Shuffled Frog Leaping Algorithm based Unequal …...Shuffled Frog Leaping Algorithm based Unequal Clustering Strategy for Wireless Sensor Networks Xunli FAN∗ and Feiefi DU School

Appl. Math. Inf. Sci.9, No. 3, 1415-1426 (2015) 1415

Applied Mathematics & Information SciencesAn International Journal

http://dx.doi.org/10.12785/amis/090336

Shuffled Frog Leaping Algorithm based UnequalClustering Strategy for Wireless Sensor NetworksXunli FAN∗ and Feiefi DU

School of Information Science & Technology, Northwest University, Xi’an 710127, P. R. China

Received: 6 Aug. 2014, Revised: 7 Nov. 2014, Accepted: 8 Nov.2014Published online: 1 May 2015

Abstract: In energy-limited WSNs, coverage and connectivity are two of the most fundamental QoS issues, which have a great impacton the performance of WSNs for minimizing the node energy consumption and maximizing the network coverage lifetime. Dueto thenode distribution, the energy consumption among nodes is more imbalanced in cluster-based WSNs. Based on this problem,this paperproposes Sink Mobility based Energy Balancing Unequal Clustering protocol (SMEBUC) for WSNs with node distribution, whichchooses the nodes with more energy as cluster heads and divides all nodes into clusters of different size through the improved ShuffledFrog Leaping Algorithm (SFLA). To reduce the cluster head replacement frequency, cluster head serves continuously to determinethe cluster head exchange time and nodes weight. The greedy algorithm is adopted to select the optimal relay node betweenclusterhead and Sink. To further reduce the energy consumption, mobile Sink routing is put forward to avoid the hot-spots. We evaluate andcompare the performance of SMEBUC with LEACH and EBUCP, and the results show that SMEBUC achieves more energy savings,and energy balance.

Keywords: Clustering routing protocol; energy balancing unequal clustering; shuffled frog leaping; target coverage; WSNs.

1 Introduction

Wireless sensor networks(WSNs) are one of the threemajor high-tech industries in the future of the worldwhich is consisted of a set of wireless sensor nodesaccording to a certain communication and topologyprotocol [1]. WSN is a comprehensive interdisciplinary ofwireless communication, sensing, microelectronics andembedded computing. WSN consists of a number ofad-hoc networks, low-power, short-lived and a number ofsensor devices that collaborate with each other toaccomplish a common task and report the collected datathrough wireless interface to a center node [2]. WSNs arewidely used in many applications such as militarysurveillance, civilian usage, industry, agriculture,healthcare, forest fire detection, wildlife conservation andother fields [3].

Due to the small size of a sensor node, lifetime,communication capabilities, processing ability andmemory are constraint of the WSNs. Therefore, a moreeffective topology control protocol to prolong thelifetime, efficient energy consumption and to improvecoverage and the payload balance is one of the key factorsin WSN design. Since sensors are often deployed in

remote or inaccessible environments where replenishingthe sensor energy is usually impossible, a critical issue ofWSN is conserving sensor energy and prolonging thenetwork lifetime while guaranteeing the coverage ofdesired areas or targets, which is called the coverageproblem [4]. The coverage concept is subject to a widerange of interpretations due to the variety of sensors andapplications. Generally, coverage which has direct effecton the network performance can be considered as themeasure of Quality of Service (QoS) in a WSN.

The increasing demand for applications in WSN hasmade the QoS an interesting and hot research topic. QoSrequirements of WSN raise the significant challenges.While providing QoS guarantee, the network protocolsneed to deal with energy constraints. With theconsideration of the properties of sensor networks such aslimited energy, dynamic topology, high network densityand large scale deployments have posed many challengesin the design, implement, and management of WSN.These challenges have demanded energy awareness androbust protocol design at all layers of the networkprotocol [5].

∗ Corresponding author e-mail:[email protected]

c© 2015 NSPNatural Sciences Publishing Cor.

http://dx.doi.org/10.12785/amis/090336

1416 X FAN, F DU: Shuffled Frog Leaping Algorithm based Unequal Clustering...

Efficient energy and network lifetime were and stillare the main design considerations for the most proposedprotocols and algorithms for sensor networks and havedominated most of the research in WSNs [6,7,8]. QoSawareness and energy conserving in different layers areimportant topic in research of WSNs, and have beenunder the focus of research community on WSNs. Relatedstudies have shown that adopting efficient routingprotocol can improve the overall performance and QoS ofWSNs [9].

