6367(Print), ISSN (Online) Volume 3, Issue 3, October-December (2012), IAEME TECHNOLOGY (IJCET)

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1 INTERNATIONAL Internatonal Journal of Computer JOURNAL Engneerng OF COMPUTER and Technology (IJCET), ENGINEERING ISSN 0976 & TECHNOLOGY (IJCET) ISSN (Prnt) ISSN (Onlne) Volume 3, Issue 3, October - December (2012), pp IAEME: Journal Impact Factor (2012): (Calculated by GISI) IJCET I A E M E POWER EFFICIENT DATA AGGREGATION BASED ON SWARM INTELLIGENCE AND GAME THEORETIC APPROACH IN WIRELESS SENSOR NETWORK ABSTRACT 1 Bharath M A, 2 Vjaya Kumar B P, 3 Manjaah D.H 1 Department of CSE, RevaITM 2 Department of Telecommuncaton Engneerng, MSRIT 3 Department of CSE, Mangalore Unversty Whle performng data aggregaton n wreless sensor networks (WSN), sgnfcant energy s consumed not only durng actve communcaton but also durng dle state. For ths, an accurate estmate of energy cost and an ntellgent routng technque havng dstrbuted heurstc capabltes are necessary. In ths paper, we propose a power effcent data aggregaton technque based on swarm ntellgence and game theoretc approach for WSN. In ths technque, swarm ntellgence s used to perform route dscovery and to select the nodes wth maxmum power level as controller nodes. These controller nodes always reman awake to carry out data aggregaton and forwardng, whle other nodes are ether n sleep/awake state. In each round of data aggregaton forwardng, the controller nodes are adaptvely changed dependng on ther power levels. After the successful delvery of data to snk, game theory approach s appled to determne the energy consumed durng data aggregaton. By smulaton results, t s observed that the proposed approach reduces the energy consumpton and ncrease the packet delvery rato when compared wth the exstng technques. Keywords: Wreless sensor networks, Data aggregaton, Swarm ntellgence, Game theory, Energy effcency. I.INTRODUCTION A Wreless Sensor Network The ad hoc network possessng huge number of small sensor nodes postoned n huge quantty n order to ntut the physcal world s termed as wreless sensor networks (WSN). Its applcatons nclude both mltary and cvlan envronment servces. Here the sensor nodes are characterzed by lmted resources and nhbted power and they experence lmted computaton, communcaton and power resources. [1]. A new feld of computng known as Ubqutous computng or ubcomp n whch the user lfetme s completely pervaded by the computer. The ubqutous computng acts as an 184

2 nvsble force assstng the user n meetng hs or her needs wthout gettng n the way. For realzng ubqutous computng, WSN plays a vtal role. These devces called sensor nodes are usually deployed conssts of sensors, transcevers, processng unt, storage resources and actuators. Those nodes are deployed n networks for achevng some process of sensng. Typcally the sensor networks share a common communcaton channel. [2] WSN consttutes three classes of sensor nodes..e. sensng node, aggregator and snk. Sensng node detects the data and forwards these detected data to the aggregator node. Aggregator node gathers the data from sensng nodes subset wth the help of sutable aggregate functon such as sum, avg, mn, max etc and forwards t to superor aggregator or sent to snk drectly. Snk processes these data and dscovers a useful nformaton [5] B Data Aggregaton Usually n WSN, many quanttes of sensor nodes gather partcular nformaton from the surroundng and transmt through aggregator and later to the snk node. In the snk node, the nformaton are processed, nvestgated and utlzed by the applcaton. The wde-rangng approach n ths network s together processng the data created by the sensor node durng the tme of forwardng t towards the base staton. Ths method of processng the data together s termed as data aggregaton that gathers the data whch s approprate to smlar events. [4] The ultmate goal of data aggregaton s dsposng redundancy n the transmtted data whch reduces the quantty of data transmsson thus savng substantal amount of energy and bandwdth. The data aggregaton s categorzed based on the network structure as tree based and cluster based data aggregaton protocol. [5] In tree based data aggregaton, a tree structure s mantaned. Here leaf node acts as sensng node, remanng non-leaf node acts as aggregator node and root acts as base staton or snk. [5] In cluster based data aggregaton, nodes are dvded nto clusters. In each cluster, a cluster head s elected that executes the process of aggregaton n localzed manner aggregator node and further the aggregated data s sent to the next cluster head pathway to the snk. [5]. C Lmtaton of Data Aggregaton n WSN 1] In case the process of data aggregaton has some suspcons, the ntermedate nodes compute the partal result and later they wll be used n computng end results thus balancng the energy. The drawback of ths process s that fundamental networkng protocol to have a necessary support for synchronzaton. Ths synchronzaton enhances extra bandwdth wth respect to bandwdth, and energy the retort duraton of the queres. [6] 2] Usually the mult-sensng data elements are transmtted n sngle packet. But n aggregaton, only lmted number can be transmtted because of packet sze constrants [10]. D Swarm Intellgence An artfcal ntellgence technque that represents the clever actvtes wtnessed n swarms wth the help of mult-agent systems s termed as swarm ntellgence. A mult-agent system (MAS) s a system composed of multple nteractng ntellgent agents. They can be used to solve problems that are dffcult or mpossble for an ndvdual agent or a monolthc 185

