An approach to data Aggregation in wireless sensor network using Voronoi fuzzy clustering algorithm

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1 Journal of Scentfc & Industral Research ol. 72, May 2013, pp An approach to data Aggregaton n wreless sensor network usng orono fuzzy clusterng algorthm S.Nthyakalyan 1* and S.Suresh Kumar 2 *1 Department of Informaton Technology, K.S.R College of Engneerng, Truchengode, Inda 2 vekanandha College of Technology for Women, Truchengode, Inda Receved 23 July 2012; revsed 08 December 2012; accepted 14 March 2013 Data collected through any sensor are needed to be processed for ganng some useful nformaton. Wreless Sensor Network (WSN) s a subclass of sensor node whch s evolvng as an astonshng technque n wreless communcaton technology for montorng large applcaton domans such as weather forecastng, mltary survellance, medcal dagnoss, fre detecton alarmng systems, etc. Each sensor wll not be able to process tself due to the prmary ssue of energy (battery power) curb n WSN. Stll many nvestgators desre to fnd a soluton to mprove the lfespan of WSN. The best way s to select an optmum head node for data aggregaton to reduce the energy of data transmsson for the reason that energy requred for computng s more than for data transmsson. Ths prototype shfts the attenton from the outmoded addresscentrc approaches to data-centrc approach. Data centrc technques lke data aggregaton va energy effcent fuzzy clusterng algorthm based on orono dagram s proposed n ths paper. The proposed novel algorthm s a combnaton of orono and modfed Fuzzy C-Means clusterng algorthm called as orono Fuzzy (F) algorthm.cluster head (CH) for F clusterng algorthm s nomnated by consderng node's resdual energy, dstance between CH and ts neghbor s sensor node and Qualty of servce. Furthermore, data aggregaton s employed n each cluster s CH to reduce the amount of data transmsson whch effectvely etends the network lfetme. Smulaton result reveals that ths method acheves agreeable performance n etendng the network lfetme compared to the estng ones. Keywords: Clusterng algorthm, Fuzzy C-Means, orono dagram, Data Aggregaton, Resdual energy, QOS. Introducton A Wreless Sensor Network (WSN) contans tny operatng system, sensng devces, memory and whch normally runs n battery power. WSN s a pool of sensor node that captures the event or data occurred n montorng envronment. To obtan the knowledge or to make some decson about the sensed event, data need to be processed by sensor tself or at other end (base staton). WSNs are self-motvated, dstrbuted and adaptve systems that are found n etensve applcatons n our daly lves, ncludng, medcal applcatons, home applances, data gatherng and montorng, mltary, space etc. arous crtcal problems n WSN are to be addressed by researchers n order to guarantee a sensble degree of cost and qualty of the network. Some of the major ssues are node clusterng, master selecton, energy dsspaton and feld coverage. 1-4 The researcher communty s aggressvely observng and rulng out several optmal *Author for correspondence: E-mal: nthyakalyan.me@gmal.com solutons to the above ssues through dstrbuted data mnng technques va combnng clusterng, coverage problem and data aggregaton (gatherng). Clusterng n data mnng technques s a key method that naturally reduces energy costs of WSN s wthout compromsng the qualty of data delvery. The process of splttng the sensor nodes nto crowds are called as clusterng. Ths approach allows cluster head to collect data from the dstnct sensors and forward t to snk. In clusterng, cluster heads (CH) are needed to be elected frst then cluster members are selected for each CH, based on dstance metrc.ch heads aggregate the packets from ther cluster members before forwardng them to snk. By rotatng the cluster head role unformly among all nodes, each node tends to epend the same energy over the tme. The clusterng may allow sngle hop or mult hop for communcaton between CH and base staton or snk. Data aggregaton s a surplus and dfferent noteworthy functon n WSN to conserve the energy. The key dea of ths process s to elmnate redundancy n data, mnmzng the number of

