Mobile Adaptive Distributed Clustering Algorithm for Wireless Sensor Networks

Transcription

1 Internatonal Journal of Computer Applcatons ( ) Volume No.7, Aprl Moble Adaptve Dstrbuted Clusterng Algorthm for Wreless Sensor Networks S.V.Mansekaran Department of Informaton Technology Anna Unversty of Technology Combatore, Inda R.Venkatesan Department of Computer Scence and Engneerng PSG College of Technology Combatore, Inda G.Devana Department of Informaton Technology Anna Unversty of Technology Combatore, Inda ABSTRACT In Wreless Sensor Network (WSN), energy optmzaton s an mportant factor to ncrease the lfetme of the network. Exstng approaches manly dscuss on routng data towards the snk and also do concentrate on statc wreless sensor network. As these approaches consume more energy, ths paper ntroduces Moble Adaptve Dstrbuted Clusterng Algorthm (MADCA) that can mnmze the energy consumpton and also support moble nodes. Ths algorthm acheves energy optmzaton by clusterng the nodes, based on smlarty of data. Also the nodes whch have low data sendng rate are allotted a sleep duty cycle for some perod. In order to support moble nodes, the clusters are rebult accordng to the clusterng perod. Thus t reduces the burden of snk and mproves the lfetme of the network. Ths scenaro s smulated usng Network Smulator NS and performance s analyzed. Smulaton results show that MADCA s effcent n terms of control overhead, average end-to-end delay, average packet delvery rato and energy consumpton when compared to a recently proposed approach based on clusterng. General Terms Clusterng, Energy consumpton, Moblty, Wreless sensor network. Keywords Data sendng rate, Dstrbuted clusterng, Smlarty measure.. INTRODUCTION Recent advances n computng and communcaton have paved way for the development of sensor nodes that constructs the sensor network. The low power sensor nodes enable the sensor network to emerge as a platform for survellance and control applcatons. The communcaton of nodes n the sensor network s acheved through wreless communcaton. It s not needed to pre-determne the poston of nodes [9]. Wreless Sensor Network (WSN) belongs to Low Range Wreless Personal Area Network (LR-WPAN) [] group. In ths network, the sensor nodes cooperate wth each other to dstrbute the gathered nformaton. One or more specal nodes called snk collects the nformaton from sensor nodes. The sensor network s used n varous applcaton areas such as mltary, home and health montorng. The characterstcs of WSN are dense deployment, lmted resource, lmted energy, and dynamc topology. The man ssue of the sensor node s ts lfe tme snce t has very low energy. The sensor node consumes energy for sensng, computng and communcaton. To transmt a message, t requres twce the energy t takes to receve the message. Energy consumpton has to be reduced because of hghly lmted battery energy. It s very dffcult to replace the battery of the sensor nodes. The energy wastage occurs because of retransmsson, collson, dle lstenng and control packet overhead. So ncreasng the lfetme of the sensor node by reducng the energy consumpton s a major ssue n wreless sensor networks. The protocols used to reduce energy consumpton are classfed nto three: [8]. Protocols that control the transmsson power of a node by ncreasng the capacty of the network.. Protocols that take routng decsons based on power optmzaton goals. 3. Protocols that determne the workng schedule of the nodes. Generally the nformaton collected by all the sensor nodes s sent to the snk. It ncreases the overhead of snk though the snk s more powerful than ordnary nodes n terms of energy and processng power. To reduce the overhead of snk, the nodes mght be clustered. Clustered sensor networks are classfed nto heterogeneous and homogeneous based on the processng capablty, hardware and functonaltes [7]. Clusterng plays an mportant role n effcent energy usage of wreless sensor network. It clusters the sensors nto group and enables them to communcate wth the cluster heads. Instead of collectng data from every node, the snk collects only from cluster heads. But, there are several challenges n clusterng the network [6]. They are network deployment, type of