IMPROVEMENT of MULTIPLE ROUTING BASED on FUZZY CLUSTERING and PSO ALGORITHM IN WSNS TO REDUCE ENERGY CONSUMPTION

IMPROVEMENT of MULTIPLE ROUTING BASED on FUZZY CLUSTERING and PSO ALGORITHM IN WSNS TO REDUCE ENERGY CONSUMPTION Gholamreza Farahan Department of Electrcal Engneerng and Informaton Technology, Iranan Research Organzaton for Scence and Technology (IROST), Tehran, Iran ABSTRACT One of the most mportant ssues dscussed n Wreless Sensor Networks (WSNs) s how to transfer nformaton from nodes wthn the network to the base staton and select the best possble route for transmsson of ths nformaton, takng nto account energy consumpton for the network lfetme wth maxmum relablty and securty. Hence, t would be useful to provde a sutable method that would have the features mentoned. Ths paper uses an Ad-hoc On-demand Multpath Dstance Vector (AOMDV) as a routng protocol. Ths protocol has hgh energy consumpton due to ts multpath. However, t s a bg challenge f t can reduce AOMDV energy consumpton. Therefore, clusterng operatons for nodes are of hgh prorty to determne the head of clusters whch LEACH protocol and fuzzy logc and Partcle Swarm Optmzaton (PSO) algorthm are used for ths purpose. Smulaton results represent 5% mprovement n energy consumpton n a WSN compared to AOMDV method. KEYWORDS Energy Aware Routng Protocol, Fuzzy Logc, Ad-hoc Multpath, LEACH, Partcle Swarm Optmzaton Algorthm 1. INTRODUCTION The use of WSNs n a varety of sectors, such as mltary, ndustral, agrculture, medcne, etc. s a unque feature of the world today due to ts ease of use. The deployment of nodes n a random or predefned manner n a specfed two-dmensonal or three-dmensonal form as sensor nodes for gatherng nformaton creates a WSN. One of the ssues that s consdered a challenge n WSNs s the routng ssue, along wth reducng energy consumpton. So far, varous mechansms have been ntroduced to collect, send and process data n WSN. One of these operaton mechansms s network clusterng. Clusterng s one of the methods used to collect and send packets n a WSN. Ths operaton has advantages such as system scalablty, ncreased network lfetme, and reduced redundancy n sendng and consumng energy. Choosng the cluster head n clusterng nodes s an mportant step, because clusterng requres energy consumpton and may be wasted a lot of energy. One of the most famous protocols n clusterng s LEACH [1]. As dscussed, routng should be aware of energy usage. One of the routng protocols s the Adhoc On-demand Dstance Vector (AODV) [2], whch has hgh power consumpton due to ts multpath and multchannel packet sendng durng routng tme. In order to mprove the effcency of the AODV protocol, a method called the AOMDV s proposed [3], the most mportant effect DOI: 10.5121/jcnc.2018.10606 97