2 288 J SCI IND RES OL. 72 MAY 2013 transmssons 6, ntegrates all the ncomng data n cluster head from dverse sources and compresson s also made before enroutng data to the snk. Ths focuses the data-centrc approach. Aggregaton algorthms are lmted to applcaton requrement that s ether n tme or energy conservaton. In the proposed work the energy consumpton s measured at all stages - n the CH electon, data aggregaton, data routng and mantenance. QoS checks are further consdered n data aggregaton so that data delvery can meet QoS that are mposed by the specfc WSN applcaton. In ths proposed algorthm throughput, delay tme and delvery rato are measured as QoS parameters. orono dagram technque s a famous computatonal geometry structure, often apt for WSN coverage problem and to the fnd optmal cluster 7. It dvdes the gven regon nto a number of small orono cell n such way that there ests only one sensor n each cell. Hence orono dagram can act as fundamental samplng method for WSN coverage, wth sensors actng as stes. In the entre orono polygons, f vertces are covered then the regon s sad to be completely covered; otherwse coverage hovels est. Let S { p1, p2, p3,..., p n } be a set of ponts or stes n a gven area. orono regon ( p ) s computed as ( p) { : p pj, j }. A set of all stes that forms the orono dagram ( S) { ( p1), ( p2), ( p3),..., ( p n )}, s eplaned n Fg. 1. It splts the gven regon nto N orono cells wth eactly one sensor n each cell, such that all the ponts n a sngle cell are closer to the sensor n t rather than any other sensor cell n the gven regon. Two sensors s 1 and s 2 are consdered n the gven plane. orono dagram s constructed by takng Fg. 1 orono dagram of WSN perpendcular bsector of the lne segment jonng the two sensors.to buld a orono dagram for three stes wth a thrd sensor, frst a trangle wth vertces (s 1, s 2, and ) at the three stes s consdered. Then the perpendcular bsectors of the three sdes of a trangle are drawn to meet at a sngle pont. In ths paper, the Fuzzy C-Means clusterng algorthm s modfed by ncorporatng orono dagram, called as orono Fuzzy (F) algorthm. It s used to determne optmal cluster head for data aggregaton, based on dstance, resdual energy and QOS. Centrod s used for CH selecton. If the centrod value s repeated n sngle sensor coverage area (e. Sngle F cell), then that sensor wll act as a cluster head for that round. Each CH wll then perform data aggregaton. The cluster head drects the data every tme to the snk node for further advanced processng. Fuzzy set theory s regularly used to handle uncertanty 5. Among several fuzzy clusterng algorthms, fuzzy c-means (FCM) s the mostly used one. Snce t has certan drawbacks, proposed algorthms s modfed wth ncorporatng orono dagram to mprove the performance. The Proposed F Clusterng Algorthm The am of the proposed approach s to reduce the consumpton of energy n every sensor node and thereby ncreasng the entre lfe tme of WSN. Sensors are placed to detect the event occurrng n the mleu. Sensed data are needed to be transmtted regularly to the base staton or snk for acqurng knowledge. However a lot of energy s spent n transmttng captured data chroncally. Henceforth to overcome from the above cause, data aggregaton and modfed orono fuzzy clusterng algorthm s proposed n ths work. F clusterng algorthm s carred out by snk node. Intally, orono method s appled to partton sensor network nto orono cells. In ths proposed approach any sensor s poston s projected by and y co-ordnates and ts resdual energy usng z. That s for any sensor node p s, y, z and dstance functon for computng orono dagram s gven by, s r s r s r d p r y y z z where r s, the set of all neghborng nodes. Successvely clusters of sensor node s formed usng Fuzzy C-Means clusterng algorthm, for whch the frst requrement s to select the CH and then ts members. For fndng the membershp functon, a number of orono cells as a CH are selected and calculated. The membershp functon for every orono cell s wth the assumed