3 system to solve. The smlar actvty of swarm s wtnessed n other organsms such as brds, fsh and nsects. Swarm behavor results n unexpected and organzed emergent behavors that make the creature swarm stronger. [7] In swarm ntellgence, there s no presence of head or any unversal scenaro. However, ntrcate ntellgence s resultant of nteracton of collectve effortless ntellgence. As the agents communcate only wth geographcal neghbors and local surroundng, they can get nformaton only from local envronment and modfes the confned surroundng [8]. The ndependent and self organzng agents are utlzed to model swarm and t pursues set of lowlevel rules whch presdes over ndvdual actvtes. An explct weght factor s related to each rule whch fnds the extent of ts power on agent s behavor. Snce every agent pursues the rule set, on the whole the actvtes of swarm organze to generate more complex evolvng performances. [7] The ant colony optmzaton (ACO) s related to one of the swarm ntellgence technque whch helps n performng route dscovery [14]. The creaton of new routes necesstates the use of a forward ant (FA) and a backward ant (BA). A pheromone track s establshed to the source node usng FA and to the destnaton node usng BA. FAs are launched n order to search the destnaton. It travels n the network from node to node and gathers detals about the node t has vsted. When t arrves at the destnaton, t s transformed nto backward ant. BAs backtracks the same path as forward ant follow. Its functon s to update the routng tables along the path based on the energy level collected by the forward ant. Snce BAs take addtonal data from the forward ants they can update both forward and backward paths at the same tme. [17] E Game Theoretc Approach The feld of appled mathematcs that demonstrates and examnes the decson makng condtons s termed as game theory. The set model n the game theoretc approach utlzes the payoff tables and the strateges nvolved n t are organzed. Here the set model s represented as game and those who make decson are represented as players. As a smple method to vew the crcumstance, the player selects a deed from pre-defned lst of actons that enhances ther proft. The utlty functon s used by players to examne the result of deed of neghbor players. The game s descrbed as follows. A game (G) n the normal form s vewed as: G =< D, S, { u }> Where D = {1, 2, 3 } s the set of players (decson makers), S s the acton set of player, Here S product of the acton set exstng to each player 186 S S S = n s the Cartesan { u } = { u 1... u 2 } s the utlty functon set that each player wshes to maxmze. For every player, the utlty functon α depend on the earler actons, α selected by the other players pror to player. jontly α and α buld a unque acton tuple α whch symbolzes the actvtes of each player. Mathematcally a s the fnest response by player to α f