beng the addton of multpath capablty n the AODV classcal protocol. The ntroducton of node states to enhance the AODV's effcency n selectng the man path s an mportant goal of ths new protocol. In the path dscovery process, the rules for updatng the route wll calculate the node's weght for each path, as well as sortng the path sze n descendng order n the lst of paths, and a route wll choose that provde more path weght for data transfer. There s also the use of Route Request (RREQ) packet delay to send packets on the network as well as the threshold of energy to smplfy network congeston. Therefore, evolutonary methods need to be developed to solve ths problem n order to mprove the energy consumpton for the longer lfetme of nodes and ultmately the network [4]. In ths paper, a method for energy aware routng n WSN wll be presented. In the proposed method, fuzzy logc and PSO algorthm are used to mprove the AOMDV routng protocol and the LEACH clusterng protocol, and a new routng scheme wth mnmum energy consumpton s proposed. In the feld of WSNs energy aware routng, there are many studes. In DAM et al. [5] and Kevn [6], ppelne and power technques are suggested for cryptosystem to reduce power consumpton. In addton, t s expected to ncrease the level of securty and acheve hgh performance that makes t sutable for unnterrupted applcatons, as dscussed n Nguyen et al. [7]. In Rages and Baskaran [8], estmatng the lfetme of a Body WSN wth use of probablstc analyss and Monte Carlo smulaton s proposed. Ths framework makes possble to control the unnterrupted health of patents wth wearable vtal sgnal wreless sensors. In the control of health, the loss of crtcal or emergency nformaton s a serous ssue. Therefore, ensurng the qualty of servce provson s essental. It s mportant to have an estmated lfetme of the network to replace or change batteres because the loss of mportant nformaton s not acceptable. The lfetme of the body WSN s defned as the duraton of the falure of the frst node due to battery dran. The heart rate and blood glucose levels are controlled n a centralzed locaton n a health / medcal envronment managed to evaluate the performance of the physcal WSN. In the other research, n Sngh and Verma [9], a homogeneous protocol has been presented that s senstve to the optmal energy consumpton dstrbuted on the adaptve threshold based on the mddle layer routng protocol. In ths research, probablstc weght s assgned to cluster heads of each cluster from the network, and the purpose of the research s to provde a new protocol for reducng dstrbuted energy consumpton durng routng. Ke et al. [10] has proposed a herarchcal clusterng approach to reduce energy consumpton n the WSN durng routng, whch named the proposed protocol as novel energy-aware herarchcal cluster-based (NEACH). Ygt et al. [11] has presented channel aware routng and the multchannel tmng prorty for routng s used. Channel selecton s carred out durng routng tme wth mnmzng energy consumpton. The use of a Lnk-Qualty-Aware Routng Algorthm (LQ-CMST) along wth the Prorty and Channel-Aware Mult-Channel (PCA-MC) for ntellgent WSN applcatons are consdered. In [12], the base staton s consdered as the Regon of Interest (ROI). The smultaneous use of two protocols, called On-Hole Chldren Reconnecton (OHCR) and On-Hole Alert (OHA), wth ther dstrbuted natural propertes, can solve the power consumpton problem n remote locatons. Two mentoned protocols have been dentfed usng two Degree Constrant Tree (DCT) and Shortest Path Tree (SPT) that has been able to contrbute up to 50% to energy storage. 98

Vmalaran et al. [13] proposed an Enhanced PSO-Based Clusterng Energy Optmzaton (EPSO- CEO) algorthm for WSN n whch clusterng and clusterng head selecton are done by usng PSO algorthm wth respect to mnmzng the power consumpton n WSN. Some researches focus on the metaheurstc methods. Kula and Jana [14] are proposed Lnear/Nonlnear Programmng (LP/NLP) formulatons of energy effcent clusterng and routng followed by two algorthms for the same based on PSO. The routng algorthm s developed wth an effcent partcle encodng scheme and mult-objectve ftness functon. The clusterng algorthm s presented by consderng energy conservaton of the nodes through load balancng. The results of algorthms demonstrate ther superorty n terms of network lfe, energy consumpton, dead sensor nodes and delvery of total data packets to the base staton n comparson wth other methods. Balaj et al. [15] presented a fuzzy based PSO routng technque to mprove the network scalablty. Sgnfcantly, n the cluster formaton procedure, fuzzy based system s used to solve the uncertanty and network balancng. Cluster heads are calculated usng PSO algorthm to reduce the energy consumpton. Ther smulaton results show that the proposed routng protocol can perform load balancng effectvely and reduce the energy consumpton of cluster heads. In other recent research, a Trust-Based Secure Routng (TBSR) scheme usng the traceback approach has been proposed to mprove the securty of data routng and maxmze the use of avalable energy n Energy-Harvestng WSNs (EHWSNs) [16]. Fardn Far and Alae [17] are proposed a method to ncrease the effcency of the Optmzed Lnk State Routng Protocol (OLSR) [18] by generatng new parameters and consderng the amount of nodes energy. They have selected the optmal route based on the remaned energy n the mddle nodes, the dstance between the nodes and the number of steps wth use of the Genetc algorthmbased approach for optmal routng n the OLSR protocol. An Energy-Balanced Routng Protocol (EBRP) for WSNs s proposed n [19]. In EBRP, the network s dvded nto several clusters by usng K-means++ algorthm [20] and select the cluster head by usng the Fuzzy Logcal System (FLS). To get the fuzzy rules for dfferent networks, Genetc Algorthm (GA) s used. EBRP compared wth the routng protocols such as LEACH, Low-Energy Adaptve Clusterng Herarchy-Centralzed (LEACH-C) [21], and Stable Electon Protocol (SEP) [22], whch prolongs the network lfetme (frst node des) by 57%, 63%, and 63%, respectvely. Kamran Khan et al. [23] proposed routng algorthm for the transmsson of data, cluster head selecton algorthm, and a scheme for the formaton of clusters named Energy-Effcent Multstage Routng Protocol (EE-MRP). Based on the energy analyss of the exstng routng protocols, they proposed a multstage data transmsson mechansm. They adopted an effcent cluster head selecton algorthm and extermnated unnecessary frequency of reclusterng. Statc clusterng s used for effcent selecton of cluster heads. They compared the performance and energy effcency of ther routng protocol wth other routng protocols and observed ther routng protocol (EE-MRP) has performed well n terms of overall network lfetme, throughput, and energy effcency. Other research uses a geographc routng protocol to route the packets. The geographc routng protocol route packets n a hop-by-hop way, where a node selects a relay node to forward packets among the neghborng nodes based on the geographc locaton nformaton of the neghborng nodes. To employ geographc routng protocols, two neghborng nodes need to exchange the locaton nformaton wth each other perodcally. In a moble ad hoc network, however, a packet transmtted between two neghborng nodes may be lost due to the out-of-date locaton nformaton, whch result n demandng extra energy to retransmt the packet. 99

