Int. J. Communcatons, Network and System Scences, 2011, 4, 249-255 do:10.4236/jcns.2011.44030 Publshed Onlne Aprl 2011 (http://www.scrp.org/journal/jcns) Energy Effcent Computaton of Data Fuson n Wreless Sensor Networks Usng Cuckoo Based Partcle Approach (CBPA) Abstract Manan Dhvya 1, Murugesan Sundarambal 2, Loganathan Nthssh Anand 3 1 Department of EEE, Anna Unversty of Technology Combatore, Combatore, Inda 2 Department of EEE, Combatore Insttute of Technology, Combatore, Inda 3 Department of Mechancal Engneerng, Anna Unversty of Technology Combatore, Combatore, Inda E-mal: {sadhvya1, nthsshanand}@gmal.com Receved February 27, 2011; revsed March 26, 2011; accepted March 30, 2011 Energy effcent communcaton s a plenary ssue n Wreless Sensor Networks (WSNs). Contemporary energy effcent optmzaton schemes are focused on reducng power consumpton n varous aspects of hardware desgn, data processng, network protocols and operatng system. In ths paper, optmzaton of network s formulated by Cuckoo Based Partcle Approach (CBPA). Nodes are deployed randomly and organzed as statc clusters by Cuckoo Search (CS). After the cluster heads are selected, the nformaton s collected, aggregated and forwarded to the base staton usng generalzed partcle approach algorthm. The Generalzed Partcle Model Algorthm (GPMA) transforms the network energy consumpton problem nto dynamcs and knematcs of numerous partcles n a force-feld. The proposed approach can sgnfcantly lengthen the network lfetme when compared to tradtonal methods. Keywords: Cuckoo Search, Generalzed Partcle Model, Energy Effcency, Clusterng, Sensor Networks 1. Introducton Wreless sensor networks (WSNs) facltate nnovatve applcatons and necesstate non-conventonal paradgms for protocol desgn. Sensor node s a tny devce that ncludes three basc components: a sensng subsystem for data acquston from the physcal surroundng envronment, a processng subsystem for local data processng and storage, and a wreless communcaton subsystem for data transmsson [1]. Ther structure and characterstcs depend on ther electronc, mechancal and communcaton lmtatons but also on applcaton-specfc requrements [2]. Based on the archtecture and power breakdown, several approaches are consdered smultaneously to reduce power consumpton n wreless sensor networks. The man enablng technques at a very general level are Clusterng Schemes, Routng Protocols, Duty Cyclng Schemes, Data Drven approaches and Moblty [3]. Clusterng, as potentally the most energy effcent organzaton, has wtnessed wde applcaton n the past few years [4] and numerous clusterng algorthms have been proposed for energy savng. Hence effcent data clusterng technques must be used to reduce the data redundancy and n turn reduce overhead on communcaton [5]. Low Energy Adaptve Clusterng Herarchy (LEACH) and Hybrd Energy Effcent Dstrbuted clusterng (HEED) are the tradtonal approaches to effectve data-gatherng protocols n WSNs. Energy consumpton n mult-cluster sensor networks s also explaned [6]. A protocol proposed n [7] optmzes the network performance under the metrc of nformaton rate per Joule whle ensurng a gven qualty of servce (QoS). QoS effectvely exemplfy the tme and energy requred to collect the packets.the tradtonal approach of repeated clusterng makes communcaton and processng overheads. Numerous strateges have been projected so far, ncorporatng optmzaton technques, but the need for energy conscousness reman as a tough ssue. Ths paper proposes a new optmzaton based data collecton scheme ncorporatng Cuckoo Search (CS) and Generalzed Partcle Model Algorthm (GPMA). The formulated technque of combnng the above algorthms s stated as Cuckoo Based Partcle Approach (CBPA).