4 { arg maxu ( a a )} a, Ths model concludes that there may be exstence of more stable model [9]. F Problem Identfcaton It has been observed that energy s not only consumed by the actve communcaton n wreless sensor networks, but also consumed n dle state. As a result, an mportant technque to reduce power consumpton durng data gatherng n sensor networks s to place nodes n the low power sleep state whenever possble. In order to overcome the problem of hgh energy consumpton durng data aggregaton, we propose a proper effcent data aggregaton based on desgn a power control technque usng swarm ntellgence and the game theoretc approaches. II. RELATED WORK Venkatesh Mahadevan et al [11] proposed a relable, nature-nspred routng algorthm called ACO for sensor networks. The algorthm s partly based on the effcent max-mn algorthm and t s sutable for flexble structure of wreless sensor networks and s not worse than other standard routng algorthms. Ths new routng scheme performs generally not worse than other standard routng algorthm, and n some occasons, t outperforms than mn-hop algorthm. Ayon Chakraborty et al [12] have proposed a novel data gatherng protocol for enhancng the network lfetme by optmzng energy dsspaton n the nodes. To acheve the desgn objectve partcle swarm optmzaton (PSO) wth Smulated Annealng (SA) have been appled to form a sub-optmal data gatherng chan and devsed a method for selectng an effcent leader for communcatng to the base staton. In the scheme each node only communcates wth a close neghbor and takes turns n beng the leader dependng on ts resdual energy and locaton. Ths helps to rule out the unequal energy dsspaton by the ndvdual nodes of the network and results n superor performance as compared to LEACH and PEGASIS. Swarup Kumar Mtra et al [13] proposed an Optmzed Lfetme Enhancement (OLE) Scheme whch shows enhanced performance over other schemes. OLE ncreases the network performance by ensurng a sub-optmal energy dsspaton of the ndvdual nodes despte ther random deployment. It employs modern heurstcs lke partcle swarm optmzaton nstead of the greedy algorthm as n PEGASIS to construct energy effcent routng paths. Extensve smulatons valdate the mproved performance of OLE. Saeed Mehrjoo et al [20] proposed a Novel Hybrd GA-ABC based Energy Effcent Clusterng n Wreless Sensor Network. As lfetme s drectly dependent upon the energy supples of the nodes, optmzaton of node energy consumpton s a robust approach to contrbute to the overall network lfetme. Network clusterng s one of the potental approaches to perform the optmzaton. To overcome ths problem, a hybrd algorthm based on Genetc Algorthm and Artfcal Bee Colony s proposed n ths paper. The algorthm resolves the ssue through fndng the optmal number of clusters, cluster heads and cluster members. Smulaton results reveal that ths algorthm outperforms LEACH and Genetc Algorthm based clusterng scheme. 187

5 Enrque Campos-Nañez et al [15] proposed a Game Theoretc Approach to Effcent Power Management n Sensor Networks. In ths paper, a dstrbuted scheme for effcent power management n sensor networks that s guaranteed to dentfy suboptmal topologes n an on-lne fashon s proposed. The scheme s based upon a general (game-theoretc) mathematcal structure that nduces a natural mappng between the nformatonal layer and the physcal layer. Suffcent condtons for the convergence of the algorthm to a pure Nash equlbrum and characterze the performance of the algorthm n terms of coverage s provded. R.Vall et al [16] proposed a power control soluton for wreless sensor network (WSN) consderng ECC n the analytcal settng of a game theoretc approach. The game s formulated as a utlty maxmzng dstrbuted power control game whle consderng the cost functon and the exstence of Nash equlbrum s studed. Wth the help of ths equlbrum a dstrbuted power control algorthm s devsed. From the analyss t s evdent that the system s power stable only f the nodes comply wth certan transmt power. The utlty of nodes employng ECC and wthout ECC s compared; the results show that the algorthm employng ECC acheves the best response for the sensor nodes by consumng less power. III. PROPOSED SOLUTION A Overvew We propose a power effcent data aggregaton based on swarm ntellgence and game theoretc approach for wreless sensor networks. In ths approach, swarm ntellgence based ant colony optmzaton s utlzed to execute route dscovery. In dscovered route, nodes wth maxmum energy level are chosen as controller nodes and remanng nodes are chosen as non-controller nodes. Ths s done to carry out the process of data aggregaton. The controller node gather the data packets receved from the source nodes and forwards t to nearby controller nodes and ths process proceeds tll the packet reaches the snk and the controller nodes always reman awake snce t makes all decsons related to routng and data aggregaton. In every round of route dscovery, the power level of the controller nodes s checked. When there s declne n the power level, then another node wth hgher power level s chosen as the controller node. Ths mples that the controller nodes keep changng adaptvely n every round. After successful transmsson of the data packet, the game theory s appled to determne the energy consumed durng data aggregaton. In ths approach, the utlty functon s calculated based on the number of data packets transmtted, number of successful transmsson and forwardng of the data performed by other nodes, n order to ensure relablty. Based on ths functon, the energy compensaton value s then calculated for forwardng the data packets. B Route Dscovery based on Swarm ntellgence We consder a swarm ntellgence technque based on ant colony optmzaton (ACO) for performng route dscovery. The procedure for route dscovery s as follows. 1) When the source (S o ) has a necessty to transmt the data packet to the snk, FA s launched from S o. FA chooses t movement to next neghbor node usng probablstc 188