network, unformty n energy consumpton, mult hop or sngle hop communcaton, and dynamc clusterng. Moblty n sensor network s neluctable because of the advancement n the networkng area. The moblty of sensor nodes supports better coverage and the moblty of snk reduces the sensor node s energy consumpton. Thus moble wreless sensor network s energy effcent and t also has better coverage when compared to statc wreless sensor network. The major mportant ssue n moble wreless sensor network s topology management. Ths paper presents an algorthm called Moble Adaptve Dstrbuted Clusterng Algorthm (MADCA) for reducng the overhead of snk by clusterng the sensor nodes that are moble. In order to address the above sad ssues n clusterng, the algorthm takes the smlarty of data to cluster the network. Smlarty of data s dentfed by data sendng rate, dstance

2 Internatonal Journal of Computer Applcatons ( ) Volume No.7, Aprl between node & snk and also the data generated by the sensor nodes. Ths helps to put the nodes that are smlar n sleep. In order to support dynamc clusterng as the nodes are moble, the clusters are rebult accordng to the clusterng perod. By avodng some of the nodes from sensng and transmttng the data to snk, the energy consumpton can stll be mnmzed. Thus t helps to mprove the network lfetme.. RELATED WORK Many clusterng algorthms have been developed n order to utlze the energy n an effcent way. The most basc and relevant algorthms are mentoned here to know how nodes have been clustered so far. Wend Rabner Henzelman et al. [] have proposed a clusterng-based protocol LEACH (Low-Energy Adaptve Clusterng Herarchy) that allows rotaton of cluster heads n a randomzed manner n order to dstrbute the energy load among the nodes n the network. The sensors themselves form a cluster and one among them acts as a cluster head. The cluster heads are gettng changed perodcally. LEACH compresses the data that s beng sent from the cluster to base staton by data fuson. By dstrbutng the energy among the nodes, LEACH reduces the energy usage. But as the cluster head selecton s done n the earler stage, changes such as addton, removal, transfer of nodes may not be possble. Ossama Youns and Sona Fahmy [8] have presented HEED (Hybrd, Energy-Effcent Dstrbuted Clusterng) that selects cluster heads based on resdual energy and node degree. The cluster heads are updated perodcally. Communcaton cost of the nodes s also the factor that helps to decde the cluster head. Udt Sajjanhar and Pabtra Mtra [4] have analyzed the problem of adaptve clusterng n terms of data reportng rates and resdual energy of each node wthn the network by proposng DEEAC (Dstrbutve Energy Effcent Adaptve Clusterng) protocol. It takes two parameters for choosng the cluster head:. Resdual energy of a node,. Hotness of regon sensed by the node. Hot regons are referred as the regons that have hgh data generaton rate. The value of the hotness s the data generaton rate of the network. In ths approach, the nodes belongng to the hot regons have hgh probablty of becomng as the cluster heads. Chong Lu et al. [5] have gven a method called EEDC (Energy- Effcent Data Collecton) that explans a data collecton method whch s based on the data beng sensed and sent to the snk by the sensors. The spatal correlaton of the data s analysed and the sensor nodes are grouped n to clusters. So the sensors n the same cluster have smlar tme seres. In ths method, the sensor nodes are dynamcally clustered usng dssmlarty measure of samplng data. The major components of EEDC are calculaton of dssmlarty, sensor clusterng, sensor schedulng, and data restoraton. The snk node takes more workload to perform clusterng of nodes and ths method does not consder data sendng rate. Mehd Saedmanesh et al. [3] have dscussed EDBC (Energy and Dstance Based Clusterng) that consders both the resdual energy of sensor nodes and the dstance of each node from the base staton when selectng cluster head. Ths protocol reduces the total energy dsspaton on the network. It allows the nodes to evenly utlze the energy. Snce the energy consumpton s dstrbuted, ths protocol helps to mprove the lfetme of the network. But t s only sutable for statc sensor