250 M. DHIVYA ET AL. The aspect of formulatng ths technque s ncorporaton of constrants that helps n energy effcent data collecton or fuson, by elmnatng data redundancy and mnmzaton of energy consumpton. Cuckoo Search s performed to form sub-optmal data fuson chans. The collected nformaton s transmtted to the base staton va cluster heads through GPM algorthm. In the GPM algorthm energy constrants are ncorporated and the nformaton s routed n shortest path. The proposed technque shows better performance n optmum number of clusters, total energy consumpton and prolongng of network lfetme. The rest of the paper s organzed as follows. The problem formulaton and Energy Model s explaned n Secton 2. Bascs of Cuckoo Search and Generalzed Partcle Approach Algorthm are explaned n Secton 3. Cuckoo Based Partcle Approach (CBPA), chan formaton, proposed methodology s explaned n Secton 4. Results and analyss are descrbed n Secton 5. Conclusons are drawn n Secton 6. 2. Problem Formulaton and Energy Model The am of ths paper s mnmzaton and conservaton of energy of WSNs. To mnmze the usage of energy sensor nodes are formed as statc clusters n frst phase, by Cuckoo Search. The rado model adopted s, stated by Henzelman et al [8]. The followng are the most wdely used assumptons and model n sensor network smulaton and analyss [9]. Nodes are dspersed randomly followng a unform dstrbuton n a 2-dmensonal space and the locaton of the Base Staton (BS) s known to all sensors. The nodes are capable of transmttng at varable power levels dependng on the dstance to the recever. The nodes are unaware of ther locaton. The nodes can estmate the approxmate dstance by the receved sgnal strength f the transmt power level s known, and the communcaton between nodes s not subject to mult-path fadng. A network operaton model smlar to that of LEACH and HEED [10] s adopted here, whch conssts of rounds. Each round conssts of a clusterng phase followed by a data collecton phase. E Tx 2 le. electrcal fs d for 0 dd 4 le. electrcal mp d for dd crossover crossover The amount of energy consumed for transmsson E Tx, of l-bt message over a dstance d s gven by Equaton (1). ERx electrcal (1) l. E (2) where E electrcal s the amount of energy consumed n electronc crcuts, ε fs s the energy consumed n an amplfer when transmttng at a dstance shorter than d crossover, and ε mp s the energy consumed n an amplfer when transmttng at a dstance greater than d crossover. The energy expended n recevng a l-bt message s gven by the above Equaton (2). 3. Background Work for Data Fuson Cuckoo search (CS) s an optmzaton algorthm developed by Xn-She Yang and Suash Deb n 2009. It s a novel algorthm whch s nspred by the oblgate brood parastsm of some cuckoo speces by layng ther eggs n the nests of other host brds of other speces. It s more effcent than Genetc Algorthm and Partcle Swarm Optmzaton to adapt to wder class of optmzaton problems [11]. In Fgure 1 the Pseudo code for Levy Flghts s gven. The qualty or ftness of a soluton s proportonal to the value of the objectve functon for the optmzaton problem. The CS s based on three dealzed rules: 1) Each cuckoo lays one egg at a tme, and dumps ts egg n a randomly chosen nest 2) The best nests wth hgh qualty of eggs wll carry over to the next generaton 3) The number of avalable host s nests s fxed, and the egg lad by a cuckoo s dscovered by the host brd wth a probablty P α. The worst nests are dscovered and dumped from further calculatons. X s related to the new soluton, for an th cuckoo and then levy s performed as gven n Equatons (3) and (4). t1 t X X * Levyn (3) t1 t * t X X E (4) 3.1. Generalzed Partcle Model Algorthm Generalzed Partcle Model gven by Danxun Shua [12], fnds ts applcaton n many felds of network orented applcatons. It s smlar to Swarm Intellgence (SI) technque. Ths algorthm eradcates the unknown emprcal performance and much computaton tme. For a mult-objectve optmzaton problem, the algorthm controls the parameters n parallel or dspersedly. The GP model conssts of numerous partcles and forces, wth each partcle havng ts own dynamc equatons to represent the network enttes and force havng ts own tme-varyng propertes to represent varous socal nteractons among the network enttes. Each partcle n GP has four man characterstcs [13]: 1) Each partcle n GP has an autonomous self- drvng force, to embody ts own autonomy and the personalty of network entty.
