink [22]. As shown in Figure 1.4, WSN layered architecture is comprised of a sink, in most cases located at the middle of the network, where multiple sensor nodes send their data using one hop, two hops, etc. All sensor node information are collected by the base station.

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Sink Sensor Node Figure 1. 4. Layered architecture of WSNs. As shown in Figure 1.5, the WSN cluster architecture is made up of sensor nodes grouped in clusters where each cluster has one cluster head functioning as a gateway. Each member of the cluster sends its messages to the cluster head and these cluster heads pass on the collected messages to the base station [22].

Sink

Cluster

Cluster Head

Figure 1. 5. Cluster architecture of WSNs. 13

Sensor

As shown in Figure 1.6, in mobile sink WSN architecture, the sink node travels over the sensing area and collects the data from sensor nodes. It either has a predefined route to travel on or has a specific stations to stop in to collect the data. The sink wakes the near nodes around it and collects their data in one hop or in multi hops as in ZebraNet protocol [23].

Sink

Sensor node

Sink movement route

Figure 1. 6. Mobile sink architecture.

1.5

Clustering in WSNs One of the characteristics of WSNs is the density of deployment. This may lead to

some problems in the network traffic and energy consumption. To solve these issues and other aspects a clustering technique is used in WSNs. A node with high energy (in terms of how much energy of the initial energy has left in the node’s battery) will be used as a cluster head while nodes with low energy will do the sensing tasks only. A cluster head gathers the data from the cluster members and sends it to the sink. This technique leads to reduced traffic and energy consumption. 14

There are two clustering strategies based on the communication range between the cluster head and its members. They are single hop clustering and multihop clustering architectures as in Figure 1.7 A and B. Also, one tier or multi-tier are types of clustering hierarchies based on the number of levels. In the multi-tier type, super cluster heads (i.e., cluster heads of normal cluster heads and sensor nodes) are used in addition to normal cluster heads, as in Figure 1.8 [24].

Sink

Sink

A

B

Figure 1. 7. A: Single hop clustering architecture, B: Multihop clustering architecture.

Sink

Figure 1. 8. Multi-tier clustering architecture.

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1.6

Wireless Sensor Network Applications The keys to WSN’s wide range of applications, in both civilian and military

fields, are the availability of low-cost sensors and wireless communication [4]. Using these cheap sensors with the proper applications, WSNs can detect or monitor a variety of physical parameters, such as light, temperature, sound, humidity, pressure, soil composition, air or water quality, size, weight, speed, position and direction [25]. Besides their reduction of the cost of deployment, WSNs can also be applied to any environment, specifically in the environments where conventional wired sensor networks are impossible to deploy, for example, in battlefields, outer space, or deep oceans [4].

1.7

Problem Statement The purpose of this research is to find a protocol that is energy efficient for

wireless sensor networks. Nodes in WSNs are battery operated and used for sensing and collecting data from the area of interest. In most cases, changing the batteries or recharging them in the field is either very costly or impossible (e.g. harsh or surveillance areas) [26]. Each node collects the information and passes it one by one or level by level towards the sink for further actions. Therefore, the energy consumption needs to be considered as a major factor of concern, for a longer lifetime and battery functioning for each node of the network. To perform the communication process between nodes two types of protocols are used in wireless sensor networks: Medium Access Control (MAC) protocols and routing protocols. They are used by the nodes to transfer the collected data toward the base 16

station [5]. MAC protocols try to organize the way nodes access the communication channel in order to reduce the energy consumption in the network. Routing protocols try to organize the way that information is passed to the sink through the network. This research concentrates on improving the energy efficiency of the routing protocols.

1.8

Organization of the Thesis This work consists of five chapters in order to illustrate a better explanation of the

proposed routing protocol: Chapter 1 focuses on the introduction to wireless sensor networks and briefly explains the problem statement. Chapter 2 discusses the work done by other researchers in the field related to this problem. Chapter 3 presents the proposed method and the structure of the proposed WSN. Chapter 4 presents the results of the simulation in comparison to other proposed methods, and the mathematical analysis of the system behaiveours. Chapter 5 presents the conclusion, and offers insight for the future work.

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CHAPTER II: LITERATURE REVIEW

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2.1

Introduction Wireless Sensor Networks have been extensively considered as one of the most

significant technologies for the past decade. They consist of sensors deployed in a physical area and networked through wireless links and the Internet. They provide unprecedented opportunities for a diversity of civilian and military applications. Distinguished from conventional wireless communication networks, such as mobile ad hoc networks and cellular systems, WSNs have unique characteristics: high density node deployment, severe energy constraints, higher unreliability of sensor nodes, storage constraints, and computational limitations. These limitations present various new challenges in the applications and development of WSNs. WSNs have received great attention from both academia and industry. A growing number of research activities have focused on various design and application issues, and significant advances have been made in the deployment and development of WSNs. As WSNs become more commonly used in various civilian and military fields, they will change the way we live, work, and interact with that new physical world [4].