networks. Although the algorthms have dentfed the way to reduce the energy consumpton, MADCA can mnmze energy consumpton stll better wth the use of data generated and ther smlartes along wth data sendng rate. 3. MADCA ARCHITECTURE Ths paper has come up wth the followng proposed archtecture for the mplementaton of MADCA n order to lessen the energy consumpton n WSN. Snk Smlarty measure calculaton Sensed data Smlarty threshold Clusterng of nodes Generaton of sleep duty cycle Sensor Nodes Fg : Proposed Archtecture of MADCA Clusterng perod Re - clusterng The proposed archtecture descrbes that the data sensed by the sensor nodes s sent to the snk, where the smlarty measure s calculated and the value s gven for clusterng of nodes. After the nodes are clustered, the sleep duty cycle s generated based on data sendng rate and gven to the nodes to adaptvely enter nto sleep/awake state. Also for every clusterng perod, reclusterng s performed by the snk whch agan proceeds wth the smlarty measure calculaton. In MADCA, the snk node performs all the tasks needed to cluster the sensor network. Those tasks are descrbed n the followng four phases:. smlarty measure calculaton,. clusterng of nodes, 3. generaton of sleep-duty cycle, 4. re-clusterng for moble nodes. 3. Smlarty Measure Calculaton In ths phase, each sensor node transmts the data to the snk, whch n turn gathers the data from the sensor nodes. After collectng enough data, t estmates the smlarty measure by 3

3 Internatonal Journal of Computer Applcatons ( ) Volume No.7, Aprl consderng par of nodes. For fndng out the smlarty measure, the snk calculates the magntude of data values of the sensor nodes, dstance of the node from the snk and data sendng rate. When data s collected contnuously, general shape and the trend of the phenomena s evolvng curve are affected as magntude determnes them. Hence, n order to evaluate the smlarty of the data, magntude s taken nto consderaton. The magntude of the data s calculated by; Where M s magntude and M dv () dv s data value of node. Dstance does matter n dfferentatng the data sensed by the sensors. So t s also taken nto consderaton n calculatng smlarty measure of the nodes. The dstance of node from snk can be calculated by; D ( x x ) ( y y) () Where D s dstance, ( x, y ) s coordnates of node & ( x, y ) s coordnates of snk. Furthermore, data sendng rate of the node s used n calculatng smlarty measure. It s gven by; R np (3) t Where R s Data sendng rate of node, np s number of packets sent and t s tme. After calculatng the magntude, dstance and data sendng rate, each node s represented by three dmensons. For example node s represented as M, D, R ) ( and node j s represented as M, D, R ). Smlarty Measure (SM) between two ( j j j nodes s based on these coordnates. In order to estmate the smlarty measure between two nodes, Eucldean dstance s used. Snk node estmates the smlarty measure usng the formula: Where SM n ( yx y jx ), n 3 (4) x SM Smlarty Measure of par, y s Dstance and y3 par. y j s Magntude, j rate of node n the par. y s Magntude, s Data sendng rate of node n the y s Dstance and y j3 s Data sendng 3. Clusterng of Nodes The smlarty value calculated n the smlarty measure calculaton phase s gven as an nput to the clusterng algorthm. In ths phase, snk node uses a clusterng algorthm to cluster the sensor nodes by usng the smlarty measure. The smlarty measure of two nodes s compared wth the smlarty measure threshold and the nodes are clustered usng clque-coverng algorthm. The nput of the clusterng algorthm s the graph. The graph s constructed as follows; Each node s consdered as a vertex. Edge s drawn between two nodes, f the smlarty measure of the two nodes s greater than the smlarty measure threshold whch s calculated by consderng samples. Lkewse, smlarty measure of all par wse node s checked out and graph s formed fnally. Ths graph s an nput of clusterng algorthm. For example, consder the Fg. shown below. The graph contans 5 nodes. It s clear from the graph that smlarty measure of node & falls below smlarty threshold and smlarty measure of &3, &5, &5, &4, 5&4 falls above smlarty threshold. Fg : Example of an Input graph to Clusterng The