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Sensors 2011, 11, 5383-5401; do:10.3390/s110505383 OPEN ACCESS sensors ISSN 1424-8220 www.mdp.com/journal/sensors Artcle FRCA: A Fuzzy Relevance-Based Cluster Head Selecton Algorthm for Wreless Moble Ad-Hoc Sensor Networks Chongdeuk Lee and Taegwon Jeong * Dvson of Electronc Engneerng, Chonbuk Natonal Unversty, Jeonbuk, Korea; E-Mal: cdlee1008@jbnu.ac.kr * Author to whom correspondence should be addressed; E-Mal: ttwjeong@yahoo.co.kr. Receved: 11 Aprl 2011; n revsed form: 4 May 2011 / Accepted: 17 May 2011 / Publshed: 18 May 2011 Abstract: Clusterng s an mportant mechansm that effcently provdes nformaton for moble nodes and mproves the processng capacty of routng, bandwdth allocaton, and resource management and sharng. Clusterng algorthms can be based on such crtera as the battery power of nodes, moblty, network sze, dstance, speed and drecton. Above all, n order to acheve good clusterng performance, overhead should be mnmzed, allowng moble nodes to jon and leave wthout perturbng the membershp of the cluster whle preservng current cluster structure as much as possble. Ths paper proposes a Fuzzy Relevance-based Cluster head selecton Algorthm (FRCA) to solve problems found n exstng wreless moble ad hoc sensor networks, such as the node dstrbuton found n dynamc propertes due to moblty and flat structures and dsturbance of the cluster formaton. The proposed mechansm uses fuzzy relevance to select the cluster head for clusterng n wreless moble ad hoc sensor networks. In the smulaton mplemented on the NS-2 smulator, the proposed FRCA s compared wth algorthms such as the Cluster-based Routng Protocol (CBRP), the Weghted-based Adaptve Clusterng Algorthm (WACA), and the Scenaro-based Clusterng Algorthm for Moble ad hoc networks (SCAM). The smulaton results showed that the proposed FRCA acheves better performance than that of the other exstng mechansms. Keywords: resource management and sharng; moble ad hoc; clusterng; fuzzy relevance; moblty; flat structure

Sensors 2011, 11 5384 1. Introducton Wreless Moble Ad hoc Sensor Networks (WMASNs) [1-3] are nfrastructureless, mult-hop, dynamc networks establshed by a collecton of moble nodes. WMASNs consst of moble sensor nodes that form the networks wthout any fxed nfrastructure or centralzed admnstraton. In these networks, each node communcates wth the other nodes mmedately or va ntermedate nodes. Ths knd of network s hghly appealng due to ts lack of nfrastructure, cost effectveness and smple nstallaton. The consderatons n these networks are to mprove the network stablty, scalablty, bandwdth utlzaton, and resource sharng and management effcency. Varous clusterng mechansms are beng appled to acheve these objectves [4-6]. Currently, clusterng mechansms are used for wreless moble ad hoc networks n varous areas, such as home networks, buldng automaton, and ubqutous applcatons. Clusterng mechansms are usually appled for large scale networks and thus nvolve hgh cost and overhead. Clusterng strongly nfluences communcaton overhead, latency, congeston, nter-cluster and ntra-cluster formaton, as well as update polcy. One of the solutons of the emergng problem s to cluster the dstrbuted nodes n the flat structure or dstrbuted network structure. The purpose of clusterng n WMASNs ncludes stablzng the network and routng, extremely sustanng bandwdth utlzaton and network effectveness, mnmzng energy consumpton, and maxmzng resource sharng and management. Therefore, an mportant pont when dealng wth clusterng s how to create the clusterhead that plays an mportant role n cluster formaton. The advantages of clusterng nclude [2,7]: Shared use of the applcaton wthn the cluster Provson for optmzaton n the routng mechansm Effcent handlng of moblty management Spatal reuse of resources Better resource sharng and management Smplfed routng schedulng Vrtual crcut support Improved bandwdth utlzaton Aggregaton of topology nformaton Mnmzaton of the amount of storage for communcaton Typcally, mechansms utlzed to overcome the overhead ssue n WMASNs consst of the cluster-based algorthm, flat-based algorthm, and locaton-based algorthm [8,9]. The cluster-based algorthm dvdes the network sze by a constant sze. Ths algorthm creates the clusters usng the dvded network. However, creatng the cluster va ths algorthm s dffcult because of the network sze and dynamc property of moble nodes. The flat-based algorthm s the routng approach based on floodng. Ths algorthm s based on routng the network addresses, whle no data-drven routng s performed. The locaton-based algorthm decdes the cluster usng locaton nformaton and resdual energy power. Ths algorthm affects the problem of determnng the lfetme of nodes n advance. Thus, f the nodes are managed n a dstrbuted manner or flat structure wthout the cluster, the clusterng performance s heavly affected by overheads.