In the recently research, clustering is an importantenergy-conserving method in WSN, and the goodperformance of WSN is highly dependent onenergy-efficient clustering algorithm. Significant effortshave attempted to develop the routing algorithms toextend the lifetime of WSN. Hierarchical routingconsiders data aggregation and data fusion in order toreduce the number of transmissions to the base station.These routing techniques can be classified as cluster.Through clustering and cluster head selection rules, thesehierarchical approaches spread energy usages over thewhole network to extend the operational time of WSN.

Low Energy Adaptive Clustering Hierarchy (LEACH)[10] algorithm is the first hierarchical routing protocolproposed in WSNs. LEACH is a classic uniformclustering routing protocol. Each node independentlyelects itself as a cluster head with a probability to reducethe network energy consumption. LEACH can save moreenergy than that of the plane multi-hop routing protocolsand the static network clustering. But LEACH does notconsider the location of nodes inducing a bad informaldistribution of clusters, and the remaining energy ofnodes leading to the early death of some nodes and theoverall invalidity of the network. LEACH supposes thatthe initial energy of all nodes is the same, and the energyconsumption of becoming cluster head node are the samein the first cluster head electing. So LEACH is not goodfor imbalanced-energy network. To solve this problem,researchers proposed some new algorithms likeLEACH-C, and LEACH-M to improved LEACH [11].

EEUC [12] is an energy-efficient unequal clusteringprotocol, which divides the nodes into clusters of unequalsize. The clusters closer to the base station have smallersizes than those farther away from the base station. Thuscluster heads closer to the base station can preserveenergy for the inter-cluster data forwarding.

EBUCP [13] partitioned all nodes into cluster ofunequal size and combined the unequal clusteringmechanism within the cluster multi-hop routing properlyto balance the energy dissipation among the nodes andprolong the lifetime of WSN. Clusters closer to the sinknode have smaller sizes than those farther away from thesink node. Thus cluster heads closer to the sink node canpreserve more energy for the purpose of inter-cluster dataforwarding. The distribution of sensor nodes is deployedaccording to the energy-balancing layered algorithm andtherefore the energy consumption in every layer is nearlyequal.

EADEEG [14] is a novel distributed clusteringalgorithm, which elects the cluster heads based on theratio between the average remaining energy of neighbornodes and the remaining energy of the node itself, whichcan achieve a good cluster head distribution and prolongthe network lifetime. TL-EBC [15] is a centralizedclustering protocol of two-layer hierarchy, which iscompact, energy-aware and energy consumptionbalanced. In the lower layer of the protocol, the optimalclustering of all nodes uses the Particle SwarmOptimization (PSO) algorithm. In the upper layer, thechief-cluster-head is responsible for collecting,aggregating the data of all cluster heads and sending thefused data to the base station. Sine the protocol still usethe uniform clustering approach, the overall networkperformance has not been fundamentally improved.

EEUC-based clustering algorithm [16] introduced thePSO algorithm into the network clustering process byselecting the best node as cluster head to reduce energyconsumption and prolong the network lifetime. However,the algorithm does not consider the problem of Sink nodecloser to cluster head, which may be excessive used andcause local node premature death.

[17] proposed a mobile Sink routing algorithm basedon the clustering structure. It achieved certain results onavoiding ”hot spots” through the designing of Sinkmobile route. However, this strategy caused a certainnetwork delay.

[18] proposed a clustering protocol based parameteroptimization. It adjusted the size and scale of the clustersby optimizing the relevant parameters to reduce theenergy consumption in the inter-cluster. But theoptimization of the relevant parameters on the networkcoverage and connectivity needs further study.

Shuffled Frog Leaping Algorithm(SFLA) [19] is aswarm intelligence-based heuristic technology, whichcombines the advantages of PSO and Mimetic algorithm,and has the characteristics of strong global searchcapability. There is no unified understanding on howSFLA effectively applies to the discrete optimizationproblem at present. The common method is to redefinefrog particle encoding according to the characteristics ofWSN, and introduces ”the conversion gene” and”conversion sequence” concept in the traditional localsearch mechanism correspond with the frog particleencoding mode to improve the algorithm search breadthand speed.