algorthm produces set of clusters as an output. For the graph gven n Fg., the number of clusters would be two as shown n Fg. 3. Cluster 4 4 Fg 3: Cluster set of example Input graph After the clusters are formed, n each cluster, the node wth hghest node degree s chosen as a cluster head. It s mportant that only few sensor nodes should be actve n each cluster at a partcular tme, n order to save the energy. To accomplsh ths, t s needed to reduce the number of clusters. So when choosng a smlarty measure threshold value, t s taken nto consderaton, so that the smlarty measure threshold whch produces mnmum number of clusters could be selected. The clque coverng algorthm has been gven n Fg Cluster 4

4 No. of Clusters Internatonal Journal of Computer Applcatons ( ) Volume No.7, Aprl Clque Coverng Algorthm Input: Graph Gr Output: Clques Let {uv} be the set of uncovered vertces n the graph Gr, Whle {{ uv } } Choose vertex v {uv} such that nd( v) max{ nd( )} where nd s node degree Let adj(v) be the set of adjacent vertces of v Pck up adj(v) and put them n {sv} Construct a graph T = {sv} graph Sort {sv} n decreasng order of nd ( j) Construct a clque C {v} Whle {{ sv } } Choose next vertex k {sv} If adj ( k) C Add k nto C End f End whle Return C Take out the vertces of C from Gr End whle Fg 4: Clque Coverng Algorthm 5 Smlarty Threshold Smlarty Threshold Vs No. of Clusters Fg 5: Smlarty Threshold Vs number of Clusters Fg. 5 demonstrates that wth the ncrease of smlarty measure threshold, the number of clusters decreases. When number of clusters decreases, the energy savng can be maxmzed. 3.3 Generaton of Sleep-Duty Cycle After the clusters are constructed, now t s the tme for the snk to generate a sleep-duty cycle for the nodes that are low n data sendng rate. In order to generate the work schedule, Random Schedulng Method has been adapted. The tme needed to fnd the smlarty between two nodes s taken and splt nto equal sze tme slots. For each tme slot, the sensor nodes wthn the cluster get nto sleep-duty cycle based on ther data sendng rate and mnmum threshold rate. There are three cases to be consdered when generatng the sleep-duty cycle.. If data sendng rate s lower than mnmum rate for some n nodes n the cluster, then n nodes are put n sleep state.. If data sendng rate s greater than mnmum rate for all n nodes n the cluster, then n/ nodes are put n sleep state randomly. 3. If data sendng rate s lower than mnmum rate for all n nodes n the cluster, then round robn schedulng method s utlzed n order to make atleast one node to be awake at the partcular tme slot. Once the work schedule s generated, the snk broadcasts the sleep duty cycle nformaton to all sensor nodes wth cluster ID, node ID, and sleep wake up tme slots. The nodes receve ths nformaton and adaptvely enter nto the energy savng mode accordng to the schedule. Then cluster heads take responsblty to collect the data from ts members. After collectng the data, the cluster heads process the data and send to the snk. The snk receves the data from the cluster heads. Snce the workload s dstrbuted among cluster heads to reduce the overhead of snk, clusterng method s called as dstrbuted clusterng. If cluster head fnds any changes n the value generated and n data sendng rate, t nforms the snk whch n turn updates the schedule once agan. 3.4 Re-Clusterng for Moble Nodes So far, the algorthm has been formulated for the statc nodes. Ths s the phase, where the algorthm s beng updated for the moble nodes. The statc nodes need not be aware of route nformaton as they only communcate wth cluster heads and snk. And there s more possblty for the same node to be the cluster head for a long perod as nodes are statonary. So the cluster heads may lose ther energy so quckly and may de. In the case of moble nodes, the nodes keep on movng. There are many chances for the change n cluster heads f the algorthm takes care. So the energy utlzaton s dstrbuted. Though energy utlzaton s good n moble nodes, there s a necessty n managng the topology of the network. The locaton of the nodes changes tmely. So the structure of the network cannot be pre-determned. Due to the movement, the path may break at any tme. So each node has to have the route nformaton ether to communcate wth the snk or cluster head. In order to desgn the algorthm for moble nodes, the problems assocated wth moblty are to be analyzed. 5