6 decson rule (usng equaton 1). Usng ths rule, FA moves through the ntermedate nodes and gathers the status of the nodes (.e. node d, energy level, neghbor node status etc). ψ ε [ µ ( N, So).[ λ( N, So)] ψ ε N P k (N, S o ) = [ µ ( N, So)].[ λ( N, So)], f k L (1) N n r 0, otherwse Where N are the repeater nodes S o s the source node. µ ( N, S o) represent ntal pheromone value λ (N, S o ) represent the heurstc value related to energy level. n r represents the recever node. L N represents the routng table for node N. ψ andε are the parameters that control the relatve weght of the pheromone and heurstc value respectvely. 2) λ (N, S o ) n equaton (1) helps n selecton of controller nodes along the traversed path based on the energy level of the node. Ths means that the node wth lower energy has less probablty to get selected. The heurstc value of the node N s expressed as follows. 1 ( E en ) λ (N, S o ) = (2) 1 ( E e ) N n n r where E s the ntal energy e N s the current energy level of recever node N. 3) Each FA deposts a quantty of pheromone ( µ g (k) ) n the vstng node accordng to followng equaton. µ g (k) = 1/ V g s (k) (3) Where V g (k) represents the total number of nodes vsted by FA durng ts tour s represented as s at teraton k and each ant s represented by g = 1,2.n 4) The amount of pheromone at each lnk c (N, S o ) of the nodes s descrbed as follows. µ N, S ) (k) µ (k) + µ ( N, S )( k), c (N, S o ) s g (k), g = 1,., n (4) ( o N, S ) ( O o 5) Increasng pheromone amounts on the paths accordng to lengths of tours, V g (k) s, would contnuously cause an ncreasng postve feedback. In order to control the operaton, a negatve feedback, the operaton of pheromone evaporaton after the tour s accomplshed whch s descrbed as follows µ ( k) (1 ξ ) ( k) (5) j µ j Where ξ represents the control co-effcent whch determnes the weght of evaporaton for each tour. In smulatons, ACO parameter settngs are set to values ψ = 1, ε = 5, and ξ = 0.5 whch were expermentally found to be good by Dorgo [18]. 6) After FA reaches the snk, the snk generates BA and transfers all the nformaton of FA nto BA. The BA takes the same path as that of ts correspondng forward ant, but n the opposte drecton. The BA updates the routng table (LN) at N for all the entres related to the FAs destnaton node. 189