Tang et al. [24] by consderng the out-of-date neghborng locaton nformaton, proposed two methods capable of augmentng geographc routng protocols to reduce energy consumpton n moble ad hoc networks. The frst one uses a tradeoff between the progress dstance and the energy consumpton when selectng a relay node. The second one uses energy consumpton when selectng a relay node, to consume mnmum energy to route a packet between a sourcedestnaton par n the contnuous doman. Ther results shown method of Tang can reduce the energy consumpton whle preservng the hgh packet delvery rate. Also, to exchange energy-effcent messages among neghborng nodes, the reactve type EAO (Energy-Aware One-to-one routng) [25] and LEU (Low-Energy Uncast Ad-hoc routng) [26] protocols are proposed to uncast messages to the destnaton node. In the EAO protocol, the total electrc energy of nodes and delay tme from a source node to a destnaton node can be reduced compared wth the ESU [27] and AODV protocols. However, a source-to-destnaton route may not be found f the communcaton range of each node s shorter. To solve ths problem IEAO (Improved Energy-Aware One-to-one routng) protocol [28] s proposed. In IEAO protocol, after a shortest route s found to the destnaton node, a more energyeffcent pror node s found n nearest neghbor of each node startng from the destnaton node. Therefore, a neghbor node whch has an uncovered neghbor node s selected as a pror node for each node to make a route. At contnuaton of paper n secton 2, model of the system s explaned, then secton 3 wll descrbe the proposed method. Smulaton results are presented n secton 4 and fnally secton 5 concludes the paper. 2. MODEL OF SYSTEM In ths secton, frst, some defntons and models to calculate power consumpton n WSN s explaned. 2.1. Defnton a) Sensng range It s range that a sensor can sense a partcular area. As shown n fgure 1, a sensng range of sensor S s a crcle wth radus r. b) Communcaton range Fgure 1. Sensng range of sensor S s nsde of crcle wth radus r It s range that sensor can communcate wth another sensor. c) Degree of coverage When an area s covered by a sensor S, then the degree of coverage of that area s one because t s covered wthn the sensng range of only one sensor. 100

2.2. Condton of ntersecton If two sensors S 1 and S 2 are consdered. Both two sensors are ntersect wth each other when sum of radus s less than and equal to dstance between centers. Therefore, followng condton could be rased. a) Case 1: Two sensors sensng range has ntersecton (fgure 2). Fgure 2. Two sensors S 1 and S 2 sensng range has ntersected wth each other b) Case 2: Two sensors sensng range has touch wthout creatng any ntersecton area (fgure 3). Fgure 3. Two sensors S 1 and S 2 sensng range has touch wth each other c) Two sensor sensng range separate wth each other (fgure 4). Fgure 4. Two sensors S 1 and S 2 sensng range separate wth each other d) One sensor s wthn another sensor sensng range (fgure 5). Fgure 5. Two sensors S 1 and S 2 have ntersect wth each other 2.3. Determnaton of ntersecton area To calculate ntersecton area, accordng to fgure 6, the two followng equatons should be solved. 2 2 x + ( y 1) = 1 (1) 101