5 Internatonal Journal of Computer Applcatons ( ) Volume No.7, Aprl. When the nodes are moble, the path from node to cluster head may get broken, so the node may not be able to reach the cluster head.. The node may move to new locaton passng the transmsson range of ts cluster head. 3. When the cluster heads are moble, the path from the cluster head to snk may be lost. 4. Because of the moblty n cluster heads, the packets sent by the nodes mght not reach. It wll lead to packet loss. On analyzng all these problems, a clusterng perod C p s ntroduced. Every clusterng perod, the snk broadcasts a request message to all the nodes. Once the nodes receve the message from snk, they all send the sensed data to the snk whch performs re-clusterng. Even though the nodes are moved on to the new locaton, every clusterng perod, they are supposed to send the sensed data to the snk not to the cluster heads. And cluster heads also transmt the data to the snk. At the result of re-clusterng, the new clusters are formed and new cluster heads are chosen for each cluster. But before the clusterng perod comes, ether the movement of nodes or cluster heads out of the transmsson range may cause packet loss. In order to avod the nodes from watng for the clusterng perod, the algorthm s updated as follows; Based on workng schedule of nodes, the cluster head sends request to the nodes n the allotted tme slot of nodes. Instead of sendng the data to the cluster heads, the nodes wat for the request from the cluster heads. After recevng the request, nodes send the data to the cluster heads. If the node does not receve the request from the cluster head n two tme slots contnuously, t decdes that t s non-member of the cluster head and t broadcasts the data. The cluster head that s n the transmsson range of the partcular node receves and comes to know the arrval of the new node. The cluster head mmedately ntmates the snk that re-constructs the cluster. MADCA Algorthm Input: Sensed data Output: Set of clusters For every clusterng perod C p Each sensor node transmts the data to the snk. The snk node receves the data. Snk node estmates the Smlarty Measure SM. (By (4)) If SM SM th where SM th s Smlarty Measure threshold End f Construct a graph Output cluster by clque-coverng Algorthm For each cluster Snk checks the data rate If End for R Rmn for some 'n' nodes then Where Else f Else End If R mn s mnmum rate and R s data rate Put 'n' nodes n sleep state R Rmn for all nodes 'n' then Put 'n / ' nodes n sleep state Put atleast one node n sleep state Snk broadcasts cluster nformaton and sleep duty cycle End for For each cluster Cluster head sends request to nodes n allotted tme slots If node receves the request and sends the data Where SM Cluster head collects data and checks SM and R If ( R R and SM SM ) mn t mn R s Dfference between two successve R values, s Dfference between two successve SM values, R s Mnmum threshold value of m n R, Mnmum threshold value of End f SM. Send detals to snk Snk updates the work schedule Else f nodes do not send the data to cluster head Cluster head nforms the snk Snk re-clusters the network SM s t mn Else f node does not receve the request from cluster head End for End f Node broadcasts the data Another cluster head n the rado range receves and nforms the snk Snk performs re-clusterng Fg 6: MADCA Algorthm 6

6 Internatonal Journal of Computer Applcatons ( ) Volume No.7, Aprl If the cluster head does not receve the data from the node n two tme slots contnuously, the cluster head decdes that the node has moved to an another locaton. In order to re-cluster the network, the cluster head nforms the snk about the change. Thus moblty of nodes s adapted. The MADCA algorthm s gven n Fg PERFORMANCE EVALUATION 4. Smulaton Setup The smulaton of MADCA s performed n NS smulator and the performance s evaluated. In the smulaton of MADCA, Mannasm has been used to generate the TCL scrpt easly based on the smulaton parameters. For smulatng MADCA, a random network deployed