EAUCF [23] is a distributed competitive unequalclustering algorithm. EAUCF aims to decrease theintra-cluster work of the cluster-heads that are either closeto the base station or have low remaining battery power.EAUCF takes the advantage of fuzzy logic to calculatecompetition radius. To estimate the competition radius fortentative cluster-heads, EAUCF employs both residualenergy and distance to the base station parameters.However, the unsolved problem of considerable energyconsumption on the cluster formation still exists. Thecluster formation overhead of the clustering protocols


Appl. Math. Inf. Sci.9, No. 3, 1415-1426 (2015) /www.naturalspublishing.com/Journals.asp 1417

includes packet transmission cost of the advertisement,announcement, joining, and scheduling messages fromsensor nodes. Also, these protocols do not supportadaptive multi-level clustering, in which the clusteringlevel cannot be changed until the new configuration ismade by the network director. Therefore, the existingprotocols are not adaptable to the various nodedistributions or the various sensing area. If the sensingarea is changed by dynamic circumstances of thenetworks, the fixed-level clustering protocols may operateinefficiently in terms of energy consumption.

In this paper, Sink Mobility based Energy BalancingUnequal Clustering protocol(SMEBUC) for WSNs isproposed. It elects cluster heads based on the ratiobetween the average remaining energy of neighbor nodesand the remaining energy of the node itself, and usesuneven competition ranges to construct clusters of unevensizes. SMEBUC improves the local search mechanism ofSFLA to determine the network clustering and clusterhead replacement strategy. Through which, SMEBUCimproves the long single-chain problem of multi-hoprouting. SMEBUC focuses on the analysis and discussionof the unequal clustering algorithm, the inter-clusterrouting and Sink mobile through controlling the networkclustering process and cluster communication, whichmake the network clustering topology more rational,balance the energy consumption among cluster heads andprolong the network lifetime.

The rest of the paper is organized as follows. Section2 covers WSN architecture and energy model and SFLA.Section 3 exhibits the detail of SMEBUC Protocol. InSection 4 we describe simulation efforts and the analysisof results. Finally, Section 5 concludes the paper.

2 WSN Model and SFLA

2.1 WSN Architecture

We note that in LEACH, each node randomly decides tobecome a cluster head(CH). Once a node decides tobecome a cluster head, it aggregates the data receivedfrom various nodes inside the cluster and sends to thebase station. However, the method of completelyindependent random cluster head selection can’tguarantee the number nodes in the cluster and thedistribution of cluster head in each round.

One possible method is to select a node which hashigher remaining energy to become the CH, but it willcause the uneven energy loss for nodes in the network andform monitoring blind spot, even will influence thenetwork’s whole performance. In this research, weassume that a set of sensor nodes are unequally deployedin the square field to continuously monitor thephenomenon under inspection.

Fig.1 presents the packet transmission from node A toB on WSN. In Fig.1, node A transmits its data packet to B

and all nodes within the transmission range can overhearthe packet.

Fig. 1: Packet transmission of WSN

The nodes are distributed within the WSN monitoringarea shown in Fig.2. The sensor node perceives the data inthe monitoring range and sends fused data to the Sink nodethrough communication technology. This paper assumesthat M sensor nodes are unequally distributed within therectangle monitoring area, and GC is the geometric centerof the monitored area.

Fig. 2: WSN Architecture

According to the WSN application researchbackground, the hypotheses in the proposed algorithm areas following.

(1)Sensor node in WSN has a unique ID:nk(k∈[1,M])and its location can not be moved. Sink node’s energy andcomputing capacity is not limited, and is able to move in apredetermined position indicated by s.

(2)The sensor node has the same initial energy. Theordinary nodes are in the same layer while the cluster headis in the higher layer. The cluster head can communicatedirectly with Sink node shown in Fig.3.

(3)The sensor nodes have the same structure,capabilities of receiving and sending data. All nodes’transmission power can be adjusted, and the ordinarynode can become a cluster head node.

(4)A sensor node has the same performance such asthe initial energy, energy consumption and parameters.


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Fig. 3: the hierarchy of WSN

(5)Each node’s location information is determined.