7 7) The BA upon reachng the source delvers the status of all nodes n the network. The source then selects the controller node whch s descrbed n secton III.C and transmts the data packet to the snk through the chosen controller nodes. C Selecton of nodes based on power level Based on the status of the nodes power level deposted by the BA to the source, the controller nodes are chosen. The nodes wth maxmum power level are selected as controller nodes (CN) and remanng nodes are selected as non-controller nodes (NCN). Ths s done n order to perform data aggregaton [3]. The controller nodes gather all receved data packet from the source nodes and forwards t to next controller node or to the snk (usng secton III.C.1 and III.C.2) as t makes all decsons related to routng and always remans awake. NCN are wthn the transmsson range of CN node and wll be n wake-up mode n perodcal manner. The steps nvolved n the selecton of CN are: The forward ant agent (FA) s launched n S o and t travels n the drecton of the snk through the ntermedate nodes n the network. The ants upon reachng every node updates ts lst wth the node detals such as node s d, flag (nforms the status whether node s CN or NCN), power level, node actvatng counter, and nformaton about the neghbors ( d, status and CN node) The nformaton of every node s collected and fnally FA delvers the detals nto snk (S ). The snk provdes all detals to the BA The backward ant agent (BA) sends the nformaton about the CN nodes along the path as a feedback to the source. Let R 1 represent the frst round where the path s establshed and source transmts the packets through the selected controller nodes at tme T 1. Let R 2 represent next round where another s establshed and source proceed ts transmsson of packets through the selected controller nodes at tme T2 and ths nvestgaton cycle contnues wth remanng range of tme perod to mnmze power consumpton. 1 Frst round (R 1 ) Durng R 1, S o transmts the data packets to the snk S through the chosen path (as per secton III.B). Let nodes 4, 7 and 9 be the CN at tme t1. (Ref fgure 1) Fg. 1 Controller nodes durng frst round R 1 When the packet reaches node 1, If node 1 s awake, Then the packets are transmtted to node

8 Node 1 checks ts neghbor nodes 2 and 3. If a node 2 or 3 s awake, Then the data s transmtted to node 2 or 3. End f Else The source checks the neghbor lst of node 1 whch s nodes 2 and 3. It then checks for the nearest CN of nodes 2 and 3. Then, the packets are transmtted to the nearest neghbor CN (node 4), drectly from S o. End f Node 4 checks for the nearest awake node If a node 5 or 6 s awake Then the data s transmtted to that node. End f Else The packets are transmtted to the nearest neghbor CN (7) drectly from node 4. Node 7 checks for the nearest awake node and packets are transmtted to node 9 drectly. End f Node 9 checks for the nearest awake node If node 10 or 11 s awake Then the data s transmtted to that node End f Else The packets are transmtted to the destnaton drectly from node 9 2 Second round R 2 After completon of R 1, Source S o transmts the data packets to the destnaton S through the path contanng another set of selected controller nodes. Let nodes1, 4, 8 and 10 be the CN at tme t 2. (ref. fgure 2) Fg.2 Controller nodes durng second round R 2 When the packet reaches node 1, the data s transmtted to that node snce t s CN. Node 1 checks the neghbor lst node 2 and 3 If a node 2 or 3 s awake, Then the data s transmtted to node 2 or

9 End f Else Node 1 checks the nearest CN of node 2 and 3 Then, the packets are transmtted to the nearest neghbor CN (node 4), drectly from node 1. End f Smlarly the process proceeds as per frst round at tme T 1. D Game model of data packet forwardng After the route dscovery process, the game theory approach s appled whch determnes the energy consumed durng data aggregaton. A player conssts of a set of nodes ncludng the source and ts correspondng nodes nvolved n transmsson of the data to the snk. For each node n the network, we consder the followng assumpton Let P (t) be the total number of data packets transmtted by the controller node to other nodes Let S (t) be the number of data packets transmtted successfully Let F (es) be the avalable energy compensaton for provdng approval to forward data packets Let h be the average number of hops crossed by the exchange of data between the source and snk nodes Let k be the number of controller nodes nvolved n the aggregaton and forwardng of data between the source and snk nodes. The energy compensaton value s then calculated for forwardng the data packets usng the utlty functon (usng equaton 6). The mathematcal expresson for a utlty functon n the model of DR-G (Data Relayng base on Game Theory) s as follows: U (P (t), S (t)) = k h S (t) - P (t) + F (es) (6) The followng assumptons are made from the vewpont of each node. 1) The node obtans nterest from the network, when the network forwards the data packet successfully. 2) The node pays costs for the network, when the node accepts the forwardng request to forward data packets for the network. As the average number of hops (h) crossed by the exchange of data between the source and snk nodes are not less than l, the benefts receved after every successful transmsson of data packet s h tmes than the loss for forwardng a data packet for the network. If a node agrees to forward the data packet, then t wll get awards from the network, whch s energy compensaton, to encourage the node forwardng data. If the node refuses to forward data packets, then t wll not get the energy compensaton, as a punshment to nodes from the network. Usng equaton (6), a decson functon of node forwardng s ntroduced as follow, whch s used to determne whether to forward data for the other nodes. where 1, k h S ( ) ( ) + ( ) 0 ' t P t F es ( P ( t), S ( t)) = (7) 0, k h S ( t) P ( t) + F ( es) < 0 192