2.2

Protocols in WSNs Medium Access Control (MAC) and Routing protocols perform the

communication process among the sensor nodes. In WSNs two communication types send the data from sensor nodes to the base station, either sent periodic data or sent event-driven data. The MAC layer is basically a sub-layer of the Data Link Layer (DLL). It provides efficient use of the channel to ensure that the sensor nodes can access the

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shared channel without collision. MAC protocols play important roles in Quality of Service (QoS), throughput, energy saving, and delay minimization. On the other hand, routing protocols are considered as a suitable solution for extracting the data from a particular region of the network, such as a cluster. Routing protocols will be discussed in details, but first let’s briefly describe the factors that effect on the working of these protocols. In general, the performance of WSNs is based on the following factors [27]. Latency is defined by how much time a sensor node needs to sense an activity and send it to the destination. It varies depending upon the application, the traffic load, and density of the network. Scalability is the ability of sensor nodes of the network to adjust themselves to the changes to the network structure, so they can deal with the increasing or decreasing of the node number [28]. Energy Awareness is that every node in the network should be aware of how much energy will be utilized depending on the task which needs to be performed. The amount of energy consumed varies depending on the activity, such as sensing, processing, storage, and transmission. Node Processing Time is the time that a sensor node needs to perform an entire operation on sensing an activity, processing the data, and transmitting it over the network. Transmission Scheme is either the flat routing scheme or the multi hop routing scheme, which is used by a sensor node to send the data to the base station. Network Lifetime is time until the first sensor node or group of sensor nodes in the network runs out of energy. 20

Network Power Usage refers to the amount of energy or power that is used by a sensor node or a group of sensors within the network, to form groups (clusters) or perform certain activities.

2.3

MAC protocols MAC protocols are divided into two types: contention-based and contention-free

protocols. In the contention-based type, the protocol allows multiple sensor nodes to access a single channel. Each sensor node has to sense the medium before sending the data. A collision can occur frequently and retransmission may be required. In contentionfree protocols, the channel is divided into time slots, and each sensor node uses the time slot(s) allocated to it to send the data. It provides collision free communication since each node knows in advance about its time slots [29].

2.4

Routing Protocols Routing protocols define the way that the gathered information or the sensors

mesuearments send from a sensor node to the base station. Routing protocols are divided into many categories, such as structure-based and operation-based. Subclasses such as flat, location-based, and Hirerachal based are types of the structure-based. Multipathbased, query-based, QoS based, coherent based and negotiation-based routing come under the operation based class routing protocols as shown in Figure 2.4 [30].

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Routing Protocols in WSNs

Structure Based Protocols

Hierarchal NW Routing

Flat Network Routing

Negotiation Based Routing

Operation Based Protocols

Location Based Routing

Multi-path Based Routing

Query Based Routing

QoS Based Routing

Coherent Based Routing

Figure 2. 1. Routing protocols in wireless sensor networks.

2.5

Minimum Transmission Energy (MTE) MTE is a routing protocol introduced in [31]. The key idea of MTE is that each

node in the network chooses to send its data to the closest neighbor in the direction of the base station. In this protocol when a node dies, all nodes that sent data to this node start sending their data to the next hop neighbor of that dead node. This will keep the same routes of the network and there is no need to form new routes when a node dies. The transmit power of each node adjusts to the minimum required to reach its neighbor. This will help in reducing the interference and the energy dissipation. Carrier Sense Multiple Access (CSMA) MAC protocol is used for the communication between sensor nodes, and if a collision occurs the sent data will be dropped. Each node passes the received data from their neighbors to the next neighbor until the data reaches the BS [32].

22

2.6

Low Energy Adaptive Clustering Hierarchy (LEACH) LEACH protocol is a hierarchical clustering algorithm for sensor networks

introduced by Heinzelman, et al. [33]. It is a cluster-based protocol, which includes distributed cluster formation and randomly selects few of sensor nodes as cluster heads (CHs). The main mechanism in LEACH is to rotate the cluster head selection among all the network nodes to ensure an even distribution of the energy load among all the sensors in the network. The CH nodes compress the data arriving from the member nodes that belong to its cluster and send them in an aggregated packet to the Base Station (BS). This will reduce the amount of information that must be transmitted to the BS. To reduce the collision between the sensor nodes or the CHs a Time Division Multiple Access (TDMA) / code-division multiple access (CDMA) MAC is used. LEACH performs a periodically centralized data collection. Therefore, it is appropriate for constant monitoring applications. The CHs will be rotated in a random fashion in order to obtain a uniform energy dissipation in the sensor network. Only 5% of the sensors act as a CHs based on the author's finding of optimal operation. That basically means that only an average form of 20% of the generated packets will be received by the BS per round. The function of LEACH protocol is separated into two phases, the setup phase and the steady state phase.

2.6.1 Setup phase In the setup phase, all the sensors within a network, organize themselves into some cluster regions as shown in Figure 2.5. During this phase, a predetermined fraction (p) of sensor nodes elect themselves as cluster heads. Each sensor node picks a random 23

number (r) between 0 and 1. Then, depending on a threshold value (T(n)), if this number is less than T(n) the node becomes a cluster head for this round. The threshold value T(n) is calculated based on the equation below, which incorporates the desired percentage of sensor nodes to become cluster heads. The set of sensor nodes that have not been selected as cluster heads will be selected as CHs in the last (1/p) round. Rounds (G), and the current round’s number T(n) is determined as follows: 𝑝

𝑇(𝑛) = { 1−𝑝∗(𝑟𝑚𝑜𝑑1⁄𝑝) 0

𝑖𝑓 𝑛 ∊ 𝐺

(1)

𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

where G is the set of nodes that are involved in the cluster head selection. An elected cluster head broadcasts a message to the rest of the sensor nodes in the network telling them that it has been selected as a new cluster head. All the normal nodes (non-cluster head nodes), receive the advertisement messages from CHs, and make the decision to which CH they want to belong. The node’s decision is based on the signal strength of the CH advertisement message. Normal nodes inform the best CH that they will be a member of the cluster. The best CH is, one has the strongest communication signal. After receiving all messages from the normal nodes that wish to be included in the cluster, the cluster head creates a TDMA schedule and assigns a time slot to each member. This schedule will be broadcast to all sensor nodes in the cluster.

