Computers and Software

ISSN 1828-6003 Vol. 8 N. 4 April 2013

International Review on

Computers and Software (IRECOS)

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Contents:

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Entropy Based Approach to Prevent the DDoS Attacks for Secured Web Services by S. Igni Sabasti Prabu, V. Jawahar Senthil Kumar

888 892

Enhancement of Game Theoretic Approach Using Nash Equilibrium with Markov Chains for VANET Security by Prabakaran M., A. R. Deepti, G. Mahadevan, R. M. S. Parvathy

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A Novel Approach for Hiding Image Using Pixel Intensity by M. Shobana, P. Gitanjali, M. Rajesh, R. Manikandan

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An Optimal Architecture for Dynamic Web Service Discovery and Selection by Abdallah Missaoui

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Dynamic Extended Rectangle Based Method for 3D Visual Scene Segmentation by N. Charara, , M. Sokhn, I. Jarkass, O. Abou Khaled, E. Mugellini

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Sensitive Information Protection and Recovery Using One to One Compound Mapping by N. V. Chaithanya Kumar Reddy, Diwakar R. Marur, Vidhyacharan Bhaskar

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Review of Methods of Distributed Barrier Synchronization of Parallel Processes in Matrix VLSI Systems by Jamil S. Al-Azzeh

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Classification of Ultrasound Carotid Artery Images Using Texture Features by Dhanalakshmi Samiappan, Venkatesh Chakrapani

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Fuzzy Based Congestion Detection Technique for Queuing in IP Networks by S. Nandhini, S. Palaniammal

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Combining UML Class and Activity Diagrams for MDA Generation of MVC 2 Web Applications by M’hamed Rahmouni, Samir Mbarki

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Digital Image Watermarking and Encryption Using DWT and RSA by Omar Alirr, Kasmiran Jumari

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Study on Multi-Agent Q Learning Based on Prediction by Ya Xie, Zhonghua Huang

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The Implementation of Plagiarism Detection System in Health Sciences Publications in Arabic and English Languages by K. Omar, B. Alkhatib, M. Dashash

(continued on inside back cover)

Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved

International Review on Computers and Software (IRECOS) Managing Editor: Santolo Meo Department of Electrical Engineering FEDERICO II University 21 Claudio - I80125 Naples, Italy [email protected]

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Marlin H. Mickle

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Vijay Bhatkar

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Dimitris Nikolos

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Arndt Bode

(Germany)

Mohamed Ould-Khaoua

(U.K.)

Rajkumar Buyya

(Australia)

Witold Pedrycz

(Canada)

Wojciech Cellary

(Poland)

Dana Petcu

Bernard Courtois

(France)

Erich Schikuta

(Austria)

Andre Ponce de Carvalho

(Brazil)

Arun K. Somani

(U.S.A.)

David Dagan Feng

(Australia)

Miroslav Švéda

(Czech)

Peng Gong

(U.S.A.)

Daniel Thalmann

(Switzerland)

Defa Hu

(China)

Luis Javier García Villalba

(Spain)

Michael N. Huhns

(U.S.A.)

Ismail Khalil

(Austria)

Catalina M. Lladó

(Spain)

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Marios Angelides

(Romania)

(Australia)

Lipo Wang

(Singapore)

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Brijesh Verma

The International Review on Computers and Software (IRECOS) is a publication of the Praise Worthy Prize S.r.l.. The Review is published monthly, appearing on the last day of every month. Published and Printed in Italy by Praise Worthy Prize S.r.l., Naples, April 30, 2013. Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved.

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This journal and the individual contributions contained in it are protected under copyright by Praise Worthy Prize S.r.l. and the following terms and conditions apply to their use: Single photocopies of single articles may be made for personal use as allowed by national copyright laws. Permission of the Publisher and payment of a fee is required for all other photocopying, including multiple or systematic copying, copying for advertising or promotional purposes, resale and all forms of document delivery. Permission may be sought directly from Praise Worthy Prize S.r.l. at the e-mail address: [email protected] Permission of the Publisher is required to store or use electronically any material contained in this journal, including any article or part of an article. Except as outlined above, no part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior written permission of the Publisher. E-mail address permission request: [email protected] Responsibility for the contents rests upon the authors and not upon the Praise Worthy Prize S.r.l.. Statement and opinions expressed in the articles and communications are those of the individual contributors and not the statements and opinions of Praise Worthy Prize S.r.l.. Praise Worthy Prize S.r.l. assumes no responsibility or liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained herein. Praise Worthy Prize S.r.l. expressly disclaims any implied warranties of merchantability or fitness for a particular purpose. If expert assistance is required, the service of a competent professional person should be sought.

International Review on Computers and Software (I.RE.CO.S.), Vol. 8, N. 4 ISSN 1828-6003 April 2013

Energy Based Efficiency Evaluation of Cluster and Tree Based Routing Protocols for Wireless Sensor Networks M. Faheem, Zia Ud Din, M. A. Shahid, S. Ali, B. Raza, L. Sakar Abstract – The wireless arena has been experiencing exponential growth in the last couple of

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years. In twenty- first century, due to recent advances in sensor and wireless communication technologies have enabled a new generation of massive-scale Wireless sensor network (WSN) and is not-too-distant future when miniature, low-cost sensors may be literally sprayed onto walls, roads, or machines, creating a digital skin that can observe a variety of interested physical phenomena. Due to the severe energy constraint of a sensor node, network architectures and protocol are important aspects in the design of Wireless sensor network due to their big impact on the energy consumption. Since last couple of years to prolong the network lifetime of a tiny sensor node various researchers have proposed a number of energy efficient routing protocols. Up till now, their performance superior or worse cannot be inspected without any suspicion by conventional techniques because of their transformed behavior in different environments. The aspire of this research is to evaluate the performance among energy efficient routing protocols in Wireless sensor networks (WSNs) based on metrics Overhead, Delay, Packet Delivery Ratio (PDR) and Congestion over Energy consumption and Network lifetime. The current research work can be utilized to guide protocol designing, application-specific protocol selection and modification of the existing routing protocols in Wireless sensor networks. Simulations between Energy Efficient Cluster based Data Gathering Protocol ECDGP and Energy Efficient Data Collection Protocol-Tree Based EEDCP-TB confirms their results in term of Overhead, Delay, Packet Delivery Ratio (PDR) and Congestion on Energy consumption and Network lifetime. Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved.