2 2 ( x 1) + y = 1 (2) Fgure 6. Two ntersect S 1 and S 2 sensors If the lne y=x s drawn on the graph, the ntersecton area wll splt nto two equal peces (fgure 7). Fgure 7. Two equal peces of ntersecton area Now, notce that we can form a trangle n the S 1 crcle, from the dashed lne to the center at (1,1). It wll be a 45º-45º-90º rght trangle (fgure 7). The area of quarter of the crcle wth radus 1 s π/4 and the area of the trangle s 1/2. Therefore, the area of S 1 crcle above the lne y=x s as equaton (3). π 1 ( π 2) = 4 2 4 (3) The equaton (3) s half of the ntersecton area, therefore, the entre ntersecton area s: π 1 ( π 2) 2 ( ) = 4 2 2 (4) Thus, the ntersecton area on the case of radus for both sensng range equal 1 s calculated. If radus of sensng range of sensors equal wth r, the ntersecton area wth be as equaton (5). ( π 2) r 2 2 (5) 102

3. PROPOSED METHOD In ths secton, the proposed algorthm to reduce energy consumpton n WSN wll explan. Intally, a seres of ponts and postons are defned by default, so that sensor nodes can be dentfed and replace at routng tme. It should be noted that all of the sensor nodes deployed at the default ponts are fxed and are n fact statc. The base staton, announcng the target and targets calculate the remanng energy n each round through equaton (6) and then mantan the remanng energy. V ( t) = [ Intal E ( t)] / r (6) In equaton (6), Intal s the ntal energy, E ( t ) s the energy of node and r s the current cycle. It should consder a regon for sensng. If there are two s 1 and s 2 sensors, both of these sensors ntersect wth each other when the total radus of the area s less than and equal to the dstance between each center n of that area and ts relaton s gven by equaton (7) (fgure 2). r + r ( x x ) + ( y y ) 2 2 1 2 2 1 2 1 (7) As can be seen n equaton (7), the Eucldean dstance s consdered that r 1 s the frst sensng range radus, r 2 s the secondary sensng range radus, (x 1,y 1 ) and (x 2,y 2 ) are coordnates n the Cartesan system for S 1 and S 2 sensors respectvely. There are several specal cases that should be nvestgated. When the sensng range from one sensor to another sensor s separated, there s equaton (8). Dstance( s, s ) > r + r 1 2 1 2 (8) When a sensor s located wthn the sensng range of another sensor, there wll be equaton (9). Dstance( s, s ) < r + r 1 2 1 2 (9) When two sensors are only touched, wthout creatng an ntersected area, the equaton (10) wll exst. Dstance( s, s ) = r + r 1 2 1 2 (10) Consderng the three condtons of the equatons (8) to (10) s vtal. It s also mportant to determne the area for ntersecton the sensng range of sensng nodes. When the sensng range of the sensors cross each other, a zone wll create n the form of equaton (11). r - r < Dstance( s, s ) < r + r, where r > r 1 2 1 2 1 2 1 2 (11) After specfyng the ntersected area, the degree of coverage for the AODMV protocol settngs can be determned for multpath. When the two sensng range of sensors ntersected wth each other, ther coverage s equal to one that s defned by the defnton of the coverage. The relatonshp between the degree of coverage for the two sensors s 1 and s 2 s a proven equaton (12) [29]. 103