Sensors 2011, 11 5385 Clusterng mechansms that dvde a large scale network nto several clusters are proposed to solve ths knd of problem [10,11]. One of the frst and most nfluental cluster-based algorthms s LEACH (Low-Energy Adaptve Clusterng Herarchy) [12], whch uses a dstrbuted probablstc mechansm. Dfferently, the lowest-id algorthm [9] constructs 1-hop clusters usng the neghbor table that has nformaton of the node ID, role of clusters, and lnk status (un-/b-drectonal) for nodes. Ths algorthm, however, generates too many cluster heads when the moble ad hoc network grows or moblty ncreases. Ths algorthm selects cluster heads accordng to the strength of sgnal of nodes, and thus, the dffculty n accurate measurement of sgnal strength s another weak pont of the algorthm. SCA (Secured Clusterng Algorthm) [13] s a clusterng algorthm that uses the trust value. Ths algorthm partally mtgates the cluster problems of 1-hop and 2-hop for clusterng. Another algorthm, CBLARHM (Cluster Based Locaton-Aware Routng Protocol for Large Scale Heterogeneous Moble Ad hoc Networks) [14], s based on GPS (Global Poston System). Ths mechansm s utlzed for clusterng large scale networks, but nvolves hgh cost due to the use of GPS. These algorthms have dffculty n clusterng and managng when the network sze s varable. To solve ths problem, ths paper proposes a novel Fuzzy Relevance-based Cluster head selecton Algorthm (FRCA) that effcently clusters and manages sensors usng the fuzzy nformaton of node status n the network. The proposed FRCA uses the Fuzzy Relevance Degree (FRD) wth fuzzy value [15] to perform and manage clusterng. We regard the Fuzzy Relevance Degree (FRD) wth fuzzy value as FRD. Therefore, n ths algorthm, FRD performs clusterng by choosng some nodes that act as coordnators of the clusterng. The fuzzy state vewng structure, whch s performs clusterng, conssts of 5 parameters: ID,, Level, M-hop, and Balance. The cluster head ClusterHead (CH) and cluster members ClusterMember (CM) are selected usng fuzzy value n the fuzzy state vewng structure. In the proposed algorthm, FRD s used to solve expandablty and to control the generaton of mult-hop cluster. FRD controls the number of clusters to mprove effcency. The clusterng based on FRD helps n mantanng the structure of the cluster as stable as possble, and thus mnmzng the topology changes and assocated overheads durng ClusterHead changes. We compared the proposed algorthm wth exstng methods, such as CBRP (Cluster-Based Routng Protocol) [8], WACA (Weghted-based Adaptve Clusterng Algorthm) [3], and SCAM [1] (Scenaro-based Clusterng Algorthm for Moble ad hoc networks), n terms of performance. Accordng to the smulaton result, the proposed algorthm acheves better performance than the exstng ones. The rest of the paper s organzed as follows. Related works are revewed n Secton 2. In Secton 3, detals of the proposed FRCA algorthm are presented. In Secton 4, the smulaton results of the proposed FRCA algorthm are gven and the algorthm's performances are dscussed. Fnally, n Secton 5, some conclusons are drawn. 2. Related Works Recently, several clusterng algorthms were proposed to ncrease stablty, routng performance, scalablty, bandwdth utlzaton, and resource allocaton n WMASNs. Clusterng n WMASNs plays an mportant role n enhancng ther basc network performance parameters lke routng delay, congeston, energy consumpton, and throughput. The herarchcal routng protocol n the clusterng