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Keywords: Wireless Sensor Network, Energy Efficient, Routing, Protocol, Cluster, Tree

I.

Introduction

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A Wireless sensor network consists of hundreds to thousands of sensor nodes or motes normally battery operated to observe events in the real world. These nodes are miniature in volume with the limited memory, sensing facilities as well as communication and processing capabilities. Modern growth in low power wireless integrated micro-sensor technologies have made these sensor nodes accessible in a large numbers at low cost, can be deployed in a wide range of applications such as disaster relief, emergency rescue operation, environmental monitoring, home security, localization, surveillance, temperature, pressure, sound, motion, pollutants, earthquake cautions and many other fields, to function for a long time [1]. The most important difference between Wireless sensor networks and traditional networks can be represented in term of their restricted power, processing, storage and reliable selforganization capabilities. Among the various scopes, one of the key applications of WSN is, all sensor nodes work simultaneously in collaboration and send several types of periodically observed information to sink over a large geographical area with great accuracy for further analysis.

Manuscript received and revised March 2013, accepted April 2013

After computing and aggregation sink conveyed this data to external network by way of internet or satellite network [2]. Among researchers, a researcher claim that communication energy cost is several orders higher than the computational cost [3]. Due to their constrained energy resources energy efficiency has become one of the most critical issues in WSNs. Inefficient use of energy can significantly reduce the network lifetime [4]. In order to make a longer network lifetime, it is essential to reduce the energy expenditure of individual nodes. In addition, imprecise use of bandwidth may lead to more collision during message transmission. This phenomenon causes the sink node to fail to learn significant data about urgent events and also causes more wastage of energy in term of data retransmission [5]. In particular minimizing energy consumption is a key requirement in the design of sensor network protocols and algorithms. Since sensor nodes are equipped with small often irreplaceable battery with limited power capacity [6]. It is essential that the network should be energy efficient in order to prolong the lifetime of the network. Energy expenditure in WSN is because of data messages transmitting/receiving, processing and forwarded quires to the neighbouring nodes. Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved

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M. Faheem, Zia Ud Din, M. A. Shahid, S. Ali, B. Raza, L. Sakar

II. II.1.

Related Work

Tree Based Routing Protocols in WSN

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In this section we briefly discuss the subsequent tree based routing protocols. The basic purpose of the tree based routing protocols are building optimum routes from the source node to sink. Usually, sink node is considered as much more powerful than sensor nodes and works as an interface with the administrator [9]. On a tree based routing Parents-Childs (PC) relation is formed among the nodes and parents are selected based on different parameters such as their number of hops, residual energy, link quality and length of routing path from the source node to sink [10]. Some most important weaknesses of tree based routing techniques has been given by Nirupama Bulusu and S. Jha in [11], are (i) tree root is a single point of failure, (ii) as a network grow up path becomes longer and longer causes to amplify end to end packet failure, congestion and decreased network lifetime and (iii) it has an extremely poor load balancing as nodes nearer to the root carry more traffic. T. Mingdong, et al. proposed a Tree Cover based Geographic Routing protocol (TCGR). To minimize energy consumption it utilizes the key idea of greedy routing to deliver the data packets to its neighbor's closet to the destination. It takes into account the two mechanisms are (i) labelling scheme and (ii) routing scheme. In the tree cover network node position can be represented by a each label set based on KPR scheme and data messages are conveyed by link and shortcut based. Many advantages and disadvantages of geographic routing also has been discussed in [12].

L. Chiu-Kuo et al. in [13] to handle the energy consumption problem proposed a Steiner Trees Grid Routing protocol (STGR). After node deployment virtual grid structure is constructed by the sink based on the square Steiner tree where the cross point of the grid is shown by Dissemination points (DPs) while Steiner points as (SPs) depending on the length of the square. To Steiner tree problem more intermediate vertices and edges are added to the graph in order to minimize the length of spanning tree. In data forwarding phase, if DPs are parallel or vertical to the location of the sink then Dissemination nodes (DNs) abundant to forward the data along with an upstream path in the grid cell. Otherwise, data is forwarded by DNs by taking into account hexagonal structure. By experimental results author assert that proposed protocol out performs Geographical Grid Routing protocol (GGR) in term of energy efficiency. A cross layer approach mechanism to achieve reliable data collection networks is proposed in [14]. ‘HAT-Mobile’ a Tree-based routing protocol for mobile Wireless sensor networks proposed by Borsani et al. in [15], addresses the problem of node energy consumption and node mobility management. It takes into account proactive techniques to speed up the handover procedures of mobile sensor nodes among different access points. HAT-Mobile restrictions the impact of the signalling overhead and maintains the handover procedure of node mobility which consequently cause to reduce energy consumption of the overall process. Furthermore, the authors state that it is also appropriate to save energy in term of handover latency and reduced packet error rate. A Tree based Routing Protocol named (TBRP) to keep in control node mobility is proposed by Mrityunjay Singh et al. in [16]. It handles the node energy constraints by considering node energy level values, one when a node has an energy level higher than half of the original battery capacity, second when a node have an energy level lower than half of the original battery capacity but higher than the average energy level. The proposed protocol shows better in targeting, quality of communication among nodes and network life time of sensor nodes. Furthermore, it has a high packet delivery ratio because of Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access Collision Avoidance (CSMA/CA) techniques. A number of approaches to handle node energy consumption, mobility and to support uplink and downlink connectivity are studied in [15], [17].

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While wasteful energy consumption is due to ideal listening and back off time, overhearing, packet collision and time latency due to control messages [7], [8]. The key aim of this manuscript is not to put forward a new protocol design for cluster or tree based networks or to improve an existing routing protocol to prolong the network lifetime. Instead, it aims to experimentally explore internal/external factors that impact the lifetime of the cluster and tree based sensor networks in a real time environment due to their miscellaneous behavior in different scenarios for the developers and researchers. So that, innovative energy efficient routing protocols can be embedded in WSNs to prolong node life time and further to motivate researchers for supplementary research to prolong sensor life. The rest of this paper is prepared as follows: Section II presents an overview of the tree and cluster based routing protocols in the literature. Section III describes the protocols description for analysis. Section IV presents the performance using network model. Section V gives an explanation of performance analysis using radio model. Section VI illustrates evolution analysis. Final section VII represents a summary of conclusions.