2.6.2 Steady state phase During this phase, the sensor nodes perform both sensing tasks and transmitting data to the cluster heads. After receiving all the data from its cluster members, the cluster head aggregates all the data into one packet and sends it to the Base Station. At the end of

24

the steady state duration, which is determined a priori, the network goes back into the setup phase again and starts selecting new cluster heads.

x Base station, * Cluster heads, o Alive nodes, ٠ Dead nodes Figure 2. 2. LEACH deployment, clusters, cluster heads, alive and dead nodes.

2.6.3 Advantages and disadvantages of LEACH LEACH reduces energy consumption in two ways: first, by minimizing the cost of the communication between sensor nodes and their cluster heads; and second, by turning off the radio of non-head nodes as much as possible [34]. By using data aggregation in LEACH the energy dissipation and latency in data transfer are reduced. Yet, LEACH cannot outperform multihop approaches such as MTE because if there is no correlation between local data and the cluster head, the data from its members cannot be compressed [35].

25

LEACH algorithm uses some characteristics, such as rotating the cluster heads randomly among nodes to achieve balanced energy consumption; all sensors know the beginning of a new cycle through their synchronized clocks. Nodes do not need to be aware of their distance or location information; the setup phase time is non-deterministic and that may lead to unstable setup phase depending on the nodes’ density or collision. No global knowledge of the network is required in LEACH; it uses single hop routing so it is not applicable to large regions’ networks; it requires extra overhead for dynamic clustering and that may diminish the gain in energy consumption [36]. Cluster heads, which are located farther away from the BS consume a larger amount of energy. Also, LEACH does not guarantee good CH distribution and it assumes uniform energy consumption for cluster heads [36]. In this protocol, it is assumed that nodes always have data to send, and the data of nodes which are located close to each other are correlated. It is not clear how the number of predetermined cluster heads (p) is going to be distributed in a uniform fashion over the network area. That means, there will be a possibility that some nodes might not have any cluster head in their vicinity and all of these CHs are deployed in one part of the network [37].

2.7

LEACH-Centralized (LEACH-C) A centralized clustering algorithm is used in LEACH-C [35]. This algorithm

modifies the setup phase of LEACH and keeps the steady state the same. In the setup phase, each sensor node has to send its energy level and current location to the base station. The base station takes the responsibility of determining the clusters, the CH 26

nodes, and the normal nodes of each cluster. Depending on the global information of the BS about the entire network, better clusters with less energy consumption are produced. Different from LEACH, the number of CHs in each round of LEACH-C is equal to a predetermined optimal value; while in LEACH, it varies from round to round due to lack of global information among the sensor nodes.

2.8

Multi-hop LEACH (M-LEACH) Multi hop communication between the sensor nodes within the cluster is used in

M-LEACH [38], to increase the energy efficiency of the protocol. The authors of [39] extend this solution by using multi hop between the cluster heads (inter-cluster communication). This is a useful solution for the WSNs where the direct communication between cluster heads or the BS is not possible due to the large distance between them. The novelty of the proposed solution is that the multi hop approach uses intra-cluster and inter-cluster communication, as well as the data fusion to reduce the overall energy consumption.

2.9

LEACH with Fixed Cluster (LEACH-F) Fixed clusters with rotating cluster heads are used in LEACH-F [33]. The clusters

are formed once and remain the same for the entire life of the network. The CH rotates among the sensor nodes within the same cluster. This solution gets rid of the repeated setup overhead at the beginning of each (n) round. The same centralized cluster formation algorithm as in LEACH-C is used here to form the clusters. However, LEACH-F does not allow the system to add new nodes or adjust their behavior based on nodes dying. 27

2.10 Power Efficient Gathering in Sensor Information Systems (PEGASIS) PEGASIS is a WSNs protocol developed by the authors of [40]. The idea of PEGASIS is that each sensor node of the network communicates with its closest neighbor and sends the data to the base station by forming a chain as in Figure 2.6. All sensor nodes in the network gather information from their vicinity and fuse it with the data coming from their neighbor and send it to the nearest neighbor. After forming the chain, every node in the chain takes a turn as a leader of the chain by sending the whole fused data, which is collected by the sensor nodes in the chain, to the BS. This way the amount of energy consumed by each sensor node is reduced. PEGASIS protocol uses greedy algorithms to ensure that all sensor nodes are used during the formation of the chain. Nodes do not necessarily know their neighbors and the chain can be formed by the base station. If the nodes have to determine their neighbors, they would use the signal strength to do it. Each sensor node sends a signal and receives a response from its neighbors; the node picks the strongest signal node as its next neighbor, and then it adjusts its signal in such a way that it hears only that neighbor in the network. If any sensor node dies in the chain, a new chain has to be formed and the dead node should be eliminated.

28

o Alive nodes, o Dead nodes,

Chain

Figure 2. 3. PEGASIS deployment, chain, alive and dead nodes.