f ( s I s = x) then s U s = s s + s s + s I s = 1 x + 1 x + x 1 2 1 2 1 2 2 1 1 2 2 2x + x = s U s 2 x = s U s 2 s1u s2 = x 2 s U s = s I s 1 2 1 2 1 2 1 2 (12) If a sensor covers a regon, the coverage degree wll be equal to one, and f two sensors cover a regon, the coverage degree of that area wll be equal to 2, whch s calculated from the proven equaton (13) [29]. s1u s2 = s1 + s2 s1i s2 = 1+ 1 2 = 0 s1u s2 = 0 (13) By replacng equaton (13) s equaton (12), wll result equaton (14). s1i s 2 = 2 (14) Now after fndng the ntersected area between nodes of WSN, the AOMDV protocol s consdered as a multpath method and a clusterng operaton s performed. The purpose of usng fuzzy logc n the paper s to dentfy and dstngush cluster patterns n the network. Frst, a known node wll select as a cluster head. The cluster head collects other nformaton and sends t to the central staton. The cluster head functons lke a local central staton sensor. Selecton of the cluster n ths paper s carred out wth the help of fuzzy membershp functons and fuzzy rules. Therefore, n order to better dstrbute the load between the sensor nodes, the same cluster that ncludes the cluster head, wll be used. In the network, there are several clusters, each wth a cluster head, and each node belongs to a cluster that s geographcally dstrbuted throughout the network. Cluster head s used to ncrease network lfe and reduce energy consumpton. The cluster s dynamcally selected accordng to ts energy. The default protocol n clusterng and choosng the cluster head s LEACH that fuzzfcaton s carred out on t. The AOMDV protocol conssts of two steps, clusterng, and schedulng for nodes that operate on packet transmssons n the network, and each node produces a random number between 0 and 1. The nodes that have values below the threshold are selected as the cluster head. Hence, due to the uncertanty, a fuzzy relaton can be consdered for nodes (Equaton (15)). p T ( N) =, f n G r mod 1 1 p. p (15) In equaton (15), p s a value that checks f the node has cluster head condton. r s the number of cycles to select the cluster head and G s a set of nodes that are not selected as cluster heads. Intally, the network area s dvded nto two regons, called regon 1 and regon 2, whch nodes n regon 1 have a hgher probablty to select as a cluster head. It should be noted that the base staton s located as a fxed area n the center of the network regon. The base staton calculates the dstance from the snk node to compare wth threshold value (equaton (16) derved from equaton (12)). ( 2 ( 2 2 )) TR = a + b (16) 104

where TR s the threshold and a and b are coordnated n the Cartesan system. In equaton (16), nodes whose dstance s greater than the threshold wll dscard, and the node that s near the center wth less energy s chosen as the snk node. In the proposed method, the two AOMDV and LEACH protocols wll fuzzy smultaneously. Two mportant parameters that are used as fuzzy nputs to select the cluster head n the AOMDV protocol are the energy level and the center of the node n a set wth respect to ts neghborng nodes and two other mportant nput parameters for the LEACH protocol, are amount of energy consumed n the packet sendng path that s used by the node and the number of steps n the path. Calculaton of energy consumpton for each cluster and the packet sendng path s crtcal to mnmzng energy consumpton. The two parameters of the AOMDV protocol are updated at the request of the packet from one node to the other node n a cluster or outsde the cluster. Then the frst node that has the destnaton path sends two values of the LEACH protocol for clusterng and packet response on the network. Untl the node for sendng respond s not on the destnaton, there wll be a packet request n the network untl t reaches the destnaton. Ths s due to determnaton the amount of energy consumed on the path. Then the output results of the fuzzy segment wll be the nput of the routng secton for the AOMDV protocol agan, whch wll frst fnd the optmal route, and second, the least amount of energy wll be consumed. The number of nodes n each path other than the source node and destnaton node s known as a step. After clusterng and choosng a cluster head based on fuzzy logc and LEACH and AOMDV algorthms, an optmzaton algorthm s requred to mprove the routng of the AOMDV protocol. Hence, PSO algorthm s used wth regard to ts advantages. Perhaps one of the most mportant reasons for usng the PSO algorthm s the hgh convergence rate compared to other evolutonary algorthms. The best value to mprove energy consumpton n the routng process s named P and the best poston ever known by the partcle populaton s named G best. After fndng the best values, the velocty and poston of each partcle are updated usng equatons (17) and (18), respectvely. best [ ] = [ ] + 1 () ( best [ ] [ ]) + 2 () ( best [ ] [ ]) v partcles v C rand P poston C rand G poston [ + 1] = [ ] + [ ] poston poston v (17) (18) where v[] s the partcle velocty, poston[] s the current partcle (soluton), rand () s a random number between (0,1) and C 1, C 2 are learnng factors. usually C 1 = C 2 = 2. The rght sde of equaton (17) conssts of three parts: the frst part s the current velocty of the partcle, and the second and thrd parts are partcle velocty change and ts rotaton towards the best personal experence and best experence of the group. If the frst part of equaton (17) s not taken nto account, then the partcle velocty s determned only by the current poston and the best experence of the partcle and the best group experence. In ths way, the best partcle of the group stays n place and the others move toward that partcle. In fact, the mass movement of partcles wthout the frst part of Equaton (17) wll be a process n whch the search space gradually becomes small and a local search around the best partcle forms. In contrast, f only the frst part of equaton (17) s taken nto account, partcles wll go ther own way to reach the boundary wall and perform a knd of global search. 105