II.2.

Cluster Based Routing Protocols in WSN

This section briefly discusses the succeeding cluster based routing protocols. In the recent era, a number of researchers demonstrate their enormous attention in cluster based energy efficient routing in Wireless sensor networks. A clustering scheme has been applied to sensor networks with hierarchical

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death of mobile nodes. Therefore, it has an extra assignment to strongly monitor the network status continuously.

III. Protocols Description for Analysis

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As shown by our current literature either it is not feasible to design a routing protocol which has stupendous performance over entire scenarios for all applications. The diverse atmospheric circumstances and non linear battery consumption are the most significant reasons which have not been discussed in Bo and Z. Xinrong proposed and J. Yong-xian et al. protocol routing protocols. In addition, inspected results cannot converse about the time varying nature of real life scenario. Hence both chosen strategies are strongly required to be tested in the real atmosphere. In our present study, these listed strategies have been tested in the real life scenario to find the effect of Congestion, Delay, Overhead and Packet Delivery Ratio on Energy consumption and Network life time. So that, supplementary energy efficient routing protocol can be set in, in Wireless sensor networks to make longer network life span. III.1. ECDGP

In [22] C. Bo and Z. Xinrong proposed an Energy Efficient Cluster based Data Gathering Protocol (ECDGP) to prolong the network lifetime. It belongs to the family of event-driven routing protocols works in two segments are (i) cluster formation and (ii) data transmission phase. In cluster formation phase, node by cooperative effort form a clustering network located inside an event area. While in data transmission phase, watched data collected by CH from active nodes are forwarded to sink by multi-hop routing. In C. Bo and Z. Xinrong protocol cluster head election takes place based on node belief degree. Which takes into account two parameters (i) residual energy and (ii) intra cluster distance between nodes coming from neighbors can be calculated by a belief degree function. In the current round the node which has more residual energy and minimum intra cluster distance appointed as CH. Furthermore, it selects active nodes according to the demand of network coverage by following an active selection algorithm as presented in [23]. Finally to forward observe the data from CH to the sink multi-path routing is used in order to achieve the load balance in the network. The C. Bo and Z. Xinrong proposed protocol works into rounds and cluster head rotation repeats when the energy of CH goes down to average energy .

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structures to get better performance of the network along with reducing the need for power consumption. Clustering is a cross - cut technique, which is used almost in all segments of the protocol stack. The vital initiative behind clustering scheme is to organize senor nodes in a group of nodes around a Cluster head (CH) with the responsibility of up keeping state and inter cluster connectivity involved in data processing. Further this processed data sent to the Base Station (BS) via sink by deciding the minimum amount of route nodes over long distance to save node energy [18]. To handle the overload energy consumption problem in Wireless sensor network an adaptive cluster based sensor network routing protocol named as (MIN-RC) is proposed by A. Ghneimat et al. in [19], is the enhancement of LEACH-C. The network life duration of the proposed protocol can be divided into a number of rounds and each round begins with the setup phase. In order to solve problem of overloading energy consumption and minimize the diversity of the energy consumption between sensor nodes it employs an adaptive method to control the round time by considering the current state of the network. Where round time ‘Tcurrent’ is defined at the beginning of the round and ‘R current’ rely on the minimum cluster size and the optimal cluster size rather than using a constant round time ‘t’ for every round in the network span. The proposed protocol overcomes the overload problem of LEACH-C and improves the network efficiency however, the proposed scheme has drawback of an extra setup overhead. In [32] author sets the time length of each round to prolong the network lifetime and throughput in LEACH. TEEN is a reactive protocol proposed in [20] by D. Baghyalakshmi et al. for time-critical applications. The main drawback of this protocol is that the transmission from nodes in cluster head will not be there when the sensed value is not greater than the hard threshold. Sarma et al. in [21] to maintain node as well as the sink mobility a hierarchical cluster based routing protocol. In the setup phase different tasks such as logical clustering, role assignment is performed and Gateway-node (GN) is selected based on higher energy and relatively inferior mobility. Inside the same cluster, two cluster head nodes utilize different coding schemes and frequency bands to limit inter-cluster interference. Re-clustering may get initiated when the cluster head loses its connectivity or assigned time period has expired. The protocol takes into account major advantages such as (i) different CHs for each cluster reduced the bottleneck situation, (ii) maintain connectivity, (iii) minimizing the control message overhead, (iv) delivering data to sink with a higher throughput level, (v) take care of the link failure and (vi) find alternate routes in order to deliver data at the destination. However, it has to suffer a problem that during data forwarding there can be a node or link failure due to the


III.2. EEDCP-TB J. Yong-xian et al. in [24] proposed a scheme to improve data aggregation and network performance by International Review on Computers and Software, Vol. 8, N. 4

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Performance Analysis Using Network Model

The energy model used is the same as used by W. Wang et al. in [25] where to transmit a l-bit data at a distance d the radio energy expands is given as: (, )= +

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A range of models have been proposed for WSNs. In the current study we mainly consider a WSN consisting of a set of sensor nodes and a BS are scattered in the network field devoid of isolation. The network is modelled is represented as fully connected graph G (U, E, R), where U is the set of all the nodes denoted by U= { , , …….. } E represents the set of all connected edges among nodes.

