Optimization_of_DEEC_Routing_Protocol_using_Genetic_Algorithm.pdf

8/16/2019 Optimization_of_DEEC_Routing_Protocol_using_Genetic_Algorithm.pdf

1/7

376 | International Journal of Computer Systems, ISSN-(2394-1065), Vol. 03 , Issue 05, May, 2016

International Journal of Computer Systems (ISSN: 2394-1065), Volume 03 – Issue 05, May, 2016 Available at http://www.ijcsonline.com/

Optimization of DEEC Routing Protocol using Genetic Algorithm

Amandeep Kaur, Mandeep Kaur

Department of ECE, Baba Banda Singh Bahadur Engineering College

Fatehgarh Sahib, India

Abstract

Due to the advancements in wireless communication, information technologies and electronics field, in recent years theWSN have gained so much attention. They consist of large no. Of sensor node that are usually deployed randomly overan area to be observed, collects data from sensor field and transmit data to base station. Because node sensors areenergy limited so to increase network lifetime is important factor. Energy saving is also an important design issue in theWSNs routing design. Distance between the nodes and BS and distance between nodes they are the factors that causeenergy dissipation. Applying genetic algorithms (GAs) in finding energy efficient shortest route for WSNs is emerging asan important field. GA could be very helpful in providing optimized solution to energy efficient shortest path problem in

WSN. Distributed Energy Efficient Clustering (DEEC) can be defined as a clustering based algorithm in which clusterhead is preferred on the behalf of probability of ratio of residual energy and average energy of the network. In this paper genetic algorithm is applied on DEEC routing protocol to enhance network lifetime.

Keywords: Wireless Sensor Networks, Stability period, Energy efficiency, SEP protocol, DEEC protocol, Genetic Algorithm (GA).

I. I NTRODUCTIONA large range of sensing element nodes that are unit

densely deployed over a large geographical region andnetworked through wireless links are used for the making

of wireless sensor networks. Each sensor node in WSN hascapability to communicate with each other and base stationis used for the data integration and circulation. In WSNeach and every node can become transmitter and receiver[12]. Energy-efficient protocols should be designed for thecharacteristic of WSN to extend the network lifetime. Inorder to reduce the energy consumption, sensor nodes areefficiently organized into clusters. On the basis ofclustering structure, many energy-efficient routing

protocols are designed. The clustering techniques arehelpful for performing data aggregation, which combinesthe data from source nodes into a little set of significantinformation. The fewer messages are transmitted under thecircumstance of achieving enough data rate specified byapplications for increasing energy saving. [7]

For processing, sensor networks include a many datafor an end-user. Therefore, there is a requirement ofautomated methods for combining or aggregating the datainto a little set of significant information. [18]

Once the network is established, it start sensing theinformation and the energy of the nodes goes on dissipatingwhenever they obtain a little information and send it toother nodes or BS. The nodes can be made more energyefficient by using routing protocols. [2]

Fig .1 Wireless sensors network

Clustering Hierarchy

In WSN nodes are not invariably same they could beheterogeneous that increase network complexness. Toincrease stability and reduce the energy consumptioncluster is essential technique in WSN.

Fig. 2 Clustering Hierarchy


2/7

Amandeep Kaur et al Optimization of DEEC Routing Protocol using Genetic Algorithm


In [1] compression of SEP and DEEC protocols has been analysis in which DEEC found best in compressionwith SEP routing protocols. So in this work DEEC

protocols is going to be optimized using genetic algorithmto improve energy efficiency.

II. REVIEW OF CLUSTERING ALGORITHMS FOR WIRELESS SENSOR NETWORK

A. SEP (Stable Election Protocol)A Stable Election Protocol (SEP) is for clustered

heterogeneous wireless sensor networks. HeterogeneousWSN Nodes have different energy levels. In SEP, many ofthe elevated energy nodes are referred to as advancednodes and the chance to become CHs is more in advancednodes as compared to non-advanced nodes. In advancednodes, extra energy is taken off by SEP. [15]

Fig3. Flow chart of CH selection in SEP protocol

Advantage:

SEP is scalable and dynamic, even normal node can be selected.

In SEP, no universal knowledge is required at everyround.