PEGASIS protocol improves on LEACH protocol by saving energy since it does not need it for cluster heads or cluster member forming. All sensor nodes have a chance to become a chain leader once and send the collected data to the BS. Based on the results of the simulation reported in [40], PEGASIS protocol outperforms LEACH protocol by reducing the amount of dead nodes, increasing the lifetime of the network, less energy dissipation, and higher performance [36]. PEGASIS avoids the clustering overhead, yet it still needs dynamic topology adjustment since a sensor node needs to know its neighbors’ energy status to make the decision where to send its data. This will lead to a significant overhead. This protocol assumes that all sensor nodes have a complete table of the locations of all network nodes, but it is not outlined how the nodes’ locations are obtained. Moreover, PEGASIS introduces an extreme delay for distant nodes on the chain. Also, it uses a single leader of 29

the chain and that could be a bottleneck, because this single leader has to pass all the data of all nodes in the network [37].

2.11 Hybrid, Energy-Efficient Distributed Clustering (HEED) HEED [41] protocol has extended the basic scheme of LEACH by using residual energy and node degree or density as a metric for cluster selection. It is using an adaptive transmission power in the communication of inter-clustering. The proposed algorithm of HEED, periodically selects cluster heads according to a combination of two clustering parameters. The first parameter is the residual energy of each sensor node. This parameter is used in calculating probability of becoming a cluster head. the second parameter is the cost of intra-cluster communication as a function of node degree (number of neighbors of the node ) or cluster density. The formar parameter is used to probabilistically select an initial set of cluster heads while the latter parameter is used for breaking ties. Each node does the calculations of cluster head probability using the residual energy and the maximum energy (initial energy) independently. If a sensor node is selected to become a cluster head, it broadcasts an announcement message as a tentative cluster head or a final cluster head. A sensor node hearing the cluster head list selects the cluster head with the lowest cost from this set of cluster heads.

30

2.12 DEEC Protocol Design of a distributed energy-efficient clustering algorithm (DEEC) [42] is a distributed energy efficient clustering scheme for heterogeneous wireless sensor networks. In this protocol, the cluster-heads are elected by a probability based on the ratio between the residual energy of each node and the average energy of the network. In DEEC protocol, all the nodes need to know the total energy and lifetime of the network, which can be determined a priori. The sink could broadcast the total energy and estimate value of a lifetime to all nodes. At the beginning of a new epoch, each node will use this information to compute its average probability, which is used to decide if this node should be a cluster head in the current round. DEEC use the average energy of the network as the reference energy. Another improvement of DEEC protocol has been introduced in [43], which is called DDEEC. This development technique is based on changing dynamically and with more efficiency the cluster head election probability.

Figure 2. 4. Cluster heads structure in DEEC protocol [42].

31

CHAPTER III: PROPOSED METHOD

32

3.1

Maximum Bottleneck Energy Routing (MBER) MBER is a general method for routing in WSNs. Basically, a WSN will be

divided into concentric circles, where each circle represents a level of the network, with one sink located at the center of the network area, as shown in Figure 3.1.

Figure 3. 1. MBER structure with uniform random distribution.

The setup phase will start from the inner level where each node informs its neighbors in the next higher level of its ID, energy, and the best path to the sink (in terms of highest energy bottleneck path). Nodes in the next level compare the received bottleneck energy with their energy and send the minimum of the two to the next level as 33

the bottleneck energy of this route. This process will be repeated until the outer level is reached. Nodes in the outer level of the network select the route with the maximum bottleneck of energy as its path to the sink. Further explanation is in section 3.3.

3.2

Analysis of Energy Consumption For the energy model used in the sensor nodes, it is similar to the model that used

in [35] for comparison purposes. The energy dissipation is divided into three stages. The first stage is the transmitter dissipation, where the transmitter needs energy to run the radio electronics and the power amplifier. Second, the receiver dissipation, where the receiver needs to run the radio electronics. Third, the processing dissipation, where each cluster head needs an amount of energy to perform the data aggregation before sending it to the next level. Two channel models are used in the simulation of this research: the free space model and multipath fading model. In the free space model, the amplifier power is multiplied by the squared distance from the sender to the receiver. In the multipath fading model, the amplifier power is multiplied by the (distance) 4 from the sender to the receiver [44]. The former is used when the communication between two nodes is with a distance of 87 meters or less [32]; and the latter is used if the communication is greateror 87 meters [21], which is used only if a node needs to transmit directly to sink. Figure 3.2 shows the geometry of the network. In LEACH, some important assumptions are made as reasonable assumptions based on the advances in radio hardware and low-power computing to get the LEACH produce the reported results. These assumptions are that all nodes: 1) can transmit with enough power to reach the base station (BS) if needed; 2) use power control to vary the 34

amount of transmit power; and 3) that each node has computational power to support different MAC protocols and perform signal processing functions. The same assumptions are made in MBER method for comparison purposes.

Rc

BS

Rt

Figure 3. 2. The geometry of MBER network.