2.2 Energy Model

In wireless transmission, attenuation of sending powerdecreased exponentially with the increasing transmissiondistance. Our energy model is different from that in [16].Here we addEDA (the energy consumption of the nodedata fusion) to energy model of [16]. Both the free space(d2 power loss) and the multi-path fading (d4 power loss)channel models are used, depending on the distancebetween the transmitter and receiver. The electronicsenergy, Eelec, depends on factors such as the digitalcoding, and modulation, whereas the amplifier energy,ε f sd2 or εmpd4, depends on the transmission distance andthe acceptable bit-error rate. Equation (1) represents theamount of energy consumed for transmitting l bits of datato d distance. Equation(2) represents the amount ofenergy consumed for receiving l bits of data [13,23].

ETX(l ,d) =

{

l(EelecEDA)+ lε f sd2,d < dmax

l(Eelec+EDA)+ lεampd4,d ≥ dmax(1)

ERX(l) = ERX−elec(l) = lEelec (2)

In which, Eelec is the energy consumption per bit inthe transmitter and receiver circuitry;ε f s is free spacemodel’s amplifier energy consumption;εmp is multipleattenuation model’s amplifier energy consumption;d0 is aconstant which relies on the application environment.

It is assumed that the sensed information is highlycorrelated, thus the cluster head can always aggregate thedata gathered from its members into a single length-fixedpacket. This assumption is impractical because thecorrelation degree of sensed data from different clusters iscomparatively low. In this work, relay nodes don’taggregate the incoming packets. We assume that a clusterhead consumesEDA(nJ/bit/signal), which is the energydissipation to perform local data aggregation and transmitthe aggregate signal to a base station [24].

2.3 Evaluation Metrics of Coverage ControlAlgorithms

How to evaluate the performance of coverage and itsalgorithm is very important for the network’s usabilityand effectiveness. The main factors are defined as follows.

Definition 1(QoS of Coverage): The QoS ofcoverage decides the completion of network tasks, reflectsthe network’s sensing ability to the physical world, and isthe basis standard of algorithm evaluating.

Definition 2(Number of active nodes):In the case ofmeeting the coverage requirements, the fewer number ofactive nodes are in a monitoring area, the larger effectivecoverage area will be with the given number of sensornodes [25]. Definition 3(Associating with the nodelocation or not): Coverage control algorithms associatedwith a node location depend on external infrastructure orsome position mechanisms, relatively cost high and needto consume large mounts of energy.

Definition 4(Energy efficiency): Coverage controlalgorithms not only require lowest energy consumption ina single monitoring task, but also maintain energy balanceof the network in a series of monitoring tasks.

Definition 5(Communications overhead): Datatransmission is the main source of sensor node energyconsumption. Coverage control algorithm with low costin the process of communication has a greater advantage.

Definition 6(Network scalability): Coverage controlalgorithm should adapt to both the scale of different WSNsand the network topology dynamically changed.

In the sensor field of a WSN, a piece of Zone Z ispossibly covered by several sensor nodes shown in Fig.4.

Fig. 4: Efficient Coverage Area

In this case, the coverage resulted from C1 amongthese nodes is redundant for Z, because the information ofZ can be sensed and acquired by other nodes. Therefore,Efficient Coverage Area(SECA) and Efficient CoverageArea Ratio(RECA) are defined as follows:

Definition 7 (Efficient Coverage Area (SECA)):Efficient Coverage AreaSECA is the coverage area that is



overlapping coverage Z’s areaSZ subtracted from NodeC1’s coverage range D(πr2), namely,

SECA= D−SZ = πr2−SZ (3)

Definition 8 (Efficient Coverage Area ratio(RECA)):Efficient Coverage Area ratioRECA is as following.

RECA=SECA

D=

D−SZ

D= 1−

SZ

D= 1−

SZ

πr2 (4)

2.4 Shuffled Frog Leaping Algorithm

In SFLA, the frog’s locationPi=(pi1, pi2,· · · , pin) is thesolution to n dimensional problem.F frogs are averagedivided into Q ethnic groups after descending inaccordance with the fitnessf(Pi). In the population, thesub-populations are constructed according to triangularmode factor probability and the frog number insub-populations isq. In the various sub-populations, thesub-population is updated in accordance with worstfitness Pw through Equation(5) to complete the localsearch.