Performance Analysis Using Radio Model

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The distance among nodes k and l can be denoted by k ∈ , ( , )≤ where represents specific distance threshold and R is the omnidirectional communication radius of the node. The following assumptions are taken into account: - All nodes and BS after deployment are motionless and located at a specific position near to sensor field. - Symmetric model is assumed here that means sensor node A is located within the transmission range of sensor B and then B is also located within a transmission range of A. - All nodes have the same capabilities, transmission ranges and restricted power resources. - The sensor nodes periodically monitor their vicinity and generate observed data after aggregating watched information in each round send it to the Base Station via sink for further analysis.

applying data distribution and load balancing technique which fulfils the latency requirement of data collection. Tree construction in J. Yong-xian et al. protocol occurs through the method of flooding avoidance also to save node energy it utilizes Cascading Time Scheme (CTS). To achieve energy balance in the network it considers the main idea of node energy state parameters during forwarding node selection and the entire network is divided into different energy level's threshold. If sink desires to disseminate information to a group of nodes of the interested areas in the network then, first it calls the next-hop forwarding nodes by running Findforwarding algorithm with certain conditions of threshold energy. The forwarded nodes run again this algorithm to find next hops until conditions satisfy. In data forwarding phase, sensor nodes send their sense data to their parent nodes by following CTS. To avoid conflict between messages it considers the delay scheme. One of the major drawbacks of this routing protocol is the network time synchronization and delay to find the next hop forwarded nodes with the required level of energy.

TABLE I NODE SPECIFIC OFFLINE PARAMETERS Simulation tool: NCTUns 6.0 Models Simulation model Values Variable Radio Energy 10pj/bit/ Model 0.0015pj/bit/ 5nJ/bit/signal 20-60m Threshold distance ( ) ‘Rx’ power dissipation 15nJ/bit during receiving data packet. ( ) ‘Tx’ power dissipation 35nJ/bit during transfer data packet. ( ) 100 bytes Application 25 bytes 25 bytes 100bits . Bit rate 9600bps 2m[bps] 0.035w Wireless Node IEEE 802.11b Number of Nodes 100~200 Sink Position 20~80m 100J Network Model Simulation trial 50 times Simulation time 127 s Total no of rounds 200 ~2000

( )=

· ·

( )+ (, )

+ · ,+ ∗

· ·

(1) , ,

< ≥

(2)

, and are the parameters of the transmission/reception circuitry depending on the distance between the transmitter and receiver. Free space and multi-path fading Channel model is considered here. While receiving, the radio expands energy: ()= = ·

()=

(3)

R

In addition, we also assumed that the energy dissipation for data aggregation is represented as . / and threshold distance is defined as = .

VI.

Evaluation Analysis

Delay can be defined as the average latency from the instant that a packet is sent out from a source to that instant it is received by the sink. Fig. 2 illustrates that the network depth is inversely proportional to the delay and delay increases from lower level to upper level (from end node to sink) due to rise in number of node tasks such as sensing, data aggregation efficiency, facing redundant data, maintaining routing table and unequal distance among nodes also to sink.



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l bit packet

Transmit Electronic s

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T X Amplifier

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l l bit packet Receive Electronic s

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Fig. 1. Radio Energy Dissipation Model

delay is the deliberated algorithm. It runs to find next hop forwarding node runs until nodes of specific threshold energy are found. On the other hand in tree construction phase, Parent-child relation does not consider the exact equal size and distance among nodes and increases towards BS. Normalized Routing Overhead- Is contains further three variants are (i) packet overhead is the number of routing packets “transmitted” per data packet “delivered” at the destination, (ii) byte overhead is the number of bytes of routing packets “transmitted” per data byte “delivered” at the destination and in the end (iii) network setup overhead which tells how many control messages are needed to put a network to be functional. Each hop-wise transmission of a routing packet is counted as one transmission. Although J. Yong-Xian et al. protocol has to face normalized overhead problems because during tree construction and data forwarding process more control messages are needed to satisfy Parent-child's relation for the network to be put into functional mode and also due to joint request message are needed to send to avoid further packet loss with the minimum hop count is less as compared to C. Bo and Z. Xinrong protocol. Fig. 3 shows that protocol proposed by C. Bo and Z. Xinrong apply more message overhead than J. Yong-xian et al. protocol for setup and routing data packets from an event area to sink. Basically in C. Bo and Z. Xinrong protocol two types of cluster formation occur which may be divided into the first and second level clustering, respectively. In the second level clustering for active node selection of a particular event area needed more messages to satisfy the coverage area of the network. During observed information forwarding from an event area to sink, messages are forwarded through multi-path and multiple-hop routing which needed more control message overhead then single-hop. In addition, due to data redundancy it may cause data packet collision due to limited buffer size. Thus, the protocol proposed by C. Bo and Z. Xinrong contains more messages overhead to set up a network to be functional.

Fig. 2. Delay between ECDGP & EECDP-TB

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Initially, in the routing phase cluster head in the protocol proposed by C. Bo and Z. Xinrong transmit the observed data to their next hop CH, with the lowest distance and fewest number of cluster members. However, after a specific time interval cluster head starts to convey watched information to their next hop relaying node by considering maximum energy without taking into account sincere congestion problem. Thus, sometimes it may cause to increase the distance among CHs or relaying nodes and node over assignment in case of entire network data flow. Our simulation results demonstrate that the protocol proposed by C. Bo and Z. Xinrong as compared to the routing protocol proposed by J. Yong-xian et al. has a smaller amount of delay. The main reason is that the C. Bo and Z. Xinrong’s proposed protocol takes into account two algorithms, one which selects the active nodes among the sleeping nodes and reduce the network traffic load for a particular event while on the other hand watched information is conveyed by taking into account the both multi-hop and multipath routing from source to destination by taking into account the idea of relay nodes with high energy. Furthermore, during cluster formation it considers the minimum distance between clusters to sink while in J. Yong-xian et al. protocol, one of the most vital issues caused more


Fig. 3. Normalize routing overhead between EEDCP-TB & ECDGP


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Normalized routing overhead in both routing protocol increases as the nodes start to die in the network also due to increase in network depth. Packet Delivery Ratio (PDR) or throughput can be defined as the ratio of the total number of data packets received successfully to the total number of data packets generated by source nodes. We can define PDR as: /

(4)

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where shows the number of data packets received while illustrates the numbers of messages send by source. As shown by in the Fig. 4 packet delivery ratio decreases as the depth of the network increases. Although, the packet delivery ratio of the routing protocol proposes by J. Yong-xian is well though, C. Bo and Z. The Xinrong’s protocol is more superior in term of the packet delivery ratio. For the reason that, during conveying watched information from source to destination it has to face a number of problems such as (i) during parent child relations there is a possibility that a node can be appointed as a route node containing minimum energy than the required energy. In such a case due to a single node failure entire specific uplink/ downlink data can be lost which can cause to increase message retransmission in the network (ii) watched information also may be lost due to limited buffering capacity of the nodes which are more nearer to the sink as a result it has to face data packet collision thus, it reduced the packet delivery ratio. In addition, if a forwarding node does not have any specific destination address then entire specific data can also be lost or delayed while in C. Bo and Z. Xinrong’s protocol during message forwarding it considers the both multi-hop and multi-path routing and selects the next hop with the maximum energy which is more fault tolerant and turn it increase packet delivery ration however, it has to face the problem of data redundancy because of sending multiple copies of a single data. Congestion occurs when a node receives a packet more than its capacity also due to inherent shared wireless link. Fig. 5 shows the congestion management in C. Bo and Z.