Five different equations are used for energy calculations in this project. Assume: Rc = The normal communication range of each node. Rt = The entire range of the network. d = The distance between two nodes in the range 87m or less. D = The distance between two nodes which are apart more than 87m. TxE = Transmission Energy used per bit. RxE = Receiving Energy used per bit. FsE = Free space amplifier Energy per bit per square meter. 35

MpE = Multipath fading amplifier Energy per bit per square meter. AgE = Aggregation Energy (the energy needed to form one bit of a consolidated frame) per bit. Bits = Number of bits in one packet. The first equation which is used in the transmission between two normal nodes in their normal communication range (Rc) of a distance of 87m or less. TxE (Bits) = (TxE * Bits) + (FsE* Bits*d2)

(2)

A second equation which is used in the transmission between two normal nodes where the distance is greater than 87m. TxE (Bits) = (TxE * Bits) + (MpE * Bits*D4)

(3)

third equation which is used in the transmission between two cluster heads or cluster head and the sink in 87m or less distance. TxE (Bits) = ((TxE+ AgE) * Bits)) + (FsE* Bits*d2)

(4)

Fourth equation which is used in the transmission between two cluster heads or a cluster head and the sink where the distance is greater than 87m. TxE (Bits) = ((TxE+ AgE) * Bits)) + (MpE * Bits*D4)

(5)

Finally, the equation that used for the receiving of any packet is the same in all above cases. RxE (Bits) = (RxE * Bits)

(6)

36

3.3

MBER: The Proposed Protocol The process of this protocol has been divided into two phases, the setup phase and

the steady state phase.

3.3.1 Setup Phase

This phase consists of four sub-phases: creation of the network, level discovery, neighborhood discovery, and route selection.

3.3.2 Creation of the Network All the parameters need to be set in this phase, the area of the network, the number of sensor nodes, and the initial energy. Then the base station has to be created, which, in our case, is located in the middle. After that, the nodes have to be created in a number that is specified in the parameter setting. Network nodes are deployed in a random uniform fashion. This will ensure a sufficient coverage over the entire area. Also, the number of levels will be calculated according to the following equation: #Levels =

√(𝑥/2)2+(𝑦/2)2

(7)

𝑅𝑐/2

3.3.3 Level Discovery In this phase, the sink node sends a level- discovery message with power for radius of Rc/2 in which the level is set to 1. All nodes receiving this message respond with their node ID using a MAC layer protocol such as CSMA/CA or CDMA. Also, each node marks itself a member of that level. Sink node increases power to cover previous 37

radius plus Rc/2 and increases the level by 1. This process is repeated until all levels are covered based on the maximum radius specified for the network. The sink node schedules TDMA for each level and each node based on ID and level.

3.3.4 Neighborhood Discovery According to the schedule specified by the sink, the nodes in the inner most circle send neighborhood-discovery messages of radius RC to their neighbors along with their remaining power and the level “1”. Nodes in level 2 receiving the message in the previous step record the remaining power and the node ID of the sender. Each node in level 2 keeps the ID and the energy of the node from level 1 with the highest energy. The energy that a node in level 2 reports to nodes in level 3 will be the minimum of its remaining power and the energy it has recorded as the maximum energy was reported by the level 1 node. This is called maximum bottleneck energy. The bottleneck energy of any route is the node in the route with lowest energy. Nodes in other levels repeat the same procedure outlined above until all levels, with the exception of most outer level, complete this procedure.

3.3.5 Route Selection This phase starts with the nodes in the most outer level with TDMA scheduled by the sink. A node in level l sends a message to a node using the ID for that node in level l-1 informing that node that it has chosen that node as its cluster head because it has reported the maximum bottleneck energy for reaching the sink. This process will be repeated for all nodes starting from most outer level nodes up to level 1. Levels 1 and 2 are combined in simulation because nodes in both levels can reach the sink directly. 38

The following outlines the process of determining the route to the sink for a node. The definition of the “Next Hop” and “Bottleneck” functions as follows: Next Hop: In this function, a node records the energy (and the ID) of a node in the immediate lower level that has reported the highest bottleneck energy among all nodes from that level that have reported their bottleneck energy. Bottleneck: In this function, a node calculates the minimum of its energy and the maximum bottleneck energy that has been reported to it from a node in the immediate lower level. This value will be recorded as the bottleneck energy of this node and later will be reported to the nodes within radius of Rc in the immediate higher level so they can use it to determine their maximum bottleneck energy.

10

Level 1

BS

2

Level 2

4

Level 3

Level 4

7 5 3 6

Figure 3. 3. Route determination process in MBER.

39

For level one, nodes 2 and 3 are the lowest level, as illustrated in Figure 3.3. Their functions are set as follows: Next Hop (2) = max (Energy (BS), 0), Bottleneck (2) = min (Energy (2), infinity) Next Hop (3) = max (Energy (BS), 0), Bottleneck (3) = min (Energy (3), infinity) For level two, it is assumed that node 4 and 5 receive messages regarding the remaining energy or bottleneck energy from both 2 and 3: Next Hop (4) = max (Bottleneck (2), Bottleneck (3)) Bottleneck (4) = min (Next Hop (4), Energy (4)) Next Hop (5) = max (Bottleneck (2), Bottleneck (3)) Bottleneck (5) = min (Next Hop (5), Energy (5)) For level three, nodes 6 and 7: Next Hop (6) = max (Bottleneck (5)) has only one neighbor (N5) Bottleneck (6) = min (Next Hop (6), Energy (6)) Next Hop (7) = max (Bottleneck (4), Bottleneck (5)) Bottleneck (7) = min (Next Hop (7), Energy (7)) For level four, node 10: Next Hop (10) = max (Bottleneck (7)) has only one neighbor (N7) Bottleneck (10) = min (Next Hop (10), Energy (10))

40

3.3.6 Isolated nodes After the above process, there might still be some isolated nodes. An isolated node is either a node that has no neighbors in the immediate higher and lower levels or it can be reached from higher level but does not have a neighbor in the lower level. Many possible solutions could be contemplated depending on the node’s level and whether it is a cluster head or a normal node. Since the isolated node solution is not the focus of this research, one simple solution has been considered. If the node is an isolated node, it has to communicate with the sink directly.