3 NITHYAKALYANI & KUMAR: AN APPROACH TO DATA AGGREGATION CLUSTERING ALGORITHM 289 cluster head. The orono cell goes to that cluster whch has the hghest value of membershp functon. Wth the help of the membershp functon, the cluster head among the clusters are calculated. In each CH, data s processed by aggregaton or compresson functon and forwards the gathered data to the snk node. Pseudo code Input: Dataset contanng all sensor node S { p1, p2, p3,..., p n }, ts poston value and resdual energy as p (, yj, zk) 1. Construct orono dagram of ( S) { ( p1), ( p2), ( p3),..., ( p n )} usng ( p ) r : d( p, r) d( p j, r), j 2. Arbtrarly select c sensor nodes as ntal cluster heads ch { ( p1), ( p2), ( p3),..., ( p c )} from (S) 3. Compute membershp functons usng the equaton (1) a. Determne dstance membershp functon usng the equaton (2) b. Determne QOS membershp functon usng the equaton (3) c. Calculate mamum membershp functon y for each ( p ) usng ( p ) ma( y ) 4. Compute and update ch y the fuzzy cluster centers usng the equaton (4) 5. Go to step 3 and 4 untl chy s placed n same p 6. Each ( p ) send ther data ( ( p )( d )) to correspondng cluster head ch 7. Cluster head ch receves data from ts cluster members D d 1, d 2,..., d 8. Implement data aggregaton n each ch Select hope 1. Avg 2. Ma 3. Mn a. If 1 then performs equaton (5), else f 2 then performs equaton (6), else perform equaton (7). 9. Result of step 8 goes to the snk node. 10. Stop Calculaton of membershp functon Based on the membershp functons the nodes are clustered. To fnd the membershp functon of each sensor node, certan numbers of sensor nodes are assumed as cluster heads. In order to fnd the membershp functon, frst c orono cells are chosen as a CH that s c 2 and any two cells as cluster head y1 ch 1 and y2 ch 2 are assumed. Wth the help of assumed cluster head, the membershp functon of each sensor pont s calculated. In ths paper, the membershp functon s obtaned based on two functons. The frst one s based on the dstance and the second one s based on the QOS values. The followng equaton (1) s used for fndng the membershp functon. 1 y Dy Qy, 1, 2,.. n and y 1, 2,.. c (1) Where y Membershp functon of th sensor node wth respect to the y th cluster head node Dy Dstance between th sensor node to the y th cluster head node Qy alue of the QOS of th sensor node wth respect to the y th cluster head node Weghtage of dstance (user defned), and Weghtage of QOS (user defned) Dstance based membershp functon Based on the dstance between the cluster head and sensor node, the calculatons are made. The Eucldan dstance method s employed to fnd the dstance between the two ponts. Ths calculaton descrbes how much space s n-between each sensor node and each cluster head node. The sensor node moves to the nearest cluster head node that s wth mnmum Eucldan dstance. The followng equaton (2) s used to calculate the dstance based membershp functon. D y c k 1 ch ch k (2) Runnng eample of dstance based membershp functon Table 1, shows the postons of each sensor node. Here the number of nodes s assumed to be 5. In that any one of the nodes s selected as a cluster head and then dstance membershp functon s computed. Snce CH s assumed to be 2 n ths eample, C1=