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The Xinrong’s protocol is less than J. Yong-xian et al protocol in term of entire network data flow, while in a case of specific event data flow it performs superior. In proposed J. Yong-xian et al protocol entire network is divided into various node assignments in term of sensing and carrying information. Observed information about an event has to be forwarded to the next hop node by considering node over the assignment with minimum distance by multi-hop routing. Although congestion management in C. Bo and Z. The Xinrong’s protocol is well because messages are forwarded along with the best path by considering node depth to sink, however it does not take into account exactly equal cluster size, which means that some CHs have maximum number of children while others have less number of children. Due to limited buffering size, nodes nearer sink or CH containing more member nodes has to face more congestion problem. In addition, it also has to face the problem of data redundancy due to sending more than one copy of data through multi-path to sink. A single copy of data can be flowed through multiple paths without considering the node over assignment among the nodes which caused to increase more congestion in the network due to node limited buffering capacity. Energy consumption can be defined as the total energy consumption by sensor nodes over communications. The congestion, delay, packet delivery ratio and normalize overhead are the vital issues which directly affect on node energy. The degradation in the network lifetime is due to the increase in the number of transmissions and receptions which increases as the network distance downwards increases from source to sink. In terms of energy consumption Fig. 6 makes it clear that C. Bo and Z. Xinrong’s protocol performs finer than J. Yong-xian et al protocol. This is due to the reason that C. Bo and Z. Xinrong’s protocol devours a lesser amount of energy by eliminating the route discoveries of all the nodes in the entire network. C. Bo and Z. The Xinrong’s protocol can save energy by maximizing the number of sleeping nodes according to the demand of network coverage in a cluster under the constraint that the remaining nodes can satisfy the coverage expectation with lesser amount of messages requirements.

Fig. 5. Congestion management between EEDCCP-TB & ECDGP

Fig. 4. PDR between EEDCP-TB & ECDGP



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Fig. 7. Network lifetime by using EEDCP-TB & ECDGP.

Furthermore, battery life is divided among the entire network nodes to balance energy consumption than J. Yong-xian et al protocol. While J. Yong-Xian et al. protocol utilizes a proficient load balancing technique by using node energy state parameters and further save node energy by utilizes CTS to avoid message collision, thus reduced the message retransmission. During forwarding and receiving message/data nodes are divided into different level of threshold and nodes with a higher energy level are selected to convey the watcher information thus, it helps in network stability as well as appropriate load balancing in the network. Table II shows the energy consumption between ECDGP and EEDCP-TB protocols by considering round numbers.

In the meanwhile number of 50% nodes died in C. Bo and Z. Xinrong’s protocol at round number 1340 as compared to J. Yong-xian et al. protocol as in 1250. Finally last node died in C. Bo and Z. Xinrong’s protocol in round 1890 while in J. Yong-xian et al. protocol round number 1823. This is because of the reason that algorithm used in C. Bo and Z. The Xinrong’s protocol is eligible to maintain stability in the entire network also C. Bo and Z. Xinrong’s protocol remains by some means stable due to its well load balancing technique such as taking into account energy state parameters and data aggregation competence.

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Fig. 6. Energy consumption between EEDCP-TB & ECDGP

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TABLE II ENERGY CONSUMPTION ACCORDING TO ROUND NUMBERS Energy Protocol Numbers of Number of rounds (J/node) rounds 50 ECDGP 680 1030 EEDCP-TB 564 841 ECDGP 1020 1373 70 EEDCP-TB 867 1173 ECDGP 1485 1880 100 EEDCP-TB 1207 1730

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Network life time of Wireless sensor network can be described by using three kinds of metrics are (i) the time form deployment of the network to the death of the first node (FND), (ii) the time when a certain percentage of node alive (PNA) and (iii) the time when all node died. By the experimental results it has been shown that Delay, Congestion, PDR, Normalize overhead and Energy consumption are the key factors which directly have an effect on Network lifetime. Fig. 7 has made is clear that in term of network life time C. Bo and Z. Xinrong’s protocol outperforms the J. Yong-Xian et al. protocol routing protocol due to a stable link between nodes, will load balancing of the entire network and least amount of message retransmission also it shows that the number of nodes alive in various rounds. The round number of the first node died in C. Bo and Z. The Xinrong’s protocol is near about 800 which is 700 in J. Yong-xian et al. protocol.


Nodes % -

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100

TABLE III NUMBER OF NODE DIED IN VARIOUS ROUND Protocol Number of FND rounds ECDGP 700 Y EEDCP-TB 600 Y ECDGP 1240 Y EEDCP-TB 1150 Y ECDGP 1880 Y EEDCP-TB 1730 Y

VII.

LND -

Y Y

Conclusion

In current research work we evaluate the performance of cluster and tree based routing protocols under realistic scenario. Here it has been mainly focused on the energy constraints of the sensor node. The comparison simulation result demonstrates that the Normalized Message overhead, Delay, Congestion and Packet delivery ratio effect on the sensor lifetime and causes to degrade the network performance and node energy. In conclusion, as referring to the Energy Efficient routing protocol ECDGP has a slower rate in decreasing energy which is much greater in EEDCP-TB in term of delay, packet delivery ratio, energy consumption and network lifetime. As simulation results show that it has some shortcomings like (i) unequal size cluster formation and (ii) approximately not all sensor nodes near to an event area are selected to satisfy close sensing. Therefore, the future research work will be the modification of the protocol or designing of a novel energy efficient routing protocol for the large area network.


