Technological development in wireless communication enables the development of smart, tiny, low cost and low power sensor nodes to outperform for various applications in Wireless Sensor Networks. In the existing Tabu search algorithm, clusters are formed using initial solution algorithm to conserve energy. We propose a Cluster Based Energy Aware Routing (CEAR) algorithm to maximize energy conservation and lifetime of network with active and sleep nodes. The proposed algorithm, removes duplication of data through aggregation at the cluster heads with active and sleep modes. A comparative study of CEAR algorithm with Tabu search algorithm is obtained. Comparative study shows improvement in the Lifetime and energy conservation by 17 and 22 % respectively over the existing algorithm

Iyengar, S.S.

Leelavathi, R.

Patnaik, L.M.

Sivasankari, H.

Vallabh, M.

Venugopal, K.R.

ePrints@Bangalore University

Chapter 4CEAR: Cluster based Energy AwareRouting Algorithm to Maximize Lifetimeof Wireless Sensor Networks (WSNs)H. Sivasankari, R. Leelavathi, M. Vallabh, K. R. Venugopal,S. S. Iyengar and L. M. PatnaikAbstract Technological development in wireless communication enables thedevelopment of smart, tiny, low cost and low power sensor nodes to outperformfor various applications in Wireless Sensor Networks. In the existing Tabu searchalgorithm, clusters are formed using initial solution algorithm to conserve energy.We propose a Cluster Based Energy Aware Routing (CEAR) algorithm to maxi-mize energy conservation and lifetime of network with active and sleep nodes. Theproposed algorithm, removes duplication of data through aggregation at the clusterheads with active and sleep modes. A comparative study of CEAR algorithm withTabu search algorithm is obtained. Comparative study shows improvement in theLifetime and energy conservation by 17 and 22 % respectively over the existingalgorithm.Keywords Clusters  Delay  Energy aware routing  Lifetime and wireless sensornetworksH. Sivasankari (&)  R. Leelavathi  M. Vallabh  K. R. VenugopalDepartment of Computer Science and Engineering, University Visvesvaraya College ofEngineering, Bangalore University, Bangalore 560 001, Indiae-mail: sankari@yahoo.comS. S. IyengarFlorida International University, Miami, USAL. M. PatnaikIndian Institute of Science, Bangalore, IndiaV. V. Das (ed.), Proceedings of the Third International Conference on Trends in Information,Telecommunication and Computing, Lecture Notes in Electrical Engineering 150,DOI: 10.1007/978-1-4614-3363-7_4,  Springer Science+Business Media New York 2013314.1 IntroductionWireless Sensor Networks (WSNs) are deployed with hundreds or thousands ofcomputable and low cost sensors. These sensor nodes are multi-functional andwith computing capabilities. These sensor nodes are integrated on a single board ina few cubic inches with embedded microprocessor, radio receivers, sensing, andcomputing and communication unit. They are powered by 50 W and can last for2–3 years with very less duty cycling and these sensor nodes are prone for failures.In WSN there are three types of communications, they are clock driven, eventdriven and query driven. In the clock driven approach, data collection and trans-mission takes place at regular periodic intervals. In the event driven and querydriven approaches, data collection is triggered by events or queries. Data Aggre-gation, Clustering, effective routing and data compression, min–max and averag-ing methods are used to reduce energy consumption in WSNs. Tabu searchalgorithm [1] form clusters of sensor nodes and routes data from source to thedestination through cluster heads. The sensor nodes are active and transmits datawithout aggregation all the time and this approach consumes more energy as thereis no aggregation. We propose Cluster based Energy Aware Routing (CEAR) inorder to maximize lifetime and energy conservation of WSNs. In a cluster, sensornode works in active and sleep mode randomly. It routes the data from source todestination through cluster head with aggregation. Thus, it reduces the energyconsumption for sending the large set of data. The rest of the paper is organized asfollows: Related work is discussed in Sect. 4.2. Problem Definition is formulatedin Sect. 4.3. Algorithm is developed in Sect. 4.4. Simulation and PerformanceAnalysis are analyzed in Sect. 4.5. Conclusions are presented in Sect. 4.6.4.2 Literature SurveyHeinzelman et al. [2] developed a Low Energy Adaptive Clustering Hierarchy(LEACH) algorithm for clustering. LEACH is a distributed