Pnew= Pw+ r[Pb(Pg)−XW] (5)

Where, Pb is the ethnic optimal solution;Pg is theglobal optimal solution; r is random number and r∈[0,1].All frogs are re-mixed, sorted and redraw in the ethnicgroups, and completing the local search. All frogsre-mixed, sorted and redraw the ethnic groups, andcompleted the local search until the number of iterationswas reached so as to achieve the frog evolution to theglobal optimal solution.SFLA has been successfullyapplied to NP hard problem, and the existing literaturesuggests thatSFLAhas strong global search capability.

3 SMEBUC Protocol

Within the monitored region, due to the differentdistances of the nodes and Sink, the energy consumptionof communication is also different that is the greater thedistance the greater the consumption of energy. For thebalance of energy, the further of the cluster, the sizeshould be larger. And the closer to clusters, the scale issmaller. The energy consumption of the network nodes ismore balanced, which is the reason of unequal clustering.

SMEBUC uses the method of combining unequalclustering and inter-cluster multi-hop routing. Thecommunication process between cluster head and Sinknode consists of two stages which are clusterestablishment and data transmission. To further balancethe node energy, SMEBUC adopts the cluster headcompetition mechanism in the process of clustering, andSink nodes can move in the default location.

3.1 The Creation of Cluster

To avoid problem that the reincarnation clusteringmechanism consumes large amounts of energy, SMEBUChas a clustering process at network launch time. At thenetwork deployment phase, the Sink node broadcasts asignal in the network with a given transmission power.Once each sensor node receives this signal, it calculatesits approximate distance to the Sink node according to thereceived signal strength. The cluster head is the mostimportant node which does not only manage the clustermembers, coordinate the data transmission of the membernodes, but also fuses the data collected by clustermembers, and sends the processed data to the Sink node.Due to the heavy burden of cluster head, we select thenode with the higher residual energy as the cluster head atthe beginning of each data collection cycle andreconstruct cluster.

The cluster head selection rule is that the Sink nodeknows the location and energy information of all nodes inthe network, cluster classification and cluster memberdetermination is completed by Sink node because at theend of each round, the cluster members report theirremaining energy to the cluster head, and the cluster headreports the sum of residual energy of all cluster members(including itself) to Sink node. Finally the Sink nodecalculates the total energy of the entire network, andbroadcasts to all nodes. Once the cluster head isdetermined, the sensor node ni belongs to the cluster headto which is the closest.

The essence of creating a cluster is the optimizationproblem of selecting N cluster heads among M nodes. Tocomplete the cluster partition, SMEBUC adopts theimproved SFLA algorithm.

The cluster selection algorithm is described asfollowing.

Step 1) When energy query messageENERGYQUERYMSGis received from node i

if parent.equal (i) is true, thenSend (i,ENERGYACK MSG)// Report residual energy information to parent nodeelseDiscard MSGend ifStep 2) When the energy query reply message

ENERGYACK MSGis received from the node iif child.equal (i) is true, thenUpdate EnergyInfo ()// Update the residual energy of candidate nodeelseDiscard MSGend ifStep 3) When the data forwarded message

DATA FORWARDMSGis received from node ik = SelectNextRelay ()// select the node with the largest residual energy as

cluster headif parent.equal (i) is true,




Send (k,DATA FORWARDMSG(data))// Send data to cluster headelseDiscard MSGend ifSend (k,DATA FORWARDMSG(data))// Send the collected data to the relay node kStep 4) The message is not receivedk = SelectNextRelay ()// select the node with the maximum residual energy as

cluster headSend (k,DATA FORWARDMSG(data))Send the collected data to the cluster headEnd of the algorithm.

3.1.1 SFLAEncoding and Target Function

Definition 9: SFLA frog particle encoding is defined asfollowing:

Pi = (P1,n1, ...,P1,nl , ...,P1,nk, ...,P1,nM)WhereP1,nk ∈ [0,N],Pi ∩ [0,N] = /0,∀P1,nl 6= 0,∀P1,nk 6=

0.If P1,nl 6=P1,nk andP1,nk 6= 0, it means that the sensornk

at the corresponding position is the cluster head. M is thenumber of sensors and N is the number of cluster heads.

Definition 10: D(x, y) is the Euclidean distancebetween x and y.