Sensors 2011, 11 5386 algorthm has been wdely used for WMASNs. The exstng floodng method [8-10] s the most popular herarchcal routng protocols. In ths method, the source node communcates wth the destnaton node rrespectve of the movement speed. In WMASNs, the number of control packets for floodng ncreases exponentally wth the number of nodes. A number of clusterng algorthms for WMASNs are proposed n the lterature [16]. The CBRP (Cluster-Based Routng Protocol) methods were proposed to solve the problem of exponental ncrease [8]. The CBRP (Cluster-Based Routng Protocol) methods have been wdely used to acheve effcent management and extenson of dstrbuted nodes. Well-known CBRP methods nclude LCA (Lnked Clustered Algorthm) [17], LID (Lowest-ID) [9], LCC (Least Cluster Change) [18], MCC (Maxmum Connectvty) [19], and RCC (Random Competton Clusterng) [20]. These exstng algorthms have clusterng crtera for selectng cluster heads and are based on the mnmum cluster overlap method n the formaton of clusters [21,22]. These algorthms, however, cannot guarantee stablty due to the ambguty n the selecton of cluster heads. Thus, several clusterng algorthms were proposed n WMASNs to mprove performance and reduce overhead [23,24]. Selectng the cluster head s based on the moblty of nodes n [25], and on the moblty of nodes and power capacty n [26]. In [1], a scenaro-based clusterng algorthm (SCAM) was proposed, where (k,r)-domnatng set was used for selectng the cluster heads and gateway nodes; here, k s the mnmum number of cluster heads per node n the network, and r s the maxmum number of hops between the node and the cluster head. Ths s to compute the qualty of all domnatng nodes. In [3] and [10], the clusterng algorthms based on weghtng were proposed, whch consdered lnk connectvty, power capacty and dstance of nodes, and moblty n the selecton of cluster head. These algorthms have the advantage of clear selecton of the cluster head, but they have the problem of requrng correct nformaton for the attrbutes and relatonshps of nodes. Though many clusterng algorthms are proposed, few algorthms are dedcated for wreless moble ad hoc networks. The Lowest-ID method [9], one of the most popular methods for moble ad hoc networks, has ambguty n clusterng due to selectng the cluster head wth the lowest value. In [21], AMCS (Adaptve Mult-hop Clusterng Scheme) was proposed as a wreless moble routng algorthm. The AMCS algorthm reaches the destnaton node n mult-hop through the cluster head. Ths algorthm, however, has a problem n that the role of a node s not clear, whether t s the cluster head or the gateway, durng the recepton of local nformaton from neghbor moble nodes. A centralzed clusterhead selecton algorthm was proposed n [27], where the base staton assgned the cluster head roles based on the energy level and geographcal poston of the nodes. In [28], a centralzed algorthm based on fuzzy was proposed, where the nodes were selected as cluster heads by the base staton based on ther dstances from each other, energy level, and the concentraton of nodes n the regon. In [3], a dstrbuted determnstc cluster head selecton algorthm based on WCA (Weghted Clusterng Algorthm) was proposed. WCA mantans 1-hop clusters wth one clusterhead. The weght of each node s used n the selecton of the cluster head. WCA consdered geographcal nformaton and relatve dstances of nodes for the weght nformaton. In [29], a dstrbuted cluster head selecton algorthm was proposed, where each node computes ts prorty based on ts ID, current communcaton round, energy level, and speed. In ths algorthm, the nodes wth the hghest prorty become cluster head. In [16], a Topology Adaptve Clusterng Algorthm (TACA) was proposed, where two major node parameters, lke ts moblty and battery power, were consdered for achevng