4 290 J SCI IND RES OL. 72 MAY 2013 (9.5, 1.3), C2= (8.8, 2.5) are selected as cluster heads from table 1.Then the selected values are substtuted n the equaton (2) to get the dstance based membershp functon and t s shown n Table 2. QOS based membershp functon Table 2, contans the values of the QoS of each node wth respect to each cluster head. Each of the sensor nodes has dfferent QoS parameter wth respect to an applcaton where the sensors are functonal. In the proposed work weather forecastng s taken as an applcaton and ts QoS functons are consdered as throughput, delvery rato, and delay tme. Based on those QoS values the membershp functon for CH are obtaned. Each QoS parameter can be operated as eplaned below. Each of the nodes has the tme slots at the begnnng epoch. Once the data s collected, data packets are sent to the cluster head wthn the net tme slot. If the packets are not delvered wthn the partcular tme perod then t s marked as the y 1 delayed packet. The delay tme z s calculated as the rato of the receved tme to the sendng tme of the node; the result of ths s subtracted by 1 that s a delay tme. Each of the nodes has a number of packets to send to the cluster head. Whle sendng the data packet to the cluster head some of the packets are not receved by the cluster head because of the nterrupton, crash or nose. The rato of the number of receved data packet n the cluster head to the number of sent data packets from the node s measured as delvery rato y 2 ( z ) and t vares from node to node wth respect to the cluster heads. Table 1 Locatons of the sensor nodes y Each of the nodes generates the data packets and sends t to the cluster head. Flow of the data packet vares from node to node wth respect to the cluster head. Number of packets sent to the cluster head n a partcular tme nterval s vared for each node wth respect to the cluster head. Here throughput y 3 ( z ) of the node s measured by number of receved packets to the tme perod.the membershp value based on the QoS values of the node wth respect to the cluster heads are determned by equaton (3). Q y 3 y q z q1 c 3 k q z k1 q1 (3) Runnng eample of QoS membershp functon Table 2 conssts of QoS values of the runnng eample for the above table 1. Consder the frst node wth two clusters C1 (9.5, 1.3) and C2 (8.8, 2.5). The QoS membershp functon for the other nodes wth respect to cluster heads C1 and C2 are calculated usng equaton (3) and ts values are shown n column 2&3 of table 2.Wth the help of Dy and Qy values now calculate the membershp functon usng equaton (1). The runnng eample of the membershp functon s shown below. Runnng eample of membershp functon The values of Dy and Q y are taken from the above sectons. Then the values are substtuted n to the equaton (1), to get the membershp values for the nodes wth the cluster heads C1 and C2. Here, the same weghtage 0.5 s assumed for the dstance and the QoS membershp functon. The weghtage value may depend upon the user. Usually ts range vares between (0-1). If the user wants to group the sensor nodes manly based on dstance then the user gves the value of greater than.if the user wants to Table 2 Dstance and QoS values of each node wth all possble cluster heads 1(2.5,2) 2(4.1,1.3) 3(5.4,0.8) 4(6.8,3.0) 5(8.8,2.5) D y Q y D y Q y D y Q y D y Q y D y Q y 1(2.5,2) 0,0, ,0.4, ,0.6, ,0.8, ,0.4, (4.1,1.3) 0.2,0.5, ,0, ,0.8, ,0.5, ,0.4, (5.4,0.8) 0.4,0.8, ,0.5, ,0, ,0.5, ,0.4, (6.8,3.0) 0.4,0.2, ,0.5, ,0.5, ,0, ,0.2, (8.8,2.5) 0.4,0.8, ,0.4, ,0.4, ,0.2, ,0,1 0

5 NITHYAKALYANI & KUMAR: AN APPROACH TO DATA AGGREGATION CLUSTERING ALGORITHM 291 group the sensor node based on the QoS then the value of s greater than. The membershp functon for all nodes wth respect to cluster head C1 and C2 s calculated and ther values are shown column 4 and column 5 of table 3.Two membershp values: 11 y 1, y2, are consdered where s the sensor node, y 1 and y 2 are the cluster heads. If y1 y2, then the sensor node moves towards cluster head y2. If y1 y2, then the sensor node moves towards cluster head y1. After fndng the membershp value for all orono cells, each and every cell s clustered based on the two cluster heads (assumed). The orono cell selects that cluster head whch conssts of hgher value than the other. The fg 2 shows that red cells are assumed to be cluster heads C1, C2 n a wreless sensor network. Membershp functons of all the other sensor nodes are gathered by ether cluster heads C1 or C2. The equaton (4) s used to fnd the adapted