No earlier distribution is assumed of energy levels.

Limitations:

The drawback of SEP method is that the election ofthe cluster heads is not dynamic among the twotypes of nodes, which results that the nodes willdie first that are far away from the powerful

nodes.

B. DEEC (Distributed Energy Efficient)DEEC use the residual and initial energy level of the

nodes to select the cluster-heads. At every election round,DEEC does not require any universal knowledge of energy.

Distributed Energy Efficient Clustering (DEEC) can be

defined as a clustering based algorithm in which clusterhead is preferred on the behalf of probability of ratio ofresidual energy and average energy of the network. Therouting time in number of round is different according to itsresidual and initial energy for each node. In this algorithm,the nodes with low-energy will have lesser chances to bethe cluster heads as compared to the high Initial andresidual energy nodes. In a two-level heterogeneousnetwork, where there are two types of nodes, m.Nadvanced nodes with initial energy equal to Eo.(1+a) and(1 − m). N normal nodes, in which the initial energy isequal to Eo. Where a and m are two variable which managethe nodes percentage types (advanced or normal) and thetotal initial energy in the network Etotal [7].

•The value of Total Energy i s given as

Etotal = N.(1−m).Eo+N.m.Eo.(1+a ) (1)

•The average energy of rth round is set as follows

E(r)=1/NEtotal(1−R ) (2)

R denotes the total rounds of the network lifetime andis defined as

R=Etotal/ERound (3)

Fig4. Flow chart of CH selection in DEEC protocol


3/7



Advantages:

At every election round, DEEC does not want anyuniversal knowledge of energy.

DEEC can perform multi-level heterogeneouswireless network.

Limitations:

Advanced nodes always punish in the DEEC, particularly once their residual energy reducedand become in the variety of the normal nodes.

In this position, the advanced nodes die rapidlythan the others.

C. Genetic AlgorithmGenetic Algorithm is used to make cluster member,

cluster head and next cluster dynamically, which is used tocalculate average fitness and increase life time of thenetwork. [3]

1. Population : A population is gathering of numerouschromosomes and the best chromosome is working tocome up with next population. Initially the GA starts with a

population of predefined variety of chromosomes andrandomly selected cluster heads. Each chromosome isevaluated by GA by calculate its fitness. GA selects bestsuitable chromosome after the evaluation of fitness andthen applies crossover and mutation. [3]

2. Fitness Calculation: The fitness function is designed toincrease the network lifetime, which evaluates whether, a

particular chromosome increases network lifetime or not.The algorithm conserve the historically obtained most

excellent chromosome, that is, with the highest fitnessvalue, called elitism. The fitness of each chromosome isconsidered by

where di denotes the distance between the (i+1)thenode (or, gene) and the ith node denotes the data gatheringchain. A longer data gathering chain is indicated by highervalue of the chromosome energy and which means to be aninferior solution. [15]

3. Selection The process of determining in which two

chromosomes will assistant to form a newchromosome is known as selection.

The chromosomes with higher fitness values havemore chances to of matting. [11]

4. Crossover

Crossover is a binary genetic process usefulon two chromosomes. It recombines the geneticmaterials of two parent chromosomes to create achild chromosome. The results of the crossoverare depending on the selection procedure.

Fig 5. General scheme of GA mechanism.

5 Mutation

The mutation is an exploration process whichtransforms genes to overcome the limitation of thecrossover.

In this paper, this operation enables the search for

optimal chromosome by transforming a cluster-head to acluster member and a cluster member and a cluster-head,with a small probability. The probability of transformingfrom cluster member to cluster-head is set higher than thatof the opposite case for preventing abnormal increase ofcluster-heads., clusters should be reconstituted afterexecuting the crossover and mutation, since the cluster-heads’ positions could have been shifted. [4]

Fig 6. Flowchart of implemented scheme


4/7



Fig 7. Flow chart of implemented scheme of GA

Fitness Parameters

The fitness of a chromosome is designed to increase thenetwork life time and to reduce the energy consumption.Some of fitness parameters are described in this segment.