Sensors 2011, 11 5387 node sutablty and cluster head. Ths mproved the network lfe tme and reduced mantenance overhead. In [3], a weghted-based adaptve clusterng algorthm optmzed for moble hybrd networks (WACA) was proposed, where nvestgatons focused on the problem of mnmzng cluster head re-electons by consderng stablty crtera. These crtera were based on topologcal characterstcs as well as on devce parameters. Ths was to avod needless cluster head re-electons for stable clusters n moble ad hoc networks. However, the exstng algorthms dd not consder relablty, scalablty, automatc awareness among cluster heads, clusterhead canddate and cluster member, dynamc change due to moblty, and the fuzzness of cluster head formaton when the network sze ncreased n proporton to the node s number n flat structure or dstrbuted network structure. Thus, ths paper proposes a Fuzzy Relevance-based Cluster head selecton Algorthm (FRCA) to solve problems, such as energy consumpton, transmsson rate reducton, decrease n throughput, and ncorrect cluster head electon. The proposed FRCA constructs clusters more effcently by reducng the ncorrectness and ambguty n the selecton of cluster heads. 3. The Proposed Fuzzy Relevance-Based Cluster Head Selecton Algorthm Ths secton descrbes the cluster head selecton algorthm based on fuzzy relevance. The effcent formaton of clusters plays an mportant role n the processng rate, performance mprovement, and network stablty. 3.1. Basc Clusterng Concept Clusterng n WMASNs can be consdered as the vrtual parttonng of dynamc nodes n the flat structure or dstrbuted network structure nto several clusters [30]. Clusters of the nodes n the flat structure or dstrbuted network structure are made wth respect to ther nearness to each other. Such nodes are consdered neghbors when all neghborng nodes are located wthn ther transmsson range and set up a bdrectonal lnk between them. Typcal algorthms for clusterng n the flat structure or dstrbuted network structure are known as one-hop clusterng and mult-hop (d-hop) clusterng algorthms [30]. In the one-hop clusterng, every member node s at most 1-hop dstance away from a central node that s called the cluster head. Thus, all member nodes reman at most two hops dstance away from each other wthn a cluster category. On the contrary, n mult-hop clusterng [21,30], the management of neghborng nodes to the cluster head s performed by allowng the nodes to be presented at most d-hop dstance away from each other to form a cluster. A typcal WMASN structure conssts of flat and herarchcal structures as shown n Fgure 1(a,b). The small crcle n the fgure represents the nodes n WMASNs. The lnes jonng the crcles denote connectvty among the nodes. Every node s dentfed wth an ID number (.e., 1 14) along wth a number wthn parenthess. The numbers n the parenthess are the weghts of the nodes. These weghts are measured wth respect to varous node parameters and apply the selecton of clusterheads. Every node n the flat structure shares equal responsblty to act as a router to route the packets to every other node. However, to acheve better routng effcency, ths structure requres an amount of message floodng. Occasonally, such message floodng has the mert of reducng overhead of the MAC layer. On the other hand, nodes n the herarchcal structure are assgned wth dfferent functonaltes whle actng as a clusterhead, gateway, or a cluster member as shown n Fgure 1(b). The clusterhead n the

Sensors 2011, 11 5388 herarchcal structure plays an mportant role n nter-cluster and ntra-cluster communcaton. Thus, the clusterhead works as the local coordnator for ts member nodes and manages the cluster members. A gateway node s a node that connects the brdge between the nter-cluster and ntra-cluster communcaton. A gateway works as the common or dstrbuted access pont for two cluster heads. Both of the dstrbuted gateways provde the path for nter-cluster communcaton. The ordnary nodes of the cluster are the mmedate neghbors of the cluster heads. They have the capablty of servng as ether a head or a gateway whenever selected to do so. Fgure 1. Flat structure and Herarchcal structure. (a) Flat structure. (b) Herarchcal structure. (a) (b) 3.2. FSV Structure for Clusterng FSV (Fuzzy State Vewng) structure clusters adaptvely and s effcent when the sze of networks vares accordng to the moblty of nodes. In the FSV structure, a node transmts not only packets but the fuzzy value [11] to neghbor nodes. The determned fuzzy value s used to prevent nterferences and attacks from other nodes. A cluster s composed of a CH, CH canddate, gateway, and CMs, where CH s Cluster Head and CM s Cluster Member. Cluster nodes, classfed as CH, CM, gateway node, and CH canddate accordng to ther roles, broadcast packets shown n Fgure 2 to neghbor nodes. Fgure 2. Packet structure of FSV. ID Level M-Hop Balance The parameters of the packet are explaned as follows: Identfer (ID): ID s assgned for dentfyng each node and used to avod nterference and attacks from other nodes durng the selecton of cluster head. Fuzzy Relevance Degree ( ): Fuzzy Relevance Degree (FRD) s a fuzzy value (0 1), determned by avalable power, dstance, and moblty. To reduce the computatonal complexty, we set a fuzzy value between 0 and 1,.e., rangng n {0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}. FRD s used for selectng the cluster head and constructon of clusters. Level: Each node has a level assgned accordng to the FRD of each node. Three levels are proposed: low level (Level-0) wth 0.4, mddle level (Level-1) wth 0.5 0.7, and hgh level