M. DHIVYA ET AL. 251 begn Objectve functon f(x), x = (x1,..., xd) T Generate ntal populaton of n host nests x ( = 1, 2,..., n) whle (t < MaxGeneraton) or (stop crteron) Get a cuckoo randomly by Levy flghts evaluate ts qualty/ftness F Choose a nest among n (say, j) randomly f (F > Fj), replace j by the new soluton; end Fractons (pa) of worse nests are abandoned and new ones are bult; Keep the best solutons (or nests wth qualty solutons); Rank the solutons and fnd the current best end whle Post process results and vsualzaton End Fgure 1. Pseudo code for cuckoo search va levy flghts. 2) The dynamc state of every partcle n GP s a pecewse lnear functon of ts stmulus, to guarantee a stable equlbrum state. 3) The stmulus of a partcle n GP s related to ts own objectve, utlty and ntenton, and to realze the multple objectve optmzatons. 4) There s varety of nteractve forces among partcles, ncludng unlateral forces, to embody varous socal nteractons n networks. The GPM s sutable for MAS s problem-solvng n these more complex envronment: mult-autonomy agents, mult-type coordnaton, mult-objectve optmzaton, hgher-degree parallelsm, and random and emergent events [14].The assgnment matrx of task allocaton and resource assgnment n MAS,, S t S t where, S t a t, p t,ζ t. j j j j k (5) nm 3.1.1. GP Algorthm In Fgure 2, the algorthm for Generalzed Partcle Model s gven. The fgure llustrates the necessary steps n devsng the model. A partcle may be drven by numerous forces that are produced by the force-feld, other partcles and by own. The approach ncorporates hybrd energy functons to mantan the advantages of the tradtonal approaches and to elmnate the defcences of the same. The gravtatonal force produced by the force feld tres to drve a partcle to move towards boundares of the force-feld, whch embodes the tendency that a partcle pursues maxmzng the aggregate beneft of systems [15]. In Fgure 3, the pushng or pullng forces produced by other partcles are used to embody socal coordnaton s among agents. The self-drvng force produced by a partcle tself represents autonomy and personalty of ndvdual agents. Under the exerton of resultant forces, all the partcles may move concurrently n a force-feld [16]. In ths way, the GPM transforms the 1. Intalzaton: (n parallel) At tme t = 0, 1,, n, k1,, m. Agents A k(t), payment P k(t), ntenton strength k t vertcal coordnate of the partcle q k(t) 2. Calculaton: utlty U k(t), potental energy functons p(t), q(t), ψ (t), for each partcal s k(t) 3. Condton 1: If du t dt 0 k Hold for every partcle, fnsh wth success, Else goto step 4 4. Condton 2: Calculate du t dt 0 and update u k k t Calculate da t dt, k dp t dt k Ak t ak t 1 dak t dt Calculate Pk t pk t 1 dpk t dt Goto step 2. Fgure 2. Algorthm for generalzed partcle model. Fgure 3. Generalzed partcle model.