The convergence rate s another mportant ssue n the PSO algorthm. Several methods have been proposed to ncrease the convergence speed of the optmal partcle swarm algorthm. Ths scheme usually nvolves changes to the PSO algorthm update equatons, wthout alterng the structure of the algorthm. Therefore, there s usually a better result n local optmzaton performance, whch s sometmes carred out wth a slght change n functonal performance. One of the advances n PSO algorthm s the use of weght nerta. The weght nerta s a factor n scalng the speed assocated wth the prevous step. As a result, a new equaton for updatng speeds can be found n equaton (19). ( ) ( ) w( t). V ( t 1) + C r P X ( t 1) + C r g X ( t 1) 1 1 2 2 Consderng the results of the PSO algorthm, w(t) has been consdered n the nterval [0, 1.4], but over tme, the results of the experments show that there s a certan amount n the nterval [0.8, 1.2], whch results n greater convergence. In some cases, for smplcty, the w(t) value s equal to 1. The acceleraton coeffcents C 1 and C 2 n equaton (19), and n essence, control the extent to whch a partcle wll move n a sngle repeat. Values for both coeffcents are set to 2. The performance of the PSO algorthm depends on the parameter settngs, whch nclude the weght nerta w(t), acceleraton coeffcents C 1 and C 2, the maxmum number of repettons T, and the ntalzaton of the populaton. Weght nerta usually decreases unformly from the maxmum number of repettons T. When a partcle s moved to a new poston, a new soluton s found for each object. Ths soluton s evaluated by a ftness functon. A WSN wth a number of nodes s consdered as T={τ 1,τ 2,,τ N } and the number n of potental poston as P={p 1,p 2,...,p n } to reduce energy consumpton durng routng tme. After the producton of partcles, t s necessary to derve the ftness functon. The ftness functon s obtaned by choosng the mnmum number of potental postons wth less energy consumpton (Equaton (20)) M MnF1 = K In equaton (20), K s a potental postons and M s a potental pont. The cost of pont coverage ( τ ) should be calculated as an equaton (21). (19) (20) k f Coverage( τ ) k Coverage of Detecton and Energycos t ( τ ) = k Coverage( τ ), otherwse (21) From the combnaton of equatons (20) and (21), equaton (22) s produced to calculate energy consumpton wth respect to sensor coverage. N 1 MaxF2 = Coveragecos t ( τ ) N k (22) = 1 Now that the ftness functon was produced for coverage, the ftness functon for connectng sensor nodes should also be calculated. For ths purpose, equaton (23) s used. m f Coverage( s ) (23) m Connectoncos t ( s ) = m Connecton( s ), otherwse 106

In equaton (23), Connecton( s ) s the set of sensor nodes wthn the s range. In order to calculate the ftness functon for calculatng energy consumpton wth respect to sensor connecton, equaton (24) s used. MaxF 3 = M = 1 Connecton M N cost ( s ) (24) In ths paper, for creaton of mult-objectve ftness functon, the sum of weghts method s used. The method of sum of weghts s a classcal method for solvng mult-objectve optmzaton problems. In ths approach, weght W s multpled by any of the targets. Fnally, all of the multpled quanttes are grouped together to convert multple targets nto a scalar target functon. Ths acton generates a general ftness functon as equaton (25). Ftness = W (1 F ) + W F + W F total 1 1 2 2 3 3 (25) Optmzaton of equaton (25) leads to mproved energy aware routng n the multpath AOMDV protocol, and can be used effcently n WSN by usng the nformaton gathered from the cluster head durng energy aware routng. 4. SIMULATION In ths paper, smulaton wll carry out n the MATLAB 2017b envronment. In the proposed method, fuzzy logc s used to fuzzy the LEACH and AOMDV protocols. The fuzzy nference process ncludes membershp functons, fuzzy operators, and f-then rules. The type of fuzzy nference system used n ths paper s Mamdan. In ths method, the fuzzy membershp functons must be non-fuzzy. Ths wll ncrease the effcency of non-fuzzy. Fgure 8 shows the parameters of the fuzzy system n the MATLAB envronment. Fgure 8. Fuzzy Inference System Parameters As shown n fgure 8, the fuzzy nference system has 4 nputs, 22 rules, and 1 output. The nputs and outputs of the fuzzy system are ndcated n fgure 9. 107