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Update table ()

Appendix

If

Some strength and weaknesses of cluster and tree based routing protocols according to our philosophy during literature review are given below in Table A1 and A2, respectively.

Node → Node { Do SND () }

ENTIRE ‘ECDGP’ WORKING MECHANISM Else

ALGORITHM I PSEUDO-CODE FOR NETWORK INITIALIZATION Initialize ()

{ Sleep ()

{

}

Sink broadcast (Hello) }

Node REC (Msg) }

CONST table () Update table () While (Node broadcast & Node

T

}

{ received)

ENTIRE ‘EEDCP-TB’ ROTOCOLWORKING MECHANISM

{

ALGORITHM III PSEUDO-CODE FOR TREE CONSTRUCTION & DATA COLLECTION

IN

CONST table () Update table ()

Sink flow () { While (energy>=30%)

} } }

{

R

}

Find forwarded node () Call tree CONST (Parent-child) Forwarded request () Update table ()

ALGORITHM II PSEUDO-CODE FOR CLUSTER FORMATION & ACTIVE NODE SELECTION Initialize ()

If {

EP

{

Event (occur) Node Node

(Sense)

Broadcast (Msg)

Update table () Else {

R

Sleep (node)

}

If

RSSI

> RSSI

{

RSSI

broadcast (Hello)

RSSI

REC (Msg)

If { RSSI > RSSI } Node RSSI

SND REQ (JOIN) REC (ID)

} Forwarded node flow () { While (energy>=30%) { Find leaf node () Call tree CONST (Child-parent) Forwarded REQ () Update table () } Leaf node flow () { Check table () Preceding node () Fetch info () Collect info () If { t>=i; // i is a specific time interval Data collect forwarded RES () } Else { Forwarded RES () } } Forwarded RES () } }



1019


TABLE A1 CLUSTER BASED ROUTING PROTOCOLS IN WIRELESS SENSOR NETWORK

2

ERP

3

MIN-RC

4

Imp-AODV

5

CEDCAP

6

MCEE

7

CTPEDCA

8

PPEECS

9

HRP

10

EACD

11

ECDGP

12

SFRP

13

Chi-Chou et al Protocol S. Alizadeh et al. Protocol H. K. D.Sarma et al. Protocol

16



Weaknesses Message Overhead.



Expense of stability awareness

[25]

   

Extra setup overhead Energy consumption Overhead Updating routing table cost

[19]



Overhead

[27]



Congestion management pb.

[28]



Network depth

[29]

  

Message overhead Energy consumption Node depth increases

     

Bottleneck situation Maintain connectivity Message overhead Higher throughput Congestion Save network lifetime

B. A .Sabrish et al. Protocol

Reference [17]

[26]

[30]

[31]



Poor Congestion management

[32]

 

Message overhead Active node distance

[33]

 

Delay CH management

[34]



Congestion

[35]

   

Delay Congestion Congestion Synchronization

[36]



Delay

R

15

EP

14

Strength  High packet delivery ratio.  Average energy consumption.  Handle node mobility.  Prolong the network lifetime.  Inter and intra cluster problem  Prolong the network lifetime  High packet delivery ratio  Overload problem in LEACH  Improve network efficiency  Minimum delay  Minimum packet loss  Balance energy consumption  High speed data movement  Distribute energy consumption among nodes  Good congestion management  Less energy consumption  Minimum delay  Very fast route finding with  Improve energy efficiency  Uniform energy utilization  Less delay  Energy efficient  Less delay  Less congestion  Less overhead  Energy efficient  Excellent in energy balancing  High packet delivery ratio  Less delay  Congestion management  Well energy utilization  Less message overhead  Congestion management  High packet delivery ratio  Less overhead  Save node energy  Balance energy consumption

T

Protocol Name MCB

IN

Sr No. 1

[21]

[37]

TABLE A2 TREE BASED ROUTING PROTOCOLS IN WIRELESS SENSOR NETWORK

Sr No 1

HAT-Mobile

R

2

Protocol Name TCGR

3

STGR

4

FFDA

5

TBRP

6

EEDCP-TB

7

Trickle Tree

8

Iman ALMomani et al. protocol

                      

Strength Less congestion Management Fast data movement Control node mobility. Handover latency Good Congestion management Save node energy Balance network energy High packet delivery ratio Data aggregation prophecy A good load balance Latency Control node mobility Maintain connectivity High packet delivery ratio Congestion management Good packet delivery ratio Data aggregation efficiency Minimum control overhead Quick setup management The packet delivery ratio is good Congestion management Good data delivery ratio Minimum delay


   

Weaknesses Overhead Tree depth Packet error rate Updating data table cost

Reference [12]



Energy consumption

[13]



Delay

[38]



Energy consumption

[9]

 

Tree depth Delay

[24]

   

Can’t check tree depth value Synchronization problem Tree depth Energy consumption

[39]

[15]

[40]


1020


Acknowledgements The work is financed by Comsats Institute of Information & Technology, Pakistan. The authors would also thank the anonymous reviewers for their valuable and insightful comments.

[5]

[6]

[7]

[8]

[9]

[10]

[11] [12]

[13]

[14]

[15]

[16]

[17]

IN

[4]

R

[3]

EP

[2]