Sensors 2011, 11 5389 (Level-2) wth 0.8. The assgned levels are used n the selecton of CH, CM node, and CH canddate nodes, and they are also are used to avod the complexty of cluster management. M-hop (Mult-hop): M-hops controls the management and generaton of the 1-hop cluster and 2-hop cluster accordng to FRD. In large scale networks, the 1-hop cluster and 2-hop cluster generate too many cluster heads. Thus, the M-hops Adjustment adjusts the sze of clusters accordng to the network sze. Balance: Each cluster head s selected accordng to FRD ( ). The balance parameter s used to balance the number of nodes n clusters for achevng far management of the attached cluster members (CM). 3.3. Cluster Head Selecton Effcent selecton of the cluster head (CH) has a bg nfluence on the cluster structure. Ths paper proposes the use of FRD to select the CH that s dfferent from exstng mechansms such as CBRP [9], WACA [3], SCAM [1], and SCA (Secured Clusterng Algorthm) [13]. The selecton of the cluster head s complex and naccurate n CBRP [9] based on Lower ID, MOBIC [25] based on moblty, and SCA (Secured Clusterng Algorthm) [13] based on trust value. Exstng mechansms select the clusterhead usng only one of the followng parameters: ID, moblty, and trust value. The proposed method, however, uses parameters jontly to select the cluster head, and the cluster head s selected by FRD and determned by the avalable power, sgnal strength, and dstance between the nodes, whch s presented as follows. 3.3.1. Fuzzy Relevance Degree Fuzzy Relevance Degree (FRD) of a node represents the degree of relablty provded by neghbor nodes n the network. The FRCA system proposed n ths paper selects the cluster head based on the fuzzy relevance, avalable power of nodes, moblty, and the dstance between nodes. The avalable power of nodes, dstance between nodes, and moblty of nodes are consdered to mantan the balance of energy consumpton of the nodes. The dstance between nodes and moblty s consdered to keep the balance between clusters. The FRCA performs clusterng based on parameters descrbed above and selects the cluster head for effcent clusterng. For n nodes of N={x 1, x 2,..., x n }, the fuzzy set, ( x ), s defned by the followng Equaton (1): ( x )={ ( x1 ), ( x2 ),..., ( xn )}, ( 1 1 ) (1) Here, x s a member node for clusterng n the network, and ( x ) s a membershp functon. Then, the fuzzy relevance degree for node x, FRD ( x ), s defned by the followng Equaton (2): FRD ( x ) = E ( t) ( x ). n (2) E ( t) where E (t) s the energy of node x at tme t gven by the sum of the avalable power of neghbor nodes for node x. For example, assume that nodes x 2, x4, and x5 are neghbor nodes of x 10, and j 1 j

Sensors 2011, 11 5390 p, and p5 are the avalable powers of neghbor nodes x 2, x4, and x5, respectvely. Then, we get E = p2 p4 p5. 2 p4, ( t 10 ) 3.3.2. Avalable Power The avalable power s the avalable energy capacty. In ths paper, we consder the energy power level of each node whle calculatng the avalable power, n order to ncrease the network lfetme. Whenever a node forwards a packet, t loses some amount of energy whose amount depends on factors such as the nature of packets, ther sze, access frequency, and the dstance between the nodes. An avalable power functon consders all these factors and decdes whch one, among all the dscovered paths, should be selected for an energy-effcent transmsson. We have consdered ndvdual energy power n consderng the path, that s, f there s a path wth a node havng very low energy level, then the avalable power functon does not select that path, rrespectve of whether or not that path s tme effcent. The avalable power for node x, AP ( x ), depends on the number of nodes for the cluster. The larger AP ( x ) means the more stable power and the more energy power. Thus, the node wth large AP ( x ) s hghly lkely to be selected as a cluster head and able to support the network lfetme for a long tme. Therefore, we consder the avalable power functon to ncrease the network lfetme, and t s defned by Equaton (3): AP ( x ) = where n s the number of nodes n the cluster, and 3.3.3. Sgnal Strength x j x Cluster j n P P x s the avalable energy power of the node x j j. We denote RS(x ) as the receved sgnal strength of node x Typcally, the sgnal strength between the sender and recever depends on the physcal dstance between the nodes, and t s shown as d x, x j [11], where d x, x s the dstance between the cluster and the member node j. However, n the j real ad hoc network, the measured sgnal strength s ambguous and naccurate due to the dynamc moblty. Ths ambguty and naccuracy have a negatve effect on the selecton of cluster head. Here, the sgnal strength based on FRD s ntroduced to solve the problems ssued from the ambguty and naccuracy n the sgnal strength of member node j wth respect to the cluster. FRD ( x ) represents the relevance degree of the sgnal strength from the cluster to member nodes and obtans the relevance nformaton accordng to the sgnal strength between the cluster and member node. The receved sgnal strength functon for node x, RS ( x ), s to measure the sgnal strength rato of the cluster head and member nodes, whch s defned by Equaton (4): RS ( x ) = 10log 10 FRD( x j x j { neghbornodesof x } FRD( clusterhead of ) x ) (3) (4)