10 h s the average number of hops crossed by transmttng a data packet to the snk node k s number of controller nodes nvolved n the aggregaton and forwardng of data to the snk node. F (es) s the avalable energy compensaton for agreeng to forward data packets. If ( S ( t), P ( t)) = 1, Then N agrees to forward; End f If ( S ( t), P ( t)) = 0, Then End f N refuses to forward E Overall algorthm Step 1 The swarm ntellgence technque s utlzed to perform the process of route dscovery. Step 2 In the dscovered route, the nodes wth maxmum power level are chosen as controller nodes (CN) and remanng nodes are selected as non-controller nodes (NCN). Ths s done for the purpose of data aggregaton. Step 3 The controller node gather the data packets receved from the source nodes and forwards t to nearby controller nodes and ths process proceeds tll the packet reaches the snk and the controller nodes always reman awake snce t makes all decsons related to routng. Step 4 In every round of route dscovery, the power level of the controller nodes n checked. When there s declne n the power level, then another node wth hgher power level s chosen as the controller node. Ths mples that the controller nodes keep changng adaptvely n every round. Step 5 After successful transmsson of the data packet, the game theory s appled to determne the energy consumed durng data aggregaton. Step 6 In game theory, the utlty functon s calculated based on the number of data packets transmtted, number of successful transmsson and forwardng of the data performed by other nodes. Based on ths functon, the energy compensaton value s then calculated for forwardng the data packets. Advantages of ths approach 1) Ths data aggregaton technque saves communcaton overhead whch occurs due to addtonal computaton and memory resources. 193

11 2) As the controller nodes selected for data aggregaton are based on ther energy level and also t keeps changng adaptvely n each round, the power consumpton n the network can be reduced to larger extent. 3) Use of game theory, helps to estmate the forwardng cost n terms of energy consumpton. IV. Smulaton Results The proposed Power Effcent Data Aggregaton usng Ant (PEDA-ANT) agents, s evaluated through NS2 [19] smulaton. We consder a random network of sensor nodes deployed n an area of 500 X 500m. The number of nodes s vared as 20,40,60,80 and 100. Two snk nodes are assumed to be stuated 100 meters away from the above specfed area. The smulated traffc s CBR wth UDP source and snk. The number of controller nodes s selected as 4 for two dfferent scenaros. Table 1 summarzes the smulaton parameters used Table 1: Smulaton Parameters No. of Nodes 20,40,60,80 and 100 Area Sze 500 X 500 Mac Routng protocol PEDA-ANT Smulaton Tme 50 sec Traffc Source CBR Packet Sze 512 bytes Rate 100kb Transmsson Range 250m No. of Sources per cluster 1, 2,3 and 4 Transmt Power w Recevng power w Idle power w Intal Energy 7.1 Joules A Performance Metrcs The performance of PEDA-ANT s compared wth the Energy Effcent Scheme for Data Gatherng Usng Partcle Swarm Optmzaton (EEDG-PSO) [12] scheme. The performance s evaluated manly, accordng to the followng metrcs. Average end-to-end delay: The end-to-end-delay s averaged over all survvng data packets from the sources to the destnatons. Average Packet Delvery Rato: It s the rato of the number.of packets receved successfully and the total number of packets transmtted. Energy: It s the average energy consumed for the data transmsson. 194