G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella, "Energy conservation in wireless sensor networks: A survey," Ad Hoc Networks, vol. 7, pp. 537-568, 2009. T. Ma, M. Hempel, D. Peng, and H. Sharif, "A Survey of EnergyEfficient Compression and Communication Techniques for Multimedia in Resource Constrained Systems," Communications Surveys & Tutorials, IEEE, vol. PP, pp. 1-10, 2012. C. Alippi, R. Camplani, C. Galperti, and M. Roveri, "A Robust, Adaptive, Solar-Powered WSN Framework for Aquatic Environmental Monitoring," Sensors Journal, IEEE, vol. 11, pp. 45-55, 2011. K. Lin, C.-F. Lai, X. Liu, and X. Guan, "Energy Efficiency Routing with Node Compromised Resistance in Wireless Sensor Networks," Mobile Networks and Applications, pp. 1-15, 2010. A. Abedi, "Power-efficient-coded architecture for distributed wireless sensing," Wireless Sensor Systems, IET, vol. 1, pp. 129136, 2011. P. Suriyachai, U. Roedig, and A. Scott, "A Survey of MAC Protocols for Mission-Critical Applications in Wireless Sensor Networks," Communications Surveys & Tutorials, IEEE, vol. 14, pp. 240-264, 2012. Q. Tang and B. Qiao, "An energy-efficient protocol architecture with multiple clustering for Wireless Sensor Networks," in Intelligent Computing and Integrated Systems (ICISS), 2010 International Conference on, 2010, pp. 898-901. L. Hai Van and T. Xueyan, "An Efficient Scheduling Algorithm for Data Collection through Multi-path Routing Structures in Wireless Sensor Networks," in Mobile Ad-hoc and Sensor Networks (MSN), 2010 Sixth International Conference on, 2010, pp. 68-73. S. Yan, L. Haiqin, and K. Min Sik, "Energy-Efficient Routing Protocol in Event-Driven Wireless Sensor Networks," in Communications Workshops (ICC), 2010 IEEE International Conference on, 2010, pp. 1-5. Z. Zusheng and Y. Fengqi, "Performance Analysis of ClusterBased and Tree-Based Routing Protocols for Wireless Sensor Networks," in Communications and Mobile Computing (CMC), 2010 International Conference on, 2010, pp. 418-422. Nirupama Bulusu and S. Jha, "Wireless Sensor Networks, A System Perspective.," © 2005. T. Mingdong, C. Hongyang, Z. Guoqing, and Y. Jing, "Tree Cover Based Geographic Routing with Guaranteed Delivery," in Communications (ICC), 2010 IEEE International Conference on, 2010, pp. 1-5. L. Chiu-Kuo, L. Chih-Hsuan, and L. Jian-Da, "Steiner trees grid routing protocol in wireless sensor networks," in Wireless Communications, Networking and Information Security (WCNIS), 2010 IEEE International Conference on, 2010, pp. 473-477. A. Niinomi, K. Naito, K. Mori, H. Kobayashi, and M. Ehara, "Simple Tree Based Routing for Data Collection Networks," in P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2010 International Conference on, 2010, pp. 535-541. L. Borsani, S. Guglielmi, A. Redondi, and M. Cesana, "Treebased routing protocol for mobile Wireless Sensor Networks," in Wireless On-Demand Network Systems and Services (WONS), 2011 Eighth International Conference on, 2011, pp. 164-170. Mrityunjay Singh, Monika sethi, Niranjan Lal, and S. Poonia, "A Tree Based Routing Protocol for Mobile Sensor Networks (MSNs)," Mrityunjay Singh et al / (IJCSE) International Journal on Computer Science and Engineering,, vol. Vol. 02,, pp. 55-60, 2010. S. Deng, J. Li, and L. Shen, "Mobility-based clustering protocol for wireless sensor networks with mobile nodes," Wireless Sensor

R

[1]

T

References

Systems, IET, vol. 1, pp. 39-47, 2011. [18] S. Ghiasi, A. Srivastava, X. Yang, and M. Sarrafzadeh, "Optimal energy aware clustering in sensor networks," Sensors, vol. 2, pp. 258-269, 2002. [19] A. Ghneimat, J. Mellor, and J. Ping, "Adaptive, Cluster Based, Sensor Network Routing Protocol," in Computer Modelling and Simulation (UKSim), 2011 UkSim 13th International Conference on, 2011, pp. 472-476. [20] D. Baghyalakshmi, J. Ebenezer, and S. A. V. Satyamurty, "Low latency and energy efficient routing protocols for wireless sensor networks," in Wireless Communication and Sensor Computing, 2010. ICWCSC 2010. International Conference on, 2010, pp. 1-6. [21] H. K. D. Sarma, A. Kar, and R. Mall, "Energy efficient routing protocol for Wireless Sensor Networks with Node and Sink mobility," in Sensors Applications Symposium (SAS), 2011 IEEE, 2011, pp. 239-243. [22] C. Bo and Z. Xinrong, "An Energy-Efficient Cluster-Based Data Gathering Protocol for Wireless Sensor Networks," in Wireless Communications Networking and Mobile Computing (WiCOM), 2010 6th International Conference on, 2010, pp. 1-5. [23] M. Liu, H. G. Gong, Y. C. Mao, L. J. Chen, and L. Xie, "Distributed energy-efficient data gathering and aggregation protocol for wireless sensor networks," Ruan Jian Xue Bao/Journal of Software, vol. 16, pp. 2106-2116, 2005. [24] J. Yong-xian, C. Feng-zhen, C. Gao-feng, and H. Wei, "EnergyEfficient Data Collection Protocol for Wireless Sensor Network Based on Tree," in Wearable Computing Systems (APWCS), 2010 Asia-Pacific Conference on, 2010, pp. 82-85. [25] W. Wang, B. Wang, Z. Liu, L. Guo, and W. Xiong, "A Clusterbased and Tree-based Power Efficient Data Collection and Aggregation Protocol for Wireless Sensor Networks," Information Technology Journal, vol. 10, pp. 557-564, 2011. [26] B. S. Krishnan, M. Ramaswamy, and A. Nachiappan, "A New Cluster Based Protocol for Wireless Sensor Networks," in Information Science and Applications (ICISA), 2011 International Conference on, 2011, pp. 1-8. [27] W. Wang, Z. Liu, B. W. X. Hu, L. Guo, and W. Xiong and C. Gao, . , "CEDCAP: Cluster-based energy-efficient data collecting and aggregation protocol for WSNs.. Res. J. Inform. Technol., 3: 93-103.," 2011. [28] T. Qi and Q. Bing, "An energy-efficient protocol architecture with multiple clustering for Wireless Sensor Networks," in Proceedings - 2010 International Conference on Intelligent Computing and Integrated Systems, ICISS2010, 2010, pp. 898901. [29] A. Rajeswari, P. T. Kalaivaani, A. Abraham, J. L. Mauri, J. F. Buford, J. Suzuki, et al., "Energy Efficient Routing Protocols for Wireless Sensor Networks Using Spatial Correlation Based Collaborative Medium Access Control Advances in Computing and Communications." vol. 192, ed: Springer Berlin Heidelberg, 2011, pp. 143-156. [30] M. Khulbe, P. Srivastava, and R. C. Jain, "Parametric protocol for energy efficient cluster head selection (PPEECS) in WSNs," in 2010 16th Asia-Pacific Conference on Communications, APCC 2010, 2010, pp. 200-203. [31] M. Zeynali, A. Mollanejad, and L. M. Khanli, "Novel hierarchical routing protocol in wireless sensor network," Procedia Computer Science, vol. 3, pp. 292-300, 2011. [32] J. Yu, Y. Qi, G. Wang, and X. Gu, "A cluster-based routing protocol for wireless sensor networks with nonuniform node distribution," AEU - International Journal of Electronics and Communications, vol. In Press, Corrected Proof, 2011. [33] B. C. a. X. Zhang, "An Energy-Efficient Cluster-Based Data Gathering Protocol for Wireless Sensor Networks," in Wireless Communications Networking and Mobile Computing (WiCOM), 2010 6th International Conference on, 2010, pp. 1-5. [34] K. Subbu and L. Xinrong, "SFRP: A selective flooding-based routing protocol for clustered wireless sensor networks," in Radio and Wireless Symposium (RWS), 2010 IEEE, 2010, pp. 380-383. [35] K. Chi-Chou, W. Jun, and C. Shih-Chieh, "Energy efficient clustering communication protocol for wireless sensor network," in Advanced Communication Technology (ICACT), 2010 The 12th International Conference on, 2010, pp. 830-833.