Sensors 2011, 11 5391 3.3.4. Dstance The dstance between the cluster head and member node j, d x, x j, s determned by the number of hops for the shortest path. Thus, the cost for dstances of nodes n the cluster s an mportant factor. The dstance cost between nodes s measured from the cluster head to member nodes. Here, the dstance for the clusterhead x s defned by the Equaton (5): 3.3.5. Jon d ( x ) = x j { cluster members of x } d (5) We measure CH based on the avalable power, sgnal strength, and dstance mentoned above. Consderng the avalable power, sgnal strength, and dstance, the jont metrc s defned by Equaton (6): x, x j Cost ( x ) = AP( x ) RS( x ) d( x ) (6) We calculate Cost ( x ) for all potental cluster heads, and we then select the cluster head wth the mnmum Cost x ). ( Fgure 3. Flowchart for cluster head selecton.

Sensors 2011, 11 5392 Frst, a node wth more energy power and stronger sgnal has more probablty to be the cluster head n a cluster. Thus, the node wth the mnmum cost becomes the cluter head canddate. Second, a non-cluster head node wth hgher energy power than those of neghbor nodes may become a cluster head canddate. The selected cluster head canddate has to notfy ts neghbor nodes of cluster head canddate selecton (NOTICE_CH_CANDIDATE). Thrd, cluster members that are not the cluster head broadcast jon request message (REQ_JOIN) to the nearest cluster head. If a node s not the cluster head canddate (NOT_CH_CANDIDATE), then the node forwards to neghbor nodes that the node s a cluster member. The whole process s shown n Fgure 3, and the correspondng cluster head selecton algorthm (Algorthm 1) s gven as follows. Input: Nodes nformaton n a Node Cluster Output: CH Node begn broadcast E n Cluster Radus receve E j n Cluster Radus E FRD ( x ) = ( t) ( x ) n E ( t) j 1 j Algorthm 1. Cluster head Selecton. If (FRD( x )==max(frd( x j ) j=1,2,,n)) then begn broadcast NOTICE_CH_CANDIDATE() n Cluster Radus receve REQ_JOIN(,j) n Cluster Radus Cluster()=Cluster() {j} calculate the avalable power calculate the receved sgnal strength calculate the dstance for the cluster heads search mn Cost x ) = AP x ) RS( x ) d( x ) ( ( f (!=j) then begn send NOT_CH_CANDIDATE end else CH_CANDIDATE=FALSE; receve NOTICE_CH_CANDIDATE(j) n Cluster Radus CH()=CH() {j} f (CH()!=Ø) then begn broadcast REQ_JOIN(,j) else CH_CANDIDATE=TRUE; end end end

Sensors 2011, 11 5393 3.4. Cluster Formaton After selectng CH by FRD, each cluster structure performs clusterng for neghbor nodes. If a node needs clusterng, then t checks the state of self-node frst and checks the number of nodes of each cluster. Clusterng s determned after checkng the number of nodes by broadcastng the FSV packets. Let s assume the cluster structure shown n Fgure 4. Fgure 4. Orgnal Cluster Structure. Fgure 5 shows the cluster structure after the clusterng of the structure n Fgure 4. Each cluster of C1, C2, and C3 has a structure wth a CH, gateway, and CM nodes. Clusterng s performed for C2 and C3 to balance wth C1. Ths clusterng s very mportant n the proposed mechansm. The clusterng of C1 and C2 or that of C1 and C3 results mbalance. After clusterng, the clusterng nformaton s stored as shown n Tables 1 and 2 for achevng stable management and performance mprovement of clusters. Fgure 5. Modfed Cluster Structure.