A total of fuzzy rules between these nputs and the lngustc varables are 22 rules that can mprove clusterng and routng [9]. Fgure 10 llustrates the fuzzy rules between membershp functons. The fuzzy rules are based on and that the combnaton of four nput varables along wth 22 combnatonal rules between them leads to a level of probablty. In fgure 11, nputs (hops) n the x-axs and routng energy n the y-axs wth the output of the probablty n the z-axs are shown. Accordng to fgure 11, the hgh blue colored sectons n the envronment have the hghest energy consumpton durng cluster-based routng and cluster head selecton. Whatever moves upwards, the green or yellow color wll result n the best possble fuzzy output, ndcatng the best rules and the combnaton of nput varables n that secton. The probablty of selectng a node as a cluster head depends on the nputs and classfcaton. After obtanng whch node s selected as a cluster head n the network, the non-fuzzy operator wll be used. In non-fuzzy, the exact value of a fuzzy number s obtaned. Therefore, the defnte number s ntroduced as the representaton of the fuzzy number There s a varety of methods for non-fuzzy, n whch the center of gravty of the fuzzy number s used n ths paper. In other words, the pont that has the degree of belongng to maxmum s consdered as the gravty center of that fuzzy number. Fgure 9. a) Membershp functons and nput lngustc varables of the clusterng energy, b) membershp functons and nput lngustc varables of centralty, c) membershp functons and nput lngustc varables of the routng energy, d) membershp functons and nput lngustc varables of hops, e) membershp functons and output lngustc varables of probablty 108

Fgure 10. Fuzzy rule set Fgure 11. Presentaton of a level of fuzzy sets The output of the fuzzy part causes the cluster head to be selected. The WSN parameters used n ths paper are shown n Table 1. Table 1. WSN Parameters. Parameter Number of nodes n the network Network dmensons Intal energy MAC Rado Frequency Rado range Communcaton coverage Value 100 400 400m 2 0.5 Joule IEEE 802.11 433 MHz 1 m 2 100 m At frst, nodes are randomly deployed n the network envronment. The dfference between the network length and the actual poston of the sensor node n the network for random deployment, determnes the power of the nodes at start tme, as determned by equaton (26). 109

Network length The actual poston of the sensor node < 0.5 (26) Then, cluster headers are selected by fuzzy logc accordng to membershp functons and fuzzy rules. Fgure 12 shows the output of the node deployment secton, whch s used n the frst step of the 100 round for tranng and cluster heads are shown n black color n the fgure. Fgure 12. Deployment of the node n the frst round along wth the two cluster heads wth black color Typcally, the number of cluster heads can be ncreased up to 5. To obtan the dstance between the clusters n a network envronment and also the closest node to another node n a cluster, the Eucldean dstance n the m-dmensonal space s used, as shown n equaton (27). m ( ) 2 d = x x j k jk k = 1 (27) At contnuaton, the PSO algorthm wll be used whch wll be able to fnd the optmal path and can maxmze the amount of mathematcal expectaton for all states n the clusters. After fuzzy logc clusterng operatons, routng optmzaton operatons are performed to reduce energy consumpton. Fgure 13 shows the current status of routng wth the AOMDV protocol n normal mode and the selecton of clusters head wth fuzzy logc. Fgure 13. The status of routng wth the AOMDV and Fuzzy Logc methods 110