1) Direct Distance (DD) to Base Station: It is definedas the sum of all distances from sensor nodes to the basestation. Therefore this distance is defined as follows:

(4)

Where dis is the distance between node i and BS nodes. For a longer network, this distance should be minimized;otherwise, the energy will be wasted of most of the nodes.However, for a smaller network, direct transfer to BS is to

be fine.

2) Cluster Distance (C): The cluster distance, C can bedefined as the sum of the distances from the nodes to thecluster head and the distance between head and BS. For acluster having k member nodes, the cluster space C isdefined as follows:

(5)Where dih is the distance between nodes i and cluster

head h and dhs is the distance between cluster head h andBS node s. For a cluster having large number of widely-

spaced nodes, the cluster distance is high and thus theenergy consumption will also be higher. C should not betoo large for reducing energy consumption. Size of theclusters will be controlled by this metric.

3) Cluster Distance - Standard Deviation (SD): The

variation in the cluster distances should not be large foruniform spatial allocation of sensor nodes, where nodes areuniformly placed. However, for non-uniform spatialdistribution, the cluster distances must not be necessarilythe same where nodes are randomly placed. According tothe deployment information the variation in clusterdistances should be tuned. Variation in cluster distanceswill show poor network configuration if the deployment isuniform and must be tuned to get uniform clusters. [14]The cluster distances, SD, with a deviation μ can beconsidered as follows:

(6)

(7)

III. SIMULATION RESULT

A. Transmitted data SEP :

This figures represent the transmitted data of SEP inwhich number of nodes are 200 and maximum number ofrounds are 2000 .The red represent the dead nodes and bluerepresent alive nodes.


5/7



(1) Count of Cluster heads SEP:

The figure shows the count of cluster heads in SEP.

(2) Dead Nodes:

The figure shows that in SEP, dead nodes start from1000.

(3) Packets to Cluster Heads:

This figure shows that the packets to cluster heads isconstant upto 1000 rounds and decreases after 1000 roundsdue to dead nodes.

B (1) DEEC Clusters:

This figure shows the DEEC clusters and representsdead nodes, alive nodes and sink.

(2)Dead nodes:

This figure shows the dead nodes of DEEC and deadnodes start from 1500 rounds.

(3) Alive nodes:

This figure represents alive nodes of DEEC in which all

nodes are alive up to 1500 rounds and dead nodes startsafterward.


6/7



( C) Gentic alogrithm:

GA is implemented on DEEC protocols and results areshown below:

(1) Dead nodes:

This figure shows the dead nodes of GA on DEEC startfrom 2111 rounds.

(2) Alive nodes:

This figure represents alive nodes of GA on DEEC inwhich all nodes are alive up to 2111 rounds and dead nodesstarts afterward.

(D )Comprsion of sep, deec & GA on DEEC

(1) Dead Nodes:

Figure D(1) shows the comparison of SEP , DEEC &GA Dead nodes in which dead nodes of SEP starts from1000 and of DEEC starts from 1300& GA on DEEC startsfrom 2111.

(2) Alive nodes:

This figure show the comparison of alive nodes of SEP,DEEC & GA .In which nodes are alive up to 1000 of SEPand up to 1300 rounds of DEEC & GA on DEEC startsfrom 2111.

IV. CONCLUSIONThe main motive of designing energy efficiency

protocol is to increase the network lifetime and improve theenergy efficiency of the wireless network. The proposedwork is based on the comparison between the conventionalDEEC protocol and the optimized DEEC using GA. Thenodes are deployed in the network and the performance

parameters of the network are evaluated after applyingGenetic Algorithm on DEEC protocol. Genetic Algorithmis helpful in searching energy-efficient clusters for sensornetworks. Total energy consumption is concerned with thenumber of cluster-heads and their position. It is clear aftercomparison that optimized DEEC using GA is better andimproving the network lifetime.

R EFERENCES [1] Amandeep kaur, M andeep kaur “Perform ance Comparative of SEP

and DEEC Protocol in Wireless Sensor Network”IETE,April2016,pp 70[2] F. Akyidiz, Y. Sankarasubramaniam W. Su,and E. Cayirci, "A

survey on sensor networks",IEEE Commun, August 2002.[3] Ali Norouzi and A. Halim Zaim “Genetic Algor ithm Application in

Optimization of Wireless Sensor Networks” the Scientific WorldJournal Volume 2014, pp 15.