n an area 3 x 3m s consdered. The number of nodes s vared as, 4, 6, 8, and they are deployed randomly over the area. Only one snk s taken and t s placed n the centre of the area. The base staton s assumed to be placed 5m away from the area. The transmsson range of snk s 5m and the transmsson range of nodes s 75m. The ntal energy of the snk s 5J and ntal energy of nodes s 5J. IEEE 8. s used for wreless LANs as the MAC layer protocol. Parameter name Area sze Table. Smulaton Parameters Values 3 x 3 m No. of nodes, 4, 6, 8, Channel type Channel/Wreless Channel MAC 8. Lnk Lnk Layer Physcal layer Mca Antenna Rado propagaton Interface queue and Length Smulaton tme Topology Pause Tme Intal energy of nodes Omn Antenna Two Ray Ground Drop Tal and 5 3sec. Random.seconds 5J Transmt power.36w Recevng power.4w Sensng power.5w Processng power.4w Random way pont moblty model has been used to provde moblty to the nodes. It allows moble nodes to pause n a partcular locaton for some tme called pause tme. After ths tme expres, the nodes start to move to random destnaton based on the speed. The smulaton parameters used n the smulaton are tabulated n Table. 4. Performance Metrcs The performance of MADCA s compared wth ADCA [] whch s already proved to be more energy effcent than EEDC [5]. The performance s evaluated n terms of control packet overhead, average end-to-end delay, average delvery rato and energy consumpton. Control Packet Overhead: It s defned as the total number of packets normalzed by the total number of receved data packets. Average end-to-end Delay: Ths s the average of overall end-to-end delay from source to destnaton. Average Packet Delvery Rato: It s the rato of total number of packets receved and the number of packets sent. Energy Consumpton: It s the average energy consumed durng transmsson, recepton, etc. 4.3 Smulaton Results Based on the number of nodes MADCA s compared wth ADCA. The number of nodes vares from to. Fg. 7., Fg. 7., Fg. 7.3 and Fg. 7.4 show the smulaton results of both ADCA and MADCA. Both are compared aganst control overhead, average end-to-end delay, delvery rato and energy consumpton when number of nodes gets ncreased. Fg. 7. shows the control packet overhead generated when applyng ADCA and MADCA. The graph demonstrates that the generated overhead packet for MADCA s less than ADCA. When overhead gets reduced, the delay mght be mnmzed and energy could be saved. Fg. 7. gves the average end -to- end delay when ncreasng the number of nodes from to. From the graph t s clear that the delay of MADCA s less than ADCA. When delay gets reduced, the energy wastage can be avoded. Fg. 7.3 shows the delvery rato aganst the number of nodes. It demonstrates that, n MADCA, the number of packets receved successfully s more than the packets receved n ADCA. When comparng wth the packets sent, MADCA acheves good delvery rato. Fnally, Fg. 7.4 presents the energy spent n both the algorthms. Because of less overhead, reduced delay and good delvery rato, MADCA s supposed to be more energy effcent. Moreover, as the nodes are moble, energy consumpton by nodes would be less. Ths s what proved by smulaton result and shown n the Fg The smulaton results fnally say that the algorthm MADCA s more energy effcent than ADCA. ADCA has already been proven to be good n energy effcency when compared wth EEDC. So, energy utlzaton s far better n MADCA, when evaluatng aganst EEDC and ADCA. 7

7 Delay (Seconds) Energy Consumpton (Joules) Overhead(No. of Packets) Delvery Rato Internatonal Journal of Computer Applcatons ( ) Volume No.7, Aprl ADCA.6.4 ADCA 5 MADCA. MADCA No. of nodes No. of nodes Fg 7.: No. of nodes Vs Overhead (No. of Packets) Fg 7.3: No. of nodes Vs Delvery rato ADCA.5.5 ADCA.5 MADCA.5 MADCA No. of nodes No. of nodes Fg 7.: No. of nodes Vs Delay (Seconds) 5. CONCLUSION In ths paper, Moble Adaptve Dstrbuted Clusterng Algorthm has been proposed to mnmze the energy consumpton n wreless sensor networks. Ths algorthm s dstrbuted and t reduces the overhead of the snk by clusterng the sensor nodes n the network. In order to cluster the network, ths algorthm enables the snk to dentfy the smlarty of data among nodes. The nodes that are