3.3.7 Steady State Phase In this phase, data transmission from a node to the sink is considered. All nodes of the network know their next hop cluster head from the information provided in routing selection section. This process can be done in different ways. One is that each node in the network sends a fixed-size packet of data in each round. Since all nodes have to send the same sized packet, including the cluster heads, each cluster head node needs to perform a data fusion on the received packets from its cluster members. In this case, all received packets in one cluster will be compressed into one packet and sent to the next level (lower level cluster head in the network). Collected data will pass through different paths toward the sink because the route will be re-determined in every n-rounds, which is specified by the user. Different ways of data transfer have been considered in this research depending on the level of the nodes, the degree of the data fusion, and other factors that will be further discussed in the next chapter. 41

CHAPTER IV: RESULTS AND ANALYSIS

42

4.1

Simulation Obviously, simulation in software can be the way of reaching a research goal, or

at least can make the whole process faster and easier. Many types of simulators are used for implementing WSNs [45], each with its own features. Because of its flexibility to implement a wide range of applications, using the MATLAB environment for WSN applications has been validated [46]. MATLAB is used in this research to implement and simulate a WSN that meets the discussed assumptions. A script has been written for the implementation, obtaining the results, and verification of the theory assumptions of this study. The results show that the MBER protocol outperforms other considered protocols in terms of prolonging network lifetime and sending more packets to the base station. Figure 4.1 shows the operation flowchart of MBER. The Matlab code for MBER is available in Appendix A.

43

Figure 4. 1. MBER protocol flowchart. 44

MBER algorithm: I. Initialize 1. Set x, y, max. Rounds, initial energy, number of nodes, etc... 2. N(i) {i: i lies within total number of nodes} 3. BS { the base station located at the center of the network} 4. BS sends level number for each N(i) 5. BS broadcast Neighborhood Discovery Message to all nodes II. Repeat 1. If (R=1, 10, 20…) 2. N(i) picks Next Hop {Next Hop= max (energy of N(j)neighbors)} 3. N(i)sends min (N(i)Energy, N(j)Next Hop) to higher level neighbor nodes 4. Else If N(i) has a Next Hop 5. N(i) sends packet to Next Hop 6. Else 7. N(i) sends packet to BS III. Finalize 1. If (R> max round, TRUE) 2. End the program 3. Else 4. Go to II.Repeat

4.2

Network Lifetime The time until the first sensor node or group of sensor nodes in the network runs

out of energy is called the network lifetime. In this study, the performance of the network has been considered depending on the first node that dies (meaning the first node that runs out of energy), as depicted in Figure 4.2, and the last node that dies (meaning all network nodes are running out of energy). Simulation results indicate that MBER has extended the network lifetime in terms of delaying the death of the first node and the last node compared to other protocols in the field. This means more and accurate data goes to the BS, longer time for the whole network to work, and more applications that can be implemented.

45

Figure 4. 2. Energy consumption levels, a dead node with 0% of energy.

4.3

Energy Efficiency and Packet Sent Results To show the efficiency of MBER protocol for variety of applications different scenarios of initial energy, network size and packet length have been considered. Table 1 and Figures 4.3 and 4.4 show the parameters and the results of the first simulation scenario. In scenario 1, MBER shows superior performance over MTE in terms of the lifetime of the network and the number of packets sent to the sink. As with MTE, the same assumption in terms of aggregation of data into one packet of 2000 bits sized for delivery to the sink, not including the initialization energy, the same number of nodes is 100 nodes; the same area 100m*100m and the same initial energy 0.5 Joule per node, are made.

46

Table 1.Parameters for simulation in scenario 1. Parameter Number of nodes Area Initial Energy Initialization Energy Rt Rc Packet length Cluster head select Total rounds

MBER 100 100m*100m 0.5 J per node Not considered 50m 5m 4000 bits Every round 5000

MTE 100 100m*100m 0.5 J per node Not considered 4000 bits Every round 5000

Figure 4. 3.Network lifetime, in terms of how fast nodes die, for scenario 1.

47

Figure 4. 4. Number of packets sent to the sink for scenario 1.

Table 2, Figure 4.5, and Figure 4.6 show the parameters and the results of the second simulation scenario. In Scenario 2, MBER has been compared to LEACH. MBER again, shows superiority in performance in terms of the lifetime of the network and the number of packets sent to the base station. The same assumptions have been made for both protocols as illustrated in Table 2. As can be seen in Figure 4.5, MBER has extended the lifetime of the network by about 300 rounds for the first dead node and around 1500 rounds for the last dead node. In addition, Figure 4.6 shows the superiority of MBER over LEACH in terms of the number of packets sent to the sink. Using MBER instead of Leach gives the user more information and accurate detailes of the system at the sink end.