Sensors 2011, 11 5394 Table 1. Informaton for C1. Node State ( ) CH1 CH 0.9 G1 Gateway G2 Gateway G3 Gateway O1 CM 0.5 O2 CM 0.6 O3 canddate 0.8 O4 CM 0.3 O5 CM 0.2 Table 2. Informaton for C2. Node State ( ) CH21 canddate 0.8 CH31 CH 0.9 G21 Gateway G31 Gateway G32 Gateway O21 CM 0.5 O22 CM 0.3 O23 CM 0.6 O31 CM 0.5 O32 CM 0.3 After clusterng, the exstng cluster structure of Fgure 4 s modfed as shown n Fgure 5, and the CH s to be changed. As shown n Fgure 5, the nodes CH31 and CH21 become the new CH and the CH canddate, respectvely. 4. Smulaton Results The paper used the NS-2 smulator [31] for the smulaton to show the performance of the proposed method. In the smulaton, the parameter values are selected at random and shown n Table 3. The parameters are network sze, number of nodes, max speed, pause tme,, packet sze, transmsson area, hello packet nterval, and smulaton tme. The proposed method s compared wth CBRP [9], WACA [3] and SCAM [1] for performance evaluaton. Table 3. Smulaton parameters. Parameters Value Network Sze 700 700 Number of Nodes 450 Speed 3 30 m/s Pause Tme 0 s 0.5 0.9 Packet Sze 100 byte Transmsson Range 20 200 m Smulaton Tme 420 s Hello Packet Interval 3 s MAC Protocol IEEE 802.11

Sensors 2011, 11 5395 In the clusterng mechansm, the generaton of optmal number of clusters s very mportant to reduce the overhead and mprove performance. Thus, the followng fve scenaros are consdered to know the performance of the modfed clusters. Smulaton Scenaro 1: The smulaton s performed to evaluate the performance wth the varyng number of cluster heads. In the smulaton, the number of nodes s 80, 160, 240, 320, and 380. Smulaton Scenaro 2: Ths scenaro s to estmate the overhead accordng to fuzzy relevance degree. The smulaton s performed for of 0.5, 0.6, 0.7, 0.8, and 0.9. Smulaton Scenaro 3: Ths scenaro s for generatng the cluster head accordng to the fuzzy relevance degree. The smulaton s performed for of 0.5, 0.6, 0.7, 0.8, and 0.9. Smulaton Scenaro 4: Ths scenaro s for testng CHER (ClusterHead Electon Rato). CHER depends on the network sze. The smulaton s performed for network szes of 350, 400, 450, 500, 550, 600, 650, and 700. Smulaton Scenaro 5: Ths scenaro s for the number of clusters wth transmsson range 200 m. The transmsson range vares between 10 and 90 wth a fxed step of 10. We were set to = 0.8 and = 0.9. Fgure 6 shows the smulaton result for comparng CBRP, WACA, SCAM, and the proposed FRCA when the number of nodes s ncreased from 80 to 380. The smulaton result shows that the proposed method has almost the same number of cluster heads as that of the other methods when the number of nodes s 80. As the number of nodes s ncreased, however, the proposed FRCA generates less cluster heads than the other methods. Ths means the proposed FRCA mantans the network performance effcently by restrctng the number of cluster heads. Fgure 6. Number of clusterheads wth the number of nodes N = 450 and = 0.9. Fgure 7 shows the smulaton result by Scenaro 2 for the relaton between overhead ratng and FRD. The overhead ratng of the proposed FRCA s smlar to those of other methods when FRD( ) s 0.5. Ths resulted from the fact that nodes are rated as CM when 0.7. The overhead ratng s very low when = 0.9. In the smulaton of the proposed method, there are only two overhead