smlar n data are clustered together usng clque coverng algorthm. In each cluster, the cluster head s chosen. The sleep duty cycle s generated on consderng the mnmal energy consumpton. The changes n the locaton of nodes are nformed to the snk. Besdes clusterng the network for every clusterng perod, the snk re-bulds the clusters when t receves notfcaton regardng changes n the network. Thus ths algorthm helps to lengthen the lfetme of the network. The smulaton results have demonstrated that MADCA s better n terms of control overhead, average end-to-end delay, delvery rato and energy consumpton. Fg 7.4: No. of nodes Vs Energy consumpton (Joules) MADCA can also be enhanced wth more than one snk and the optmal locaton for placng a snk can also be found, so that the energy can stll be reduced further. 6. ACKNOWLEDGMENTS The authors would lke to thank all who have supported n brngng out the paper wth qualty. 7. REFERENCES [] S.V.Mansekaran, R.Venkatesan, "An Adaptve Dstrbuted Power Effcent Clusterng Algorthm for Wreless Sensor Networks", Amercan Journal of Scentfc Research, no., pp. 5-63,. [] Sabtha Ramakrshnan, T.Thyagarajan, "Energy Effcent Medum Access Control for Wreless Sensor Networks", Internatonal Journal of Computer Scence and Network Securty, vol.9, no.6, 9. 8

8 Internatonal Journal of Computer Applcatons ( ) Volume No.7, Aprl [3] Mehd Saedmanesh, Mojtaba Hajmohammad, and AlMovaghar, "Energy and Dstance Based Clusterng: An Energy Effcent Clusterng Method for Wreless Sensor Networks", World Academy of Scence, Engneerng and Technology, vol. 55, p.p , 9. [4] Udt, S. and Pabtra, M. 7. Dstrbutve Energy Effcent Adaptve Clusterng Protocol for Wreless Sensor Networks. In Proceedngs of the 7 Internatonal Conference on Moble Data Management, p.p [5] Chong Lu, Ku Wu and Jan Pe, "An Energy-Effcent Data Collecton Framework", IEEE transactons on parallel and dstrbuted systems, vol. 8, no. 7, p.p -3, 7. [6] Nauman Israr, Irfan Awan, "Multhop clusterng algorthm for load balancng n wreless sensor networks", Internatonal Journal of Smulaton, Systems, Scence and Technology, 7. [7] Stanslava, S. and Wend, B. H. 5. Prolongng the Lfetme of Wreless Sensor Networks va Unequal Clusterng. In Proceedngs of the 9th IEEE Internatonal Parallel and Dstrbuted Processng Symposum, p.p 8-6. [8] Ossama Youns, Sona Fahmy, "HEED: A Hybrd Energy - Effcent, Dstrbuted Clusterng Approach for Ad-hoc Sensor Networks", IEEE Transactons on Moble Computng, vol.3, no. 4, p.p , 4. [9] Pavlos, P.. Lterature Survey on Wreless Sensor Networks, In Proceedngs of the Frst ACM Internatonal Workshop on Wreless Sensor Networks and Applcatons. [] Wend, R.H., Anantha, C. and Har, B.. Energy- Effcent Communcaton Protocol for Wreless Mcrosensor Networks. In Proceedngs of the 33rd Hawa Internatonal Conference on System Scences. AUTHORS PROFILE S.V.Mansekaran was born n Tamlnadu, Inda n 98. He receved hs B.E. degree n Informaton Technology from Bharathyar Unversty, Combatore n 3. He completed hs M.E. n Computer Scence and Engneerng from Anna Unversty Chenna n 5. He s currently workng as an Assstant Professor n the Department of Informaton Technology at Anna Unversty of Technology, Combatore, Inda. Hs research areas nclude moble computng, qualty management and software engneerng. R.Venkatesan was born n Tamlnadu, Inda, n 958. He receved hs B.E (Hons) degree from Madras Unversty n 98. He completed hs Masters degree n Industral Engneerng from Madras Unversty n 98. He obtaned hs second Masters degree MS n Computer and Informaton Scence from Unversty of Mchgan, USA n 999. He was awarded wth PhD from Anna Unversty, Chenna n 7. He s currently Professor and Head n the Department of Computer Scence and Engneerng at PSG College of Technology, Combatore, Inda. Hs research nterests are n Smulaton and Modelng, Software Engneerng, Algorthm Desgn, Software Process Management. G. Devana was born n Tamlnadu, Inda, n 983. She receved her B.Tech degree n Informaton Technology from Anna Unversty, Chenna n 5. She s currently pursung her M.Tech n Informaton Technology at Anna Unversty of Technology, Combatore. Her areas of nterests nclude wreless network, wreless sensor network and mddleware technologes. 9