[4] A. Zahmatkesh and M. H. Yaghmaee “A Genetic Algorithm -BasedApproach for Energy- Efficient Clustering of Wireless Sensor

Networks” Internation al Journal of Information and ElectronicsEngineering, Vol. 2, No. 2, March 2012

[5] G. Smaragdakis, I. Matta, A. Bestavros, "SEP: A Stable ElectionProtocol for clustered heterogeneous wireless sensor networks",Second International Workshop on Sensor and Actor NetworkProtocols and Applications (SANPA 2004), 2004.

[6]

Jiun-Jian Liaw, Chen-Yi Dai, Yi- Jie Wang “The Steady ClusteringScheme for Heterogeneous Wireless Sensor Networks “ inProceeding of Ubiquitous, Autonomic and Trusted Computing,2009. UIC-ATC '09. Symposia and Workshops on 7-9 July 2009.


7/7



[7] L. Qing, Q. Zhu, M. Wang, "Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensornetworks". ELSEVIER,Computer Communications 29, pp 2230-2237, 2006.

[8] Manjeshwar A, Grawal DP. “TEEN: A protocol for enhancedefficiency in wireless sensor networks”. In Proc. of the 15th Paralleland Distributed Processing Symp. San Francisco: 2001. vol. 3,

pp.30189a[9] M. Handy, M. Haase ,and D.Timmermann, Low energy adaptive

clustering hierarchy with deterministic cluster – head selection , in proceeding of IEEE, International Conference on Mobile andwirless Communications networks, Stockholm, Sweden,2002, pp.368-372

[10] Ming Yu, Leung, K.K. and Malvankar, A. “A dynamic clusteringand energy efficient routing technique for sensor networks”. IEEEon Wireless Communications, Vol: 6(8): pp3069-3079, August2007.

[11] Mohammed Abo-Zahhad, Sabah M. Ahmed1, Nabil Sabor1 andShigenobu Sasaki “ A New Energy -Efficient Adaptive ClusteringProtocol Based on Genetic Algorithm for Improving the Life timeand the Stable Period of Wireless Sensor Networks” InternationalJournal of Energy, Information and Communications Vol.5, pp.47-72. 3 (2014) .

[12] Monica R Mundada (2012), “A Study on Energy Efficient RoutingProtocols in Wireless Sensor Networks “International Journal ofDistributed and Parallel Systems (IJDPS) Vol.3, No.3,Pp 311-330.

[13] P. Krishna, N.H. Vaidya, M. Chatterjee, D. Pradhan, A cluster- based approach for routing in dynamic networks, ACM SIGCOMMComputer Communication Review 27 (2) (1997) 49 – 65.

[14] Sajid Hussain, Abdul Wasey Matin, Obidul Islam “GeneticAlgorithm for Hierarchical Wireless Sensor Networks” Journ al ofnetworks, VOL. 2, No. 5, September 2007.

[15] Sharma T,Kumar B,Tomar G.S “Performance Comparision ofLEACH, SEP and DEEC Protocol in Wireless Sensor Network”Proc. of the Intl. Conf. on Advances in Computer Science andElectronics Engineering ,2012

[16] V. Mhatre, C. Rosenberg, D. Kofman, R. Mazumdar, N. Shroff,Design of surveillance sensor grids with a lifetime constraint, in: 1stEuropean Workshop on Wireless Sensor Networks (EWSN),Berlin, January 2004.

[17] Vinay Kumar Singh, Vidushi Sharma “Eli tist Genetic algorithm based energy efficient routing scheme for wireless sensorsnetworks” International Journal Of Advanced Smart Sensor

Network Systems ( IJASSN ), Vol 2, No.2, April 2012.[18] W.R. Heinzelman, A.P. Chandrakasan, H. Balakrishnan,

Energyefficient communication protocol for wireless microsensornetworks,in: Proceedings of the 33rd Hawaii InternationalConference on System Sciences (HICSS-33), January 2000.

Documents

Optimization_of_DEEC_Routing_Protocol_using_Genetic_Algorithm.pdf