cluster heads wth the help of calculated membershp functon. The m s called as fuzzer used for controllng the fuzzness of the algorthm and the value of m s greater than 1. Fndng the cluster head ch y n 1 n 1 y y m. m (4) By usng equaton (4), cluster centrod ponts (3.15, 5.48) and (8.02, 4.9) are got. Both the ponts are plotted n Fg 2 and the correspondng orono cell s treated as cluster head. After the selecton of the cluster head and ts members, members transmt sensed data to ther CH. CH now needs to transmts the gathered data to the snk.ths procedure leads to transferng sensed data a number of tmes so energy of the cluster head s draned much more. To solve the above problem, data aggregaton or compresson s proposed. Data aggregaton In the wreless sensor network, each sensor node transmts ts sensed data to the cluster head.the cluster head calculates the average or mamum or mnmum of the receved sensory data whch are sent to the snk node for a fed ntervel of tme as shown n fg 2. The proposed method, controls the traffc of the wreless sensor network and also saves the energy of the cluster head. Ths helps to ncrease the lfespan of the WSN. Let as consder weather forecastng applcaton, n whch sensors are used for montorng the gven envroment. Here every sensor transmts the captured sensory data ( ( p )( d )) to ts correspondng CH. A set of data s receved by the each cluster head D { d1, d 2,..., d where 1 n, and n s the total number of data receved n the cluster head. Each ( ( p )( d )) comprses value of temperature, d, date and tme d, tmp, tme, date. The data aggregaton technque s computed usng equaton (5),(6) and (7) respectvely. d n1 avg (5) n ma ma D (6) mn mn D (7) Smulaton Results and Dscusson Energy calculaton The sensors are arbtrarly deployed n the meters and snk node s located out the sensng Table 3 QOS based membershp functon of the cluster head C1 C Fg. 2 Cluster usng FC n WSN

6 292 J SCI IND RES OL. 72 MAY 2013 feld. The number of tmes the nodes need transmt ther messages to ther CH and cluster heads transmt the messages to the base staton, s employed as a measure to compare the network lfetme wth dfferent clusterng methods. In wreless channel, f collson occurs then the retransmsson of message s not possble.the rado model and to fndng the amount of energy requred for data transmsson, energy requred to receve k-bts of data and energy requred for data aggregaton respectvely are smlar to Henzelman 1. Epermental desgn In the proposed approach, cluster head selecton for data aggregaton n wreless sensor network has been mplemented usng MATLAB. The comparatve analyss of the cluster head selecton approach wth F clusterng and K-Means s presented. The performance of the proposed system s analyzed usng the evaluaton metrcs ncludng runnng tme and sze of receved data n the snk node based on the number of cluster head n the wreless sensor network. Evaluaton of runnng tme by varyng the number of the sensor nodes The F Clusterng algorthm s compared wth the K-Means algorthm n order to evaluate the performance of the proposed F algorthm. Fg 3a shows the runnng tme of the F algorthm and K- Means algorthm. The number of sensor nodes s changed at each eecuton and the tme of both the algorthms are evaluated, the value of the cluster head beng constant for both the algorthms to fnd the eecuton tme. The number of cluster heads s taken as 10 for all eecuton. Fg 3a shows that, the F algorthm needs less tme to eecute when compared wth the K-Means algorthm. The reason behnd s that f the membershp functon value of the sensor node s repeated n a sngle orono cell then the eecuton of the membershp functon for that sensor node stops and hence the runnng tme of the F algorthm gets reduced. Evaluaton of runnng tme by varyng the number of cluster heads Here, the runnng tme s evaluated by keepng the number of sensor nodes constant and varyng the number of cluster heads each tme. Fg 3b shows the runnng tme of F algorthm and