approach and itrequires no control information from the base station. In this approach data isrequired to be sent in the allotted time. Madan et al. [3] proposed TAG: TinyAggregation Service for Ad-Hoc Sensor networks. Interface for data collection,aggregation, executed aggregation queries in the sensor networks. Aggregationqueries are executed in time, efficiently. Each mote is required to transmit only asingle message per epoch, regardless of its depth in the routing tree. It is sensitiveto resource constraints and lossy communication. Liang et al. [4] have designed agenetic cost model for data gathering in sensor networks, as routing tree used forquery evaluation and therefore the network is balanced. The proposed heuristicalgorithm showed that online gathering problem is NP complete. It is focused onlyon the event driven data and data gathering queries are assumed sequential. Ghiasiet al. [5] proposed a balanced K clustering algorithm to minimize energy32 H. Sivasankari et al.consumption using minimum cost network flow. Agarwal et al. [6] have proposedsub exponential algorithms to solve the K-center problem to compute the optimalK-center. They considered the metrics for planar case, i.e., smaller dimensions.Noritaka et al. [7] have proposed centralized and distributed approaches forClustering. These methods prolong the network lifetime than the conventionalmethods.4.3 Problem Definition and Mathematical ModelGiven a set of Wireless Sensor Nodes Si 2 V where i = 1, 2,…, n. we consider asingle sink with maximum coverage radio and power. The objectives are to reducethe energy consumption and to maximize the lifetime of the network. Theassumptions are (i) All source sensor nodes are static. (ii) Links in between thenodes are bidirectional. (iii) Sink has long communication range and energy thanthe source sensor nodes (iv) The highest energy node becomes the cluster head.(v)Sensor nodes act in either active or sleep mode.4.3.1 Mathematical ModelAll sensor nodes are deployed with equal amount of energy. Clusters are formedbased on Euclidean distances. Cluster size is increased as the number of nodeincreases in the deployment. Graph (G) i.e., G = (V, E) where V is a set ofvertices and E is a set of edges. Neighbor of a node is selected based on thefollowing constraints. In a graph, vi is the source node and vj next neighbor node toreceive data as the destination. The distance between source and destination iscalculated based on Euclidean distance.Dist vi  vj \ Range ð4:1ÞEuclidean Distance ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðxi  xjÞ2 þ ðyi  yjÞ2qð4:2ÞList of Neighbors L Nið Þ ¼[Vj2VfVj=Dist vj; vi \rg ð4:3ÞTheorem Aggregation at the cluster head removes duplication of data thus itincreases energy conservation and lifetime.C eð Þ ¼Xni¼1Cið Þ nið Þ ð4:4Þ4 CEAR: Cluster based Energy Aware Routing Algorithm 33A eð Þ ¼ l vð Þ 1 ruvXeð Þ ð4:5ÞPtotal ¼ T eð Þ þ A eð Þ þ R eð Þ þ C eð Þ ð4:6ÞWhere T(e) is Transmission cost, A(e) is Aggregation cost, ruv is reduction ratioand Xe is the Aggregation factor if Xe = 1 there is an aggregation else there is noaggregation, R(e) Reception cost, C(e) Cluster formation cost, load on node v, Cirepresents the ith cluster and ni is the number of nodes in a cluster.4.4 CEAR: Cluster Based Energy Aware Routing AlgorithmIn a Cluster based Energy Aware Routing (CEAR), clusters are formed as thenodes are deployed in the region. When the first node is deployed, the cluster isformed and it becomes the cluster head. Later, the deployed nodes identify theneighboring nodes by sending hello packets. When it receives successive echopackets from neighboring nodes then it is added to the cluster. If the node is neitherin the range nor in neighbor list of any cluster then a new cluster is formed andnode becomes the cluster head. As the more and more nodes are deployed, thecluster size is increased. This procedure is repeated until there is no more node tobe deployed.In a cluster, after each iteration it checks for the highest energy node. It electsthe highest energy node as the cluster head. In a cluster, a set of source nodes act inactive and sleep mode. Before a node goes for sleep mode it ensures that enoughnumber of nodes are in active mode in order to ensure that any event during thetransition of active and sleep mode is not missed. Energy Aware Routing helps thedata to be routed through cluster heads (Table4.1).4.5 Simulation and Performance AnalysisIn the setup of MATLAB simulation, a 100 9 100 m region is considered with150 sensor