252 M. DHIVYA ET AL. problem-solvng process n Mult agent system (MAS) nto knematcs and dynamcs of partcles n the forceeld.when all the partcles reach ther equlbrum states, the soluton to the optmzaton of task allocaton and resource assgnment s obtaned. 4. Cuckoo Based Partcle Approach (CBPA) The energy functon for the Cuckoo Search s desgned as [17]; n1 100* d f df (6) 1 The above equaton s derved from Equaton (2), by consderng the value of ε mp as 100 pj/bt/m 2 for n = 2,.e.; communcaton range between the sensors. The ftness functon F, s consdered n ths problem for mnmzaton of energy and maxmzaton of lfetme of the nodes. 4.1. Proposed Algorthm for Data Fuson Input: A set of N sensor nodes n randomly deployed feld and a base staton. Step 1: Intalzaton: Select the number of sensor nodes, cuckoo nests, eggs n nests to start the search. Intalze the locaton and energy of nodes and the locaton of base staton. Step 2: Formaton of Statc Clusters: The clusters are formed, by Cuckoo Search technque. Each egg n a nest corresponds to a sensor node. A group of M nests are chosen wth N eggs n t. The probablty of choosng the best egg or qualty egg s done by random walk. Step sze and Levy angle s updated n each teraton. In turn the nests are updated. The optmal soluton.e.; best egg hgh energy node s taken as cluster head n context to energy, dstance between the nodes and dstance to the base staton. The worse nets are abandoned n normal Cuckoo Search. In order to compensate the unequal energy dsspaton, the worse nets (or) least energy nodes are allowed to jon the cluster as non cluster head nodes, n the proposed approach. The less energy nodes jon the proxmty cluster heads to form cluster. The cluster formaton s done by approprate advertsement of cluster-head to all other nodes to jon a partcular cluster. The cluster head s not permanent. In each run, accordng to the resdual energy of the nodes, the cluster head s perodcally changed. Ths helps to eradcate the communcaton overhead and redundancy. Step 3: Shortest Path Routng: After the clusters are formed, the Cluster Heads (CHs) fuse or aggregate the nformaton before forwardng t to the base staton. The energy model ncorporates free space rado model followed by all nodes. The nter cluster and ntra cluster routng va shortest path s to be performed based on the applcaton. Intra cluster refers to communcaton between cluster-head and non clusterhead nodes wthn the cluster. Inter cluster communcaton refers to communcaton between the clusters. For ntra cluster communcaton, the most wdely used methodology as followed by the basc LEACH algorthm concept s TDMA Schedulng- Tme Dvson Multple Access Schedulng s followed. For nter cluster communcaton, Generalzed Partcle Model s used. The objectve of usng GPM approach s optmzaton of route and extenson of the network lfetme. GPM s gven n many types accordng to the network optmzaton problems by Danxun Shua. Normally, communcaton between two Wreless Sensor Nodes happens, when there s no other nterferng node between two nodes. It s assumed that there exsts wreless path and lnk between two nodes durng communcaton. The Generalzed Partcle Model transforms the network shortest path problem nto knematcs and dynamcs of numerous partcles n a force-feld. Nodes are consdered as partcles, and utlty, overall utlty, potental energy due to gravtatonal force, potental energy due to nteractve force of partcles are calculated n each teraton. It s personfed n the descrbed model, as the resultant forces on the partcles are hgh, the partcles also move fast. Then the partcles are tested for stablty condton. If the partcles are stable, then the algorthm s termnated successfully. Else the partcles are updated, wth the hybrd energy equatons to obtan the optmal soluton. The shortest path s calculated by the lnk cost of each lnks. The lnk cost s substtuted as the resdual energy of nodes; wth context to the dstance to the communcatng nodes. In more bref terms the resdual energy of cluster head to communcatng cluster head s to be consdered for communcaton of the sensed data. After several numbers of teratons, the optmal path to transmt the data to the base staton s beng establshed. The mportant steps n Cuckoo Based Partcle Approach (CBPA) are lsted n Fgure 4. 5. Results and Analyss In ths secton the performance of the proposed technque s evaluated va smulaton results. The network model s smulated usng MATLAB. The results are summarzed after runnng several teratons. The focus on ths paper, as by the objectve functon, s mnmzaton of energy and maxmzaton of lfetme. The smulaton Parameters are lsted n Table 1.