48

Table 2. Parameters for simulation in scenario 2. Parameter Number of nodes Area Initial Energy Initialization Energy Rt Rc Packet length Cluster head select Total rounds

MBER 100 100m*100m 0.5 J per node Not considered 50m 5m 2000 bits Every round 6000

LEACH 100 100m*100m 0.5 J per node Not considered 2000 bits Every 10 rounds 6000

Figure 4. 5. Network lifetime, in terms of how fast nodes die, for scenario 2.

49

Figure 4. 6. Number of packets sent to the sink for scenario 2.

For scenario 3, Table 3, Figure 4.7, and Figure 4.8 show the parameters and simulation results under the same initial conditions as the previous simulation, but with a different network size and a value for Rc. Again, the results clearly show that MBER outperforms LEACH.

Table 3. Parameters for simulation in scenario 3. Parameter Number of nodes Area Initial Energy Initialization Energy Rt Rc Packet length Cluster head select Total rounds

MBER 100 250m*250m 0.5 J per node Not considered 125m 30m 2000 Every round 6000

50

LEACH 100 250m*250m 0.5 J per node Not considered 2000 Every 10 rounds 6000

Figure 4. 7. Network lifetime, in terms of how fast nodes die, for scenario 3.

Figure 4. 8. Number of packets sent to the sink for scenario 3.

In scenario 4, the initialization energy was considered for MBER but not for LEACH because it is not outlined in LEACH paper [35]. Even under this condition, MBER outperformed LEACH in terms of last node die and the number of packets received at the sink side, as can be seen in Table 4, Figure 4.9, and Figure 4.10. 51

Table 4. Parameters for simulation in scenario 4. Parameter Number of nodes Area Initial Energy Initialization Energy Rt Rc Packet length Cluster head select Total rounds

MBER 100 100m*100m 0.5 J per node Considered 50m 10m 2000 bits Every round 6000

LEACH 100 100m*100m 0.5 J per node Not considered 2000 bits Every 10 rounds 6000

Figure 4. 9. Network lifetime, in terms of how fast nodes die, for scenario 4.

52

Figure 4. 10. Number of packets sent to the sink for scenario 4. By increasing the data packet length and value of Rc as well as keeping the previous assumptions in Scenario 5, MBER still performs better than LEACH. Even though the two protocols almost have the same time for the first dead node, MBER has a much better time for the whole network lifetime in terms of the last dead node, as can be seen in Table 5, Figure 4.11, and Figure 4.12. In summary, MBER protocol outperformed LEACH protocol in different scenarios, in different areas, and under different conditions. Table 5. Parameters for simulation in scenario 5. Parameter Number of nodes Area Initial Energy Initialization Energy Rt Rc Packet length at sink Cluster head select Total rounds

MBER 100 100*100m 0.5 J per node Not Considered 50m 10m 4000 bits Every rounds 5000 53

LEACH 100 100*100m 0.5 J per node Not considered 4000 bits Every 10 rounds 5000

Figure 4. 11. Network lifetime, in terms of how fast nodes die, for scenario 5.

Figure 4. 12. Number of packets sent to the sink for scenario 5.

54

Scenario 6, involves MBER versus DEEC and DDEEC. The parameters in Table 6 have been used in this scenario to make the comparison among MBER and both of DEEC and DDEEC protocols. From Figure 4.13, it is clear that MBER has outperformed these protocols in network lifetime and in data packets sent to the base station. Table 6. Parameters for simulation in scenario 6. Parameter Number of nodes Area Initial Energy Initialization Energy Rt Rc Packet length Cluster head select Total rounds

MBER 100 100m*100m 0.5 J per node Not considered 50m 5m 4000 bits Every rounds 5000

55

DEEC &DDEEC 100 100m*100m 0.5 J per node Not considered 4000 bits Every 10 rounds 5000

Figure 4. 13. Network Lifetime, Number of packets sent to the sink, for scenario 6.

A table of comparison has been considered, as in Table 7, to show the performance of some protocols in the field and compare their results to the MBER protocol.Some of these protocols codes can be find on [47], [48].Table 7 shows the performance of each protocol in terms of number of rounds until the first node dies (1%) , ten nodes die (10%), twenty nodes die (20%), half of the network nodes die (50%), and all the network nodes die (100%). Two values of initial energy per node have been used, 56

which are 0.5 Joule and 1 Joule. In all cases, the number of bits of the message stay the same at 2000 bits. Also, the number of nodes is 100 nodes, and the area is 100m*100m for all protocols. The base station is located at the center of the network at coordinates (50, 50) for all simulations. As shown in Figures 4.14 and 4.15, MBER outperforms all the considered protocols with different values of superiority over each. Table 7. A comparison of protocols performance. Initial Energy (J/Node)

0.5

1

Packet Length (bits)

Protocol

Percentage of dead nodes 10% 20% 50%

1%

2000

MTE PEGASIS LEACH DEEC MBER

25 713 1199 1240 1612

74 1680 1280 1349 2147

120 1846 1791 1549 2378

454 2058 1987 2209 3860

2271 2743 3138 4323 4993

2000

MTE PEGASIS LEACH DEEC MBER

45 2012 3090 3106 3223

123 3797 3741 3853 4397

211 3949 4050 4196 4741

907 4191 4676 5260 7574

4545 4859 5616 8363 9970

100%

Number of rounds until the above percentages appear

57

5000

Rounds

4000 3000

2000 1000 0 1%

10%

20%

50%

100%

Percentages of dead nodes MTE

PEGASIS

LEACH

DEEC

MBER

Figure 4. 14. Protocols comparison each with initial of 0.5J per node. MTE

PEGASIS

LEACH

DEEC

MBER

12000 10000

ROUNDS

8000 6000 4000 2000 0 1%

10%

20%

50%

100%

PERCENTAGES OF DEAD NODES

Figure 4. 15. Protocols comparison each with initial of 1J per node.