12 A. Based on Nodes In our ntal experment, we vary the number of nodes as 20,40,60,80 and 100. Nodes Vs Delay Delay(Sec) EEDG-PSO PEDA-ANT Nodes Fg.3 Nodes Vs Delay Nodes Vs DelveryRato Delvery Rato EEDG-PSO PEDA-ANT Nodes Fg. 4 Nodes Vs Delvery Rato Nodes Vs Energy 8 Energy(J) EEDG-PSO PEDA-ANT Nodes Fg.5 Nodes Vs Energy When the number of nodes n the network s ncreased, generally t wll result n ncrease of end-to-end delay, snce the number of hops may ncrease, n the routng path. Fgure 3 shows that the delay s lnearly ncreased for both the schemes when the number of nodes s vared from 20 to 100. It shows that our proposed PEDA-ANT protocol has an average of 83% lower delay than when compared to EEDG-PSO, snce the controller nodes are always awake n PEDA-ANT, the sleep watng tme s mnmzed. 195

13 When the number of nodes n the network s ncreased, t leads to more packet drops due to ncreased number of hops and contenton. Hence the average packet delvery rato decreases when the number of nodes s ncreased. Fgure 4 gves the packet delvery rato for both the schemes when the number of nodes s ncreased. It shows that our proposed PEDA- ANT protocol acheves delvery rato more than 18% of EEDG-PSO n average. Ths s because of the fact that the transmsson falure s mnmzed by usng the controller nodes as aggregaton pont n PEDA-ANT. Snce the sleep duty cycle s adaptvely changng and controller nodes are selected based on power level, PEDA-ANT protocol utlzes 16% lower energy when compared to EEDG-PSO. Fgure 5 shows ths. b. Based on Sources In our second experment we vary the number of sources as 1, 2, 3 and 4. Sources Vs Delay 8 Delay(Sec) EEDG-PSO PEDA-ANT Sources Fg.6 Sources Vs Delay Sources Vs Delvery Rato Delvery Rato EEDG-PSO PEDA-ANT Sources Fg.7 Sources Vs Delvery Rato 196

14 Sources Vs Energy 5.5 Energy(J) EEDG-PSO PEDA-ANT Sources Fg. 8 Sources Vs Energy When the number of sources n the network s ncreased, t wll result n ncrease of end-to-end delay, snce the traffc load may ncrease, n the routng path. Fgure 6 shows that the delay s ncreased for both the schemes when the number of sources s vared from 1 to 4. We can see that our proposed PEDA-ANT protocol has an average of 42% lower delay than when compared to EEDG-PSO, snce the controller nodes are always awake n PEDA-ANT, the sleep watng tme s mnmzed. When the number of sources n the network s ncreased, t leads to more packet drops due to ncreased traffc load and congeston. Hence the average packet delvery rato decreases when the number of nodes s ncreased. Fgure 7 shows the packet delvery rato for both the schemes when the number of sources s ncreased. It shows that our proposed PEDA-ANT protocol acheves delvery rato more than 41% of EEDG-PSO n average. Ths s because of the fact that the transmsson falure s mnmzed by usng the controller nodes as aggregaton pont n PEDA-ANT. Fgure 8 gves the energy consumpton when the number of source s ncreased. It shows that our proposed PEDA-ANT protocol utlzes 6% lower energy when compared to EEDG-PSO, Snce the sleep duty cycle s adaptvely changng and controller nodes are selected based on power level. V. Concluson In ths paper, we have proposed a power effcent data aggregaton based on swarm ntellgence and game theoretc approach for wreless sensor networks. In ths approach, swarm ntellgence based ant colony optmzaton s utlzed to execute route dscovery. In the dscovered route, nodes wth maxmum energy level are chosen as controller nodes and remanng nodes are chosen as non-controller nodes to perform data aggregaton. The controller nodes always reman awake snce t makes all decsons related to routng. In every round of route dscovery, the power level of the controller nodes n checked. When there s declne n the power level, then another node wth hgher power level s chosen as the controller node. Ths mples that the controller nodes keep changng adaptvely n every round. After successful transmsson of the data packet, the game theory s appled to determne the energy consumed durng data aggregaton. By smulaton results, we have shown that the proposed approach reduces the energy consumpton and ncrease the packet delvery rato when compared wth the exstng approach. 197

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