M. DHIVYA ET AL. 253 Start Intalzaton of parameters In Fgure 5, the clusterng energy for hundred numbers of nodes s explaned. It s seen the clusterng energy s less than 0.05 joules for LEACH, less than 0.04 joules for HEED and less than 0.02 joules for CBPA. In Fgure 6 and 7, the network lfetme of nodes s explaned n context to the frst node death and rounds untl the last node des s explaned. Calculate the dstance of nodes to the base staton Formaton of Cluster Heads usng Cuckoo Search Formaton of clusters by jonng of Non Cluster-Head nodes Intra cluster communcaton by TDMA Schedulng Fgure 5. Clusterng energy vs number of nodes. Inter cluster communcaton by Generalzed Partcle Model Stop Fgure 4. Flowchart of cuckoo based partcle approach. Table 1. Smulaton parameters. Sensor deployment area Base staton locaton 100 m *100 m (50 m, 150 m) Number of nodes 100-200 Data Packet sze 100 bytes Control Packet sze 25 bytes Intal Energy 2 J E electrcal 50 nj/bt ε fs 10 pj/bt/m 2 ε mp 0.0013 pj/bt/m 4 Mode of Topology confguraton random MAC Protocol IEEE 802.15.4 Duty cycle duraton 1 second Cuckoo step sze 1 Round duraton 60 seconds Fgure 6. Rounds untl the frst nodes de vs. number of nodes. Fgure 7. Rounds untl the last node des vs. number of nodes.
254 M. DHIVYA ET AL. In Fgure 8, the average energy consumed per rounds s gven. Number of alve nodes versus number of teratons s gven n Fgure 9. Percentage of alve nodes explans the lfetme of the network. The number of teratons s taken as 100. In Fgure 10, the number of nodes versus the percentage of nodes as cluster heads s gven. Normally the number of clusters s desrable to be less, as t wll help n transton of node states. The energy consumpton per node ncreases, as the number of nodes ncreases. Ths s due to the fact that the exchange of nformaton between neghborhood nodes and the rado channels to compete n the spectrum. The number of cluster heads s hgh n LEACH and HEED when compared to CBPA. As the node densty ncreases, dependng on the applcaton there are chances for the number of clusters to ncrease or decrease n the tradtonal approaches. But n the proposed technque, t s evdent that the cluster head Percentage s wthn the range. Fgure 8. Average energy consumed per round vs number of rounds. Fgure 10. Number of nodes vs % of nodes as cluster heads. 6. Conclusons The cuckoo Based Partcle Approach s developed to acheve energy effcent Wreless Sensor Network and multmodal objectve functons. In ths paper cuckoo search s appled for cluster head selecton and formaton of clusters among the Sensor nodes. The proposed CBPA s compared wth the standard LEACH protocol and HEED protocol. The smulaton results exhbts that CBPA produces comparable results manly due to optmal search process n cluster formaton and allocaton of approprate paths n transmsson of sensed data. The developed suboptmal algorthm reduces complexty n chan formaton and prolongs the longevty of the Sensor Network. The results are obtaned by runnng more number of smulatons. The hybrd approach offers consstency n the cluster formaton, mnmal number of clusters, average energy consumpton and energy consumpton per rounds. In future, mult objectve constrants are to be consdered to obtan a realstc communcaton envronment, wth scalng and system complexty. Hybrd Optmzaton technques combned wth cross-layer desgn and Machne/Parameter learnng s a challengng ssue n research arena. 7. References Fgure 9. Number of alve nodes vs number of teratons. [1] F. Akyldz, W. Su, W. Sankarasubramanam and E. Cayrc, A Survey on Sensor Networks, IEEE Communcaton Magazne, Vol. 40, No. 8, August 2002, pp. 102-114. [2] F. P. Ferentnos, T. A. Tslgrds, Adaptve Desgn Optmzaton of Wreless Sensor Networks Usng Genetc Algorthms, Computer Networks, Vol. 51, No. 4, 2007, pp. 1031-1051. [3] M. Dhvya, M. Sundarambal and L. N. Anand, A Revew of Energy Effcent Protocols for Wreless Sensor