1021


Muhammad Anwar Shahid received his M.Sc. Degree in Computer Science from University of the Punjab, Lahore, Pakistan in 2004. He is working as a lecturer at Department of Computer Science and Management Information Systems, Oman College of Management and Technology, Oman. His research interests include the areas of Energy Efficient routing in wireless ad-hoc networks.

[36] S. Alizadeh and A. Ghaffari, "An Energy-efficient hirerchical Clustering protocole in wireless sensor networks," in Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, 2010, pp. 413-418. [37] B. A. Sabarish and K. SashiRekha, "Clustering based energy efficient congestion aware protocol for Wireless Sensor Networks," in Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on, 2011, pp. 1129-1135. [38] H. Inanlou, K. S. Shourmasti, H. Marjani, and N. A. Rezaei, "FFDA: A tree based energy aware data aggregation protocol in wireless sensor networks," in Wireless Information Networks and Systems (WINSYS), Proceedings of the 2010 International Conference on, 2010, pp. 1-5. [39] W. Bober, L. Xiaoyun, and C. Bleakley, "TrickleTree: A Gossiping Approach to Fast Staggered Scheduling for Data Gathering Wireless Sensor Networks," in Sensor Technologies and Applications (SENSORCOMM), 2010 Fourth International Conference on, 2010, pp. 214-219. [40] Iman ALMomani, Maha Saadeh, Mousa AL-Akhras, and H. A. Jawawdeh, "A Tree-Based Power Saving Routing Protocol for Wireless Sensor Networks," International Journal Of Computers And Communications, vol. 5, 2011, 2011.

T

Saqib Ali received his B.Sc. Engineering in 2000, M.Sc. Computer Science in 2003, and M. Phil in Communication & Networks in 2005 from University of Agriculture Faisalabad (UAF), Pakistan. In 2004, he appointed as an Assistant Network Engineer at UAF-Pakistan. Since 2005, he is working as a lecturer in the Department of Computer Science, UAFPakistan. Currently, he is a PhD candidate in the faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Malaysia. His research interest includes cross layer optimization, multi channel multi radio wireless mesh networks and resource management in wireless communication systems.

R

Muhammad Faheem received the B.Sc. Computer Engineering degree in 2010 from the Department of Computer Engineering at the University College of Engineering & Technology, Bahauddin Zakariya University Multan, Pakistan. In 2012, he received an MS degree in Computer Science from the Faculty of Computer Science and Information System at Universiti Teknologi Malaysia. He is currently serving as a lecturer in Computer Science Department, Comsat Institute of Information and Technology Vehari Campus, Pakistan. His research interests include the areas of Energy Efficient Routing in Wireless ad-hoc and Sensor Network. E-mail: [email protected]

Basit Raza received the Master in Computer Science degree in 2008 from the Department of Computer Science at the University of Central Punjab, Lahore, Pakistan. He is pursuing PhD in Computer Science degree from International Islamic University, Islamabad, Pakistan. He conducted PhD research in Soft Computing Research Group (SCRG), Computer Science and Information System at Universiti Teknologi Malaysia. Currently he is working as Lecturer in COMSATS Institute of Information Technology, Islamabad, Pakistan. His research interests include the areas of Energy Efficient routing in wireless ad-hoc and sensor network.

IN

Authors’ information

R

EP

Ziauddin is working as an Associate Professor in Computer Science Department at Comsats Institute of Information & Technology, Vehari Campus (COMSATS), Pakistan. His area of research includes Software Engineering, Software Process Improvement, Software Reliability Engineering, Software Development Improvement, Software Quality Assurance, Requirement Engineering and Routing Algorithms in Sensor Networks.

Leyla Sakar received her B.Sc. Software Engineering degree in 2012 from the Faculty of Computer Science and Information systems at Universiti Teknologi Malaysia. Currently she is pursuing her Master’s degree in Software Engineering from University Teknologi Malaysia (UTM). Her research interests include the areas of Artificial Intelligence, Optimization techniques and sensor network algorithm designing.



1022

International Review on Computers and Software (IRECOS) (continued from outside front cover) Optimization of Processor Architecture for Sobel Real-Time Edge Detection Using FPGA by Fawnizu Azmadi Hussin, Zahraa E. M. Osman, Likun Xia, Noohul Basheer Zain Ali

970

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1012

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Abstracting and Indexing Information: Cambridge Scientific Abstracts (CSA/CIG) Academic Search Complete (EBSCO Information Services) Elsevier Bibliographic Database - SCOPUS Index Copernicus (Journal Master List): Impact Factor 6.14 Autorizzazione del Tribunale di Napoli n. 59 del 30/06/2006

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