The remaining energy of sensor i at time T is theinitial energy of sensor i minus the total energy consumedto transmit data to neighbors and the base station. Theproposed routing algorithm uses a path with energysufficiency as well as energy efficiency to pursue energybalance for the sensor network. By Reference[20], wehave an optimal formulation maximizing the minimumremaining energy of sensors. The objective is tomaximize the minimum remaining energy E of sensorswith the target function (6).

E(pi) = a1 ·A1(pi)+a2 ·A2(pi)+a3 ·A3(pi)

A1(pi) = maxj=1,2,··· ,N ∑∀nk∈Cpi , jD(ni ,CHpi , j)/N

A2(pi) = ∑Mk=1 e(nk)/∑N

j=1e(CHpi , j)

A3(pi) = ∑Nj=1D(s,CHpi , j)/N ·D(s,NC)

(6)Where, a1 + a2 + a3 = 1. A1(pi) is the maximum

average distance between the nodes and the cluster head.The smaller the value, the more compact the clustering.A2(pi) is the ratio of the energy of all nodes and theenergy of all cluster head, the smaller the value, the nodewith greater remaining energy can play as cluster head.A3(pi) is the ratio of the average distance from clusterhead to Sink and that from Sink to monitoring center. Thesmaller the value, the more cluster head nearer the regionof Sink node, so as to reduce the size of the cluster.According to the energy balance principle, the optimalsolution is to minimum the objective functionE(pi).

3.1.2 SFLALocal Search Mechanism and itsImprovement

The essence of the traditionSFLA local searchmechanism is the process that the poor individuals learnfrom the outstanding individuals [21]. If thesub-populations updatePw only through the updatePb orPg, it will reduce the diversity of frog particles and is notconducive for the individual’s global optimal evolution,which is easy to fall into local optimum. So in order toincreasing the diversity of the sample, frogPa and Pc israndomly selected and added to the partial updatemechanism. The frog particles have non-repeatability fordiscrete optimization problem of WSN. If it directly usesthe Equation(5), it will produce a large number ofinfeasible solutions and seriously reduce the algorithmefficiency. This paper prompts the concept of”transforming gene” and ”transforming sequence” whichcan improve the algorithm efficiency compared to thetraditional local search mechanism.

Definition 11 Transforming gene: Let the frogparticles’ transforming gene H(xi , x j ) converts theposition i and j in the frog particles’ coding. IfH(xi , xj )=H(xi, xi), it means that the position is not changed.

Definition 12 Transforming sequence: As for the frogparticle pi and p j , it has the transforming genemanipulation on each location according the genesequence in frog particlepi .

Eventually, it has M-1 transformations. Oncetransformingpi , it obtainsp j .

After rearranging the M-1 transforming genes, we getthe transforming sequence. Let the transforming sequencebe l i, j = (pi p j) and define the transforming betweenpiandp j is p j = p j + l i, j .

The improved local search algorithm is described thatselecting the frogpi andp j (i 6= k 6= j 6= w) randomly, thelocal search strategy is,

Pnew=

{

Pw+d′× lw, j ,Pr ≥CR

Pw+d′× l i,k+d

′× lw, j ,Pr <CR

d′∈ [dmin,dmax]

(7)

Once finishing the partial update, if the new frog’sfitness is better than that of the original frog, the newparticle will replace the original one. Conversely, itre-implements the strategy Equation (7) usinglw,g insteadof lw, j . If the result is not improved yet, it randomlygenerates a new frog particle and substitutes the originalparticle. When all ethnic groups complete the localsearch, it re-mixes the sort and enters into a new round oflocal search, until it reached the number of iterations andoutput the optimal solution.



3.2 Competition and the Rotation Mechanism ofCluster Head

Because the cluster size is smaller if the cluster head isnear to the sink node, while relatively larger if the clusterhead is away from the sink node. To compute thecompetition radius, we consider not only the distancefrom the sink node, but also the energy of the node itself.The candidate cluster head node with higher residualenergy competes for cluster head according to theircompetition radius. After completing the cluster divisionusing improvedSFLA, we can get the radius of the clusterhead competitionRCH calculated by Equation (8) [27].