nodes. All sensor nodes have equal amount of energy initially with50 J. Radio model is lossy in nature and communication range is 150 m, energyconsumption per bit is 60 pJ. Control packet size is 500 bytes. Fig 4.1 shows thegraph between the energy consumption with number of sensor nodes or clusters.The proposed algorithm CEAR consumes lower energy than the existing algorithmTabu. The energy savings in CEAR is 22 % higher than the existing Tabu searchalgorithm. Fig 4.2 depicts that the lifetime is increased by 17 % in CEAR algo-rithm in comparison with the existing algorithm.34 H. Sivasankari et al.Table 4.1 Cluster based energy aware routing algorithmThe Subgraph G’ [ G, V nodes Ni.Beginwhile deployment of nodes is True do N ++;if(Id_Nj == 1) then Cluster C1; Nj as Cluster Head for C1;else neighbor node selection() get node id of Nj i.e. id_Nj;for n 1 to N doif hellojn = = 1 then Add node Nn & Nj as neighbors;Update Neighbor list;endifendforfor i 1 to M do for n 1 to N doif id_Nj [ neighbor Nn & & in_range(Ci) then Make node Nj [ Ci; return;else if (n == N && i == M) then M ++; Create new Cluster CM;Elect node Nj as Cluster head for CM;endif return;endif, endfor, endif, endwhileK = ActiveNode(); Cluster Head Election Algorithmfor i 1 to M dofor p 1 to K do V kp [ Ci;if Pup tð ÞPvp tð ÞSelect active node vp(t) at time t as Cluster Head to the current Cluster Ci;elseSelect active node up(t) at time t as Cluster Head to the current Cluster Ci;endif endfor endforif event then Select V kp [ Ci at time t;if(dist(event, kp) B min) then min = dist (event, kp); Si = kp;endif, endifRoute : for non sink active pair(u, v) dofor neighbor j to k of Si doif Near Sj = active() thenmake Sj as active node;endifEuclidean_distance = Euclidean_distance ? dist(i, j); Si = Sj;endfor Compute minimum Euclidean Distance;Calculate fusion benefit dactive(u, v) V kp [ Ci;if dactive (u, v) == 0 thenSelect active pair(u, v) as non fusion pairs [ set En;else Select active pair(u, v) as fusion pairs [ set Ef;endifif Si+1 = St then $go to Route;else return;endif, End4 CEAR: Cluster based Energy Aware Routing Algorithm 354.6 ConclusionsWireless sensor networks works with limited battery and lifetime. We proposeCluster based Energy Aware Routing (CEAR) to conserve energy and maximizethe life time of the WSNs. In the case of Tabu search algorithm, clusters areformed and routed through clusters heads without aggregation and active/sleepmodes. Therefore, it consumes more energy. The data aggregation and periodicactive/sleep mode in CEAR algorithm improves the life time and energy conser-vation, This work can be enhanced in future to reduce delay.50 100 150200300400500600700800Number of Nodes in NetworkLifetime (Iterations)TabuCEARFig. 4.1 LifetimeComparison between TabuSearch and CEAR5 10 15 2000.20.40.60.811.21.41.6Number of Cluster Heads in Routing PathEnergy Consumption (nJ)TabuCEARFig. 4.2 Energyconsumption versus numberof cluster heads in routingpath36 H. Sivasankari et al.References1. El Rhazi A, Pierre S (2009) A Tabu search algorithm for cluster building in wireless sensornetworks. IEEE Trans Wirel Comput 8(4):433–4442. Heinzelman W, Chandrakasan A, Balakrishnan H (2002) An application specific protocolarchitecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–6703. Madden SR, Franklin MJ, Hellerstein JM, Hong W (2002) TAG: tiny aggregation service forad-hoc sensor networks. In: Proceedings of the fifth symposium operating systems design andimplementation (OSDI 02), pp 131–1464. Liang W, Liu Y. (2007) Online data gathering for maximizing network lifetime in sensornetworks, IEEE Trans Mobile Comput 6(1):2–115. Ghiasi S, Srivastava A, Yang X, Sarrafzadeh M (2002) Optimal energy aware clustering insensor networks. IEEE Trans Mobile Comput 2:258–2696. Agarwal PK, Procopiuc CM (2002) Exact and approximation algorithms for clustering.Algorithmica 33(2)201–2267. Shigei N, Miyajima H, Morishita H, Marda M (2009) Centralized and distributed clusteringmethod for energy efficient wireless sensor networks. In: Proceedings of the InternationalMulticonference of Engineers and Computer Scientists(IMECS), Vol.2174, No 1, LNCS,pp 423–4274 CEAR: Cluster based Energy Aware Routing Algorithm 37

CEAR: Cluster based Energy Aware Routing Algorithm to Maximize Lifetime of Wireless Sensor Networks (WSNs)

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CEAR: Cluster based Energy Aware Routing Algorithm to Maximize Lifetime of Wireless Sensor Networks (WSNs)

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