Durng the selecton of the cluster head, the AOMDV-based routng operaton s also runnng and energy consume. The network contnues to operate untl the end of ts energy consumpton. The probablty of selectng an optmal node for a cluster head s 0.5. Fnally, the deployment output and cluster heads of fgure 12 wll be as n fgure 14 wth mnor varatons n each round of tranng due to the deployment of the ntal node n the envronment as well as space change. Fgure 14. Deployment of nodes n the last round As shown n fgure 14, there are several cluster head nodes, and the nodes, due to ther overlap, are nodes that are more energy-consumng n the envronment, whch, of course, are outsde the cluster. In ths paper, three values for the sze of the packet to be transmtted n the network are defned n bts of dfferent szes, n order to determne the energy consumpton versus dstance n the WSN. The frst packet sze s 10 bts. Then the PSO algorthm s used to reduce energy consumpton n the AOMDV protocol, whch the result of usng the PSO algorthm s shown n fgure 15. In the upper part of fgure 15, the cluster heads are optmzed by the PSO algorthm, and nodes that have hgh energy consumpton are dentfed that the reducton of energy consumpton durng routng takes nto account these nodes. The lower part of fgure 15 shows the energy consumpton by usng the PSO algorthm that the PSO algorthm repeatedly reduces energy consumpton, and the optmzaton rate s appled. Clearly, as a new packet s sent, the energy consumpton of the network wll rse to a certan extent but wll not be as hgh as the ntal energy consumpton, as the paths predefned by the proposed algorthm are mostly optmal and for the new packages only just make some adjustments to the prevous path. Now that the energy consumpton s optmzed, the packets enter the network to get the result. In fgure 16(a), shows energy consumpton versus dstance wth packet sze10-bt on the network. A packet wth a length of 12 bts s shown n fgure 16(b), and a packet length of 16 bts s shown n fgure 16(c). Fgure 16 results that wth ncreasng data values from 10 to 16 bts at a gven dstance, the growth rate of energy consumpton s reduced. The reason for ths s the use of clusterng based on the proposed method, whch has an effectve result on the WSN to reduce energy consumpton. Fgure 17 shows the current status of the routng wth the AOMDV protocol at workng tme and mprovement of AOMDV wth the PSO algorthm. 111

Fgure 15. The result of PSO algorthm for optmzaton Fgure 16. a) The result of energy consumpton versus dstance of 10 bts, b) The result of energy consumpton versus dstance of 12 bts, c) The result of energy consumpton versus dstance of 16 bts 112

Fgure 17. Routng based on AOMDV and ts mprovement wth PSO algorthm 5. CONCLUSIONS Increasng the effcency of WSNs s measured and evaluated wth a seres of parameters. One of these parameters s the network lfetme that s very mportant because the nodes n the WSN have battery lmts. Hence, ncreasng the lfe of the network n terms of energy consumpton n dfferent stuatons s mportant. Another mportant parameter s the routng that consumes energy. A methodology that can make energy aware routng and ncrease network effcency s an effectve way. Therefore, provdng a method that can address these parameters can be used as an effectve method n the WSN. Ths paper uses smart methods to reduce energy consumpton durng routng. The proposed approach s that uses AOMDV routng protocol and the LEACH clusterng protocol. In the clusterng and cluster selecton method due to the uncertanty that exsts n the LEACH method, n ths paper fuzzy logc ncludng membershp functons and fuzzy rules are used. The PSO algorthm, by optmzng the AOMDV multpath routng protocol wth regard to energy consumpton, has shown that the proposed method s an effectve and effcent technque and can reduce 5% energy consumpton n WSN compared to AOMDV method. REFERENCES [1] Henzelman, W. R., Chandrakasan, A. & Balakrshnan, H., (2000) Energy-Effcent Communcaton Protocol for Wreless Mcrosensor Networks, Proceedngs of the Hawa Internatonal Conference on System Scences, 4-7 January, Mau, Hawa. [2] Houda, L. (2010) Wreless ad hoc and Sensor Networks, Vol. 6, John Wley & Sons. [3] Marna, M. K. & Das, S. R., (2001) On-demand Multpath Dstance Vector Routng for Ad Hoc Networks, Proceedng of 9th IEEE Internatonal Conference on Network Protocols, pp14-23, Rversde, 11-14 November, Calforna, USA. [4] Amne, D. A., Kamel, A. M. & Bouabdellah, K., (2014) Formal Verfcaton of a New Verson of AOMDV n ad hoc Network, Proceda Computer Scence, Vol. 37, pp160-167. [5] Dam, M. T., Nguyen, V. C., Nguyen, T. T. & Tran Le, T. D., (2014) Low-Power and Hgh- Performance Desgn for Cryptosystem Usng Power Aware and Ppelne Technques, Internatonal Conference on Advanced Technologes for Communcatons (ATC), 15-17 October, Hano, Vetnam. 113

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