RCH = [1− c∗FRAC] ·Rmax

FRAC= MAX1−−D(nk,s)MAX1−MIN1

MAX1= maxk=1,2,···,M(D(nk,s))MIN1= mink=1,2,··· ,M(D(nk,s))

(8)

Where, Rmax is the maximum value of competitionradius, is the distance from the node k to the sink node, isthe maximum distance from the nodes to the sink node, isthe minimum distance from the nodes to the sink node, isused to control value range. Through analysis, the fartherthe distance between cluster head and Sink, the greaterthe competitive radius, which proves rationality ofclustering usingSFLA.

However, if the network is too large, it may lead to thesituation of c luster head-intensive usingSFLAclusteringwhich is shown in Fig.5.

Fig. 5: Optimized seamless coverage

To solve the above problem, this paper introduces thecluster head competitive mechanism, whose principle canbe described as following.

For the cluster headsCHi andCHj , the cluster headwith larger remaining energy is selected as the new clusterhead, and those with smaller remaining energy is becomethe ordinary nodes. Finally, it completes the final cluster

division by the Sink node according to the existing clusterhead.

SMEBUC takes the mechanism of cluster head servedcontinuously, which can reduce the energy of consumingin cluster head rotationally selecting. To determine thecluster head replacement time, defining the cluster headweightCHi(v) calculated through Equation (9).

CHi(v) = ∑∀nk∈Ci

e(nk)/|Ci | ·e(CHi) (9)

Where,|Ci | is the nodes contained in the cluster, andCHi(v) is the ratio of the mean energy of nodes in thecluster and the energy of the cluster head.

If CHi(v) ≥ 1, the node with the largest remainingenergy in the cluster is chosen as the new cluster head.

Through introducing the mechanism of cluster headcompetition and rotation, SMEBUC can avoid theoccurrence of cluster head becoming too dense, whichmakes the WSN clustering topology more reasonable.And SMEBUC always selects the node with the largestremaining energy to act as the cluster head, which is moreconducive to the energy balance of the network.

3.3 Inter-Cluster Communication

SMEBUC uses the multi-hop routing communicationmethod for inter-cluster, and the cluster heads use thegreedy algorithm[22] to determine its relay nodes. Thedetail method is that SMEBUC chooses the nearestcluster head as its relay node until reaching the Sink node.Thereby SMEBUC completes the data transfer.

However, if it only takes the shortest distanceprinciple, it is possible to cause a too long single chainformed by the cluster head which is shown in Fig.6(a). Toavoid a single chain becoming too long, SMEBUC adoptsthe threshold to control the process of multi-hop routinginto the chain. Therefore, the inter-cluster communicationcan be described as following.

The cluster head uses a greedy algorithm to determinethe next relay node. Once the relay node is selected,SMEBUC calculates ratio of the distance fromcluster-head to the next relay node and that from clusterhead to Sink node which is shown in Equation(10).

T = D(CHj ,s)/D(CHi ,s) (10)

Where,CHj is the relay point of the next hop ofCHi .If , the cluster head is connected directly to the Sink node.

3.4 Sink node moving strategy

The multi-hop routing can effectively balance the networknode energy consumption. But the closer the cluster headto the Sink node, the faster the energy consuming, whichprone to the phenomenon of premature death. This is the




problem of ”hot spots”. To avoid the ”hot spot”effectively, SMEBUC adopts a Sink node position mobilestrategy which makes the Sink node mobile periodicallyin the schedule region.

The Sink moves to the node with the largest nodedegree, which has not been passed through by Sink. Afterseveral moving, it returns to its original position, and thenthe optimal mode is divided into several discrete modessolved by Newton approach. Sink node moves to the nodewith bigger value of node degree. This will reduce theenergy consumption of data transmission within the areaof the local node.

Definition 13 Node degree:Node degree refers to thenumber of edges associated with the node.

SMEBUCcan effectively avoid the excessive use ofcertain cluster head, which is shown in Fig.6.

Fig. 6: Cluster head competition and inter-clustercommunication

Suppose that the distance from the cluster headCHi tothe right side of the rectangular monitored region(assuming that Sink node is in the right side of themonitored region) isLCH. If , CHi is calledsubCHi. Wedraw a circle with the center of cluster headsubCHi andthe radius of .

The part of circle outside the monitored region, thehatched portion shown in Fig.6(a), is area that the SinkNodeSinki can be placed.