Finally, from the results in Table 7, the following percentage improvement of MBER over other protocols can be found in Table 8. LEACH protocol has been used as a reference in comparison between MBER and HEED because we do not have the code for HEED protocol. It is mentioned in [41] that HEED protocol has outperformed LEACH 58

by 8% , this percentage has been used for HEED over LEACH. Figure 4.16 shows the percentages improvement of MBER over other protocols in terms of last node death with consideration of two initial (energy/node) values. Initialization energy for cluster heads selection is not considered for any of the methods in this analysis. Table 8. Percentage improvement of MBER over other protocols. Protocol

MBER improvement over each one with 0.5 (J/Node) 119.8% MTE PEGASIS 82.03% 59.1% LEACH 51.1% HEED 15.1% DEEC

MBER improvement for 0.5 (J/Node)

MBER improvement over each one with 1 (J/Node) 119.3% 105.1% 77.5% 69.5% 19.2%

MBER improvement for 1 (J/Node)

IMPROVEMENT

150.00% 100.00% 50.00% 0.00% MTE

PEGASIS

LEACH

HEED

DEEC

PROTOCOLS

Figure 4. 16. MBER improvement over other protocols.

59

4.4

Distribution Analysis In this section, the conditions for delaying the death of the first and last nodes and

distributing the assignment of cluster head to different nodes are discussed. Such that prolonging the lifetime of the network, and having almost the same lifetime for all nodes can be achieved. Figure 3.4 shows a 3-level network with intermediate circles between consecutive levels.

Figure 4. 17. A 3-level network with intermediate circles between consecutive levels. Definitions: L=# of levels (circles) in the network Rt =Maximum radius of the network from the sink Rc/2=radius of circle for level 1 Rc/2= increment in radius for level 2 and above Ri=radius of the circle for level i=iR c/2 for i=1 to L N= #of nodes in the network NA=#of nodes in area A NLi=#of nodes in level i AiRc/2 = Area of circle with radius iRc/2 which is πi2R2c /4 where i is the level number Ai=Area of level i 60

This leads to the following: Total area of the network is πRt2. The area for level i is: Ai = AiRc/2 -A(i-1)Rc/2= π[i2 (Rc)2/4 – π((i-1)2 (Rc)2/4)] = π(Rc)2 (2i-1)/4 for i=1 to L

(8)

Assuming uniform distribution of nodes, the probability that a node is in area A is: PA= A/ π(Rt)2

(9)

The Expected value of the number of nodes in a region with area equal to A is: 2 E[NA]=∑𝑁 𝑖=1(𝑃𝐴𝑖 ∗ 1 + (1 − 𝑃𝐴𝑖) ∗ 0) = (N*A)/π(Rt)

(10)

The Expected value of number of nodes in level i is: E[NAi]= N[π(Rc)2(2i-1)/4]/ π(Rt)2 = N(2i-1)(Rc)2/4(Rt)2 for i=1 to L

(11)

For a uniform distribution, the probability that a node in level i is within x distance on the line connecting that node to the sink from the circle for level i-1 is: (x)/(Rc/2)=2x/Rc

(12)

The Expected value of the distance of a node in level i from the circle for level i-1 is Rc/4 as shown below: 𝑅𝑐/2

E[dli-1]= ∫0

(2𝑥/𝑅𝑐)𝑑𝑥 = Rc2/4Rc=Rc/4

(13)

The Expected value for the distance between two nodes in level i [E(dNAi)] is: [The circumference for the mid circle between level i-1 and level i] /[Expected value for number of nodes in level i] 61

(14)

The following shows the calculation of this entity: Radius for mid circle = (iRc/2) - Rc/4 = Rc(2i-1)/4 E(dNAi) = 2π[Rc(2i-1)/4] /[N(2i-1)Rc2/4Rt2] = (2π/N) Rt (Rt/Rc)

(15)

The E(dNAi) is a constant value, regardless of the position of the nodes. The expected distance between a node in level i and the closest node in level i-1 is Rc/2. The expected distance, Rnc, between a node in level i and the next closest node in level i-1, as shown in Figure 4.17, is approximately: Rnc={[(2π/N) Rt (Rt/Rc)]2 + (Rc/2)2}1/2

(16)

If the above value is greater than Rc, which is the communication range of a node, then approximately the same set of nodes will be selected for sending data from a node in level i to the sink. It is because only the node(s) on the Rc/2 line will be in the Rc range. This means that a rather uniform distribution of load on nodes between the outer circle and the sink will exist and indicates that nodes will die rather late and almost at the same time which is a property we would like to see in a WSN. The following shows under what condition this may occure: {[(2π/N) Rt (Rt/Rc)]2 + (Rc/2)2}≥Rc2 or Rc ≤1.07 Rt (2π/N)1/2  Rc ≤ (2.69Rt) /(N)1/2

(17)

For example, if N=100 and Rt=87m then the simulation should show better results for Rc