M. DHIVYA ET AL. 255 Networks, Proceedngs of 1st Internatonal Conference on Modelng, Control, Automaton and Communcaton, Tamlnadu, 20-21 December 2010, pp. 273-278. [4] D. We, Clusterng Algorthms for Sensor Networks and Moble Ad Hoc Networks to Improve Energy Effcency, Ph.D. Thess, Unversty of Cape Town, Rondebosch, September 2007. [5] A. G. Akojwar and R. M. Patrkar, Improvng Lfe Tme of Wreless Sensor Networks Usng Neural Network Based Classfcaton Technques wth Cooperatve Routng, Internatonal Journal of Communcatons, Vol. 2, No. 1, 2008, pp.75-86. [6] T. F. Shh, Partcle Swarm Optmzaton Algorthm for Energy-Effcent Cluster-Based Sensor Networks, IEICE Transactons on Fundamentals, Vol. E89-A, No. 7, 2006, pp. 1950-1958. [7] Q. Zhao and L. Tong, Energy-Effcent Informaton Retreval for Correlated Source Reconstructon n Sensor Networks, IEEE Transactons on Wreless Communcatons, Vol. 6, No. 1, 2007, pp. 157-165. do:10.1109/twc.2007.04885 [8] W. Henzelman, A. Chandrakasan and H. Balakrshnan, An Applcaton-Specfc Protocol Archtecture for Wreless Mcrosensor Networks, IEEE Transactons on Wreless Communcatons, Vol. 1, No. 4, 2002, pp. 660-670. [9] N. Aslam, S. Svakumar, W. Phllps and W. Robertson, Energy Effcent Cluster Formaton Usng a Mult-Crteron Optmzaton Technque for Wreless Sensor Networks, Proceedngs of IEEE Internatonal Conference on Consumer Communcatons and Networkng, Las Vegas, 11-13 January 2007, pp. 650-654. [10] O. Youns and S. Fahmy, Heed: A Hybrd, Energy- Effcent, Dstrbuted Clusterng Approach for Ad Hoc Sensor Networks, Transactons on Moble Computng, Vol. 3, No. 4, 2004, pp. 660-669. [11] X.-S. Yang and S. Deb, Cuckoo Search va Levy Flghts, Proceedngs of World Congress on Nature & Bologcally Inspred Computng, New Delh, 9-11 December 2009, pp. 210-214. [12] D. X. Shua, X. Wang and R Gong, A Generalzed Partcle Model for Socal Coordnaton and Autonomy n MAS, Proceedngs of the 2nd IEEE Internatonal Conference on Servces Systems and Servces Management, Chongqng, 13-15 June 2005, pp. 985-990. [13] X. Feng, F. C. M. Lau and D. X. Shua, A New Generalzed Partcle Approach to Parallel Bandwdth Allocaton, Computer Communcatons, Vol. 29, No. 18, 2006, pp. 3933-3945. [14] D. X. Shua, Q. Shua, Y. M. Dong and L. J. Huang, Problem-Solvng n Mult-Agent Systems: A Novel Generalzed Partcle Model, Proceedngs of the 1st IEEE Internatonal Mult-Symposums on Computer and Computatonal Scences, Hangzhou, 20-24 June 2006, pp. 322-329. [15] D. X. Shua and X. Feng, Dstrbuted Problem Solvng n Multagent Systems: A Sprng Net Approach, IEEE Intellgent Systems, Vol. 20, No. 4, 2005, pp. 66-74. [16] D. X. Shua, B. Zhang, C. P. Lu, A New Generalzed Partcle Dynamcs Model for Software Cybernetcs, Proceedngs of IEEE Internatonal Computer Software and Applcatons Conference, Chcago, 17-21 September 2006, pp. 240-245. [17] A. Chakraborty, K. Chakraborty, S. K. Mtra and M. K. Naskar, An Energy Effcent Scheme for Data Gatherng n Wreless Sensor Networks Usng Partcle Swarm Optmzaton, Journal of Appled Computer Scence, Vol. 6, No. 3, 2009, pp. 9-13.