K-Means algorthm.from the graph t s seen that proposed approach va the F algorthm takes less tme to eecute compared to the K-Means algorthm. The runnng tme of both the algorthms s drectly proportonal to the number of cluster heads. The calculaton of the membershp functon for each pont wth respect to each cluster head takes more tme.hence the runnng tme ncreases as the number of cluster head ncreases. Number of receved data n snk node by varyng the number of the sensor nodes After the selecton of cluster head, each of the sensor nodes sends ts sensed data to the cluster head. The cluster head has receved many data from the sensor nodes, as per the user nstructon.the cluster head operates on the receved data and sends that data to the snk node. The snk node receves the data from the cluster heads. Fg 4a shows the number of receved data n a snk node for dfferent number of sensor nodes. Based on the poston of the cluster head, the receved data of the snk node gets vared snce the poston of the cluster head s dfferent for both F algorthm and K-Means algorthm. Hence, the number of receved data n a snk node as a Fg. 3(a) Runnng tme S number of nodes Fg. 3(b) Runnng tme S Number of clusterng head

7 NITHYAKALYANI & KUMAR: AN APPROACH TO DATA AGGREGATION CLUSTERING ALGORITHM 293 Fg. 4(a) Number of data receved data n snk node s Number of nodes. parameter s taken. The graph n fg 4a shows that, the snk node receves more number of data by usng F algorthm than by usng the K-Means algorthm. Number of receved data n snk node by varyng the number of the cluster heads The number of receved data n a snk node vares dependng on the number of cluster heads n a sensor network. Fg 4b shows the number of receved node n a snk node based on dfferent number cluster heads for both F algorthm and K-Means algorthm. The snk node receves more number of data by usng F algorthm than by usng the K-Means algorthm. Conclusons In ths paper, the approach for energy effcent cluster algorthm for data summarzaton n wreless sensor network has been presented. In order to reduce the energy of the wreless sensor network, we have presented the effcent orono fuzzy clusterng algorthm. At frst the orono cells are appled for each node n the wreless sensor network based on the energy of the sensor node. Consequently, the cluster head s selected by the fuzzy clusterng algorthm. Every node n the wreless sensor network sends ts sensed data to the cluster head. The data aggregaton and data compresson process s done n the cluster head. The cluster head aggregates the receved data by Fg. 4(b) Number of data receved data n snk node s No of cluster head takng the mnmum, mamum and average values. The aggregated value s sent to the snk node by the cluster head. Fnally, the epermental results shows that the effcency of F clusterng algorthm (60%) s hgher when compared wth the K means algorthm. References 1 Kulk J, Henzelman W, & Balakrshnan H, Negotatonbased protocols for dssemnatng nformaton n wreless sensor networks, J Wreless Networks, 8 (2002) Xanghu Wang & Guoyn Zhang, DECP: A Dstrbuted Electon Clusterng Protocol for Heterogeneous Wreless Sensor Networks,Int conf on Computatonal Scence, ICCS 07, 4489 (2007) Km J M, Park S H, Han Y J & Chung T M, CHEF: Cluster head electon mechansm usng fuzzy logc n Wreless Sensor Networks, n Proc.Int Conf. Adv. Commun Technol.,2 (2008) Mohammad Zeynal, Lel Mohammad Khanl & Amr Mollanejad TBRP: Novel Tree Based Routng Protocol n Wreless Sensor Network, Int J Grd and Dstrbuted Computng, 2 (4) (2009) Wang H & Fe B, A modfed fuzzy c-means classfcaton method usng a multscale dffuson flterng scheme, J Med Image Anal, 13(2) (2009) Zh J, Papavasslou S & Yang J, Adaptve localzed qosconstraned data aggregaton and processng n dstrbuted sensor networks, IEEE Trans. Parallel Dstrb. Syst,19 (9) (2006) Yun Wang & Dharma P. Agrawal, Optmzng Sensor Networks for Autonomous Unmanned Ground ehcles, n Proc. of Soc. Photo. Instru. Eng., SPIE 7112 (2008)