Sensors 2011, 11 5396 packets durng the transfer of 220 packets when = 0.9. Thus, the use of FRD mproved the throughput and performance and mantans clusters stablty. Fgure 7. Overhead Rate wth 0.5 0.9. Fgure 8 s the smulaton result for Scenaro 3 and shows the relaton between the number of cluster heads and FRD( ). As shown n the fgure, our method generated more cluster heads than the other methods when = 0.5. The reason for ths s that our method generates cluster heads assumng 0.8. Therefore, the proposed FRCA generates the optmum number of cluster heads when 0.8. Too many cluster heads n clusterng results dffcultes n the management of clusters. In ths paper, we assumed that a cluster head manages optmally about 100 nodes accordng to our experence. The smulaton generated 4 clusters. The processng rate may be mproved by adjustng the number of nodes n a cluster. Fgure 8. Number of Clusterheads wth 0.5 0.9.

Sensors 2011, 11 5397 In Scenaro 4, we showed the performance of the proposed FRCA by varyng network szes. To acheve ths, we vary the network sze by 350, 400, 450, 500, 550, 600, 650, and 700. The smulaton result s shown n Fgure 9. As shown n Fgure 9, the proposed FRCA acheves better CHER than SCAM that s known for ts good performance. Better CHER of the proposed FRCA s due to the classfcaton of nodes as the CH node, CH canddate node, or CH member nodes. Thus, the performance of the proposed FRCA does not degrade wth the ncrease of network sze. CHER s nfluenced by nodes wth < 0.8 that means low sgnal ntensty and low battery power. Therefore, the smulaton s performed wth FRD 0.9. Fgure 9. Clusterhead Selecton Rato wth Network Sze 700 and = 0.9. In Scenaro 5, we smulated the number of clusters by varyng transmsson ranges. To acheve ths, we vared the transmsson range between 10 and 90, and we vared the number of nodes N by 100, 200, 300, 400, and 500. The smulaton results are shown n Fgures 10 and 11. Fgure 10. Transmsson Rate vs. Number of Clusters wth = 0.9.

Sensors 2011, 11 5398 Fgure 11. Number of Clusters n CBRP, WACA, SCAM, and FRCA wth N = 450 and = 0.9. Fgure 10 shows the average number of clusters s relatvely hgh when the transmsson range s small. On the other hand, when the transmsson range ncreases, the number of clusters created decreases. A smaller backbone reduces the routng overhead. Therefore, the transmsson power of a node n a heterogeneous envronment depends on the qualty of domnatng nodes. Fgure 11 shows the smulaton result wth the number of nodes N = 400 and = 0.9. The proposed FRCA creates fewer clusters compared wth those of CBRP, WACA, and SCAM. Ths s because the proposed FRCA apples FRD( ) and results n form fewer clusters. But f FRD( ) decreases more and more, then the cluster number and sze decrease n proporton to FRD( ), whch affects the performance. Therefore, FRD( ) s mportant to select the cluster head. Thus, the proposed FRCA selects the cluster head stably by flterng out nodes wth low sgnal ntensty and low battery power usng the proper FRD( ). 5. Conclusons Durng the set up of routng n a wreless ad hoc network wth moble nodes, clusterng s an mportant mechansm to buld a stable network structure and to reduce the overhead and the table sze. In case of large scale flat structure network envronment, the overhead s due to the ncrease of management cost, the decrease n routng performance, the early consumpton of battery energy, and the ncrease n the complexty of head selecton. Ths paper proposed a method, FRCA, to reduce the overhead. The proposed method used FRD for effcent selecton of the CH and FSV for effcent clusterng n the network. The proposed FSV s used to classfy nodes under clusterng as the CH node, the CH canddate, a gateway node, and CM nodes. For the effcent selecton of the CH, exstng methods used sngle measured parameter whle the proposed method consdered parameters such as FRD( ), AP ( x ), RS ( x ), and d ( x ). The consderaton of varous parameters n the selecton of CH node reduced the overhead due to the flat structure by easy resources management and bandwdth allocaton, effcent management of

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Sensors 2011, 11 5401 31. NS-2 smulator. Avalable onlne: http://www.s.edu/nanam/ns (17 May 2011). 2011 by the authors; lcensee MDPI, Basel, Swtzerland. Ths artcle s an open access artcle dstrbuted under the terms and condtons of the Creatve Commons Attrbuton lcense (http://creatvecommons.org/lcenses/by/3.0/).