Therefore, whenSMEBUCuses the greedy algorithmto establish multi-hop routing,SMEBUC changes thecut-off point of the multi-hop routing tosubCHi . subCHipredicts the arrival time of mobile Sink node and then goto state of sleep. The ”Sleep-wakeup” mechanism caneffectively save the cluster head’s energy.

When Sink node is located in areaSinki, subCHi iswaken up, and then transfer the data to the Sink node,which completes a whole data communication process.

During a data communication process, the energyconsumed inWSNis calculated by Equation (11).

Ere = M ∗Eelec∗ lECH = Ere(CHi)+Ese(CHi)+Ero(CHi)

= l ∗Eelec∗ |Ci|+ l ∗EDA∗ |Ci|

+(l + ltx)∗Ef x∗D2(CHi ,CHj)

(11)

Where, Ere is energy consumed by the Sink nodereceiving the broadcast message during the process ofbuilding the cluster,ECHi is the energy consumed by thecluster head receiving and sending the date. ltx is thepacket length forwarded by cluster head.Ere(CHi) is theenergy consumed by the cluster head fusing the data fromcluster nodes and itself.Ese(CHi) is the energy consumedby the cluster head sending data to the other relay nodes.Ero(CHi) is the energy consumed by the cluster headtransferring the data from the other relay nodes.

4 Simulations and Analysis

4.1 Simulation Parameters

In this section, we implement theSMEBUCand evaluateits performance in NS2. In the simulation, we don’tconsider calculation, data fusion, query group transceiverand energy consumption, we just only considerate energyconsumption. We choose 100× 100m2 networksimulation, 100 nodes are unequal distributed in themonitoring area. The initial position of the sink node is(110, 50). The minimum radius is 2, and the maximumradius is 5. The remain simulation parameters are shownin Table 1.

Table 1: Simulation Parameter ofSMEBUC

Parameter ValueEelec 50nJ/bitε f s 10pJ/(bit ·m2)εmp 0.0013pJ/bit/m4

EDA 5nJ/bit/signalNetwork monitor area 100m×100mInitial node energyE0 0.5J

The node number 100

The network topology generated bySFLA andSMEBUCis shown in Fig.7.

The cluster head nodes in SMEBUC were distributedmore uniformly, because they took into account thedistance that had been constrained to optimize the clusterscheme.



Fig. 7: The performance ofSFLA

4.2 The Performance Analysis ofSMEBUC

With the energy consuming, there appears the death of thenodes in the network. The more uniform distribution ofthe death of the nodes, the more balanced energyconsuming is on the network. To analyze the energybalance capacity for SMEBUC, this paper simulates thedistribution of the network node number with deathpercentage reaching 50% which is shown in Fig.8(a). Inaddition, the size of the network sub-clustering has agreater impact on network lifetime, Fig.8(b) shows therelationship between cluster number and networklifetime.

Fig. 8: the simulation performance ofSMEBUC

From the simulation results shown inFig.8(a), thedead nodes are relatively evenly distributed within themonitored region. The simulation results illustrate thatSMEBUChas the better energy balance capacity. It alsocan be seen from Fig.8 (b) that the number of clusters istoo much or too little will affect the network lifetime.Therefore, the reasonable clustering size can maximizethe network lifetime.

For further verifying the performance ofSMEBUC,SMEBUC, LEACHand EBUCP proposed in [13]. aresimulated respectively. The simulation results are shownin Fig.9.

The simulation results in Fig.9 show that the networklifetime of SMEBUC is much longer than that of the othertwo protocols. Also the death time of the network nodesis closer and the network remaining energy is higher than

Fig. 9: Simulation for Number of Alive Nodes andRemaining Energy

that of the other two. This is due that SMEBUC adoptsthe method of centralized control for the Sink node, thenetwork clustering and the determining of the clusterhead is completed by the Sink node. SMEBUC uses theunequal clustering and multi-hop routing, and the Sinknode is mobile in a predetermined region. SMEBUC caneffectively balance network energy consumption whichmakes the balance of energy and better network lifetimeof WSN.

4.3 The Character of Cluster Head

According to section 3.2 and Equation (8), the clusternumber is decided by maximum competition radiusRmaxand control range c. Fig.10 shows the relationshipbetween the cluster head number andRmax of twodifferent control range c at 0 and 0.5. It illustrates that thesmaller the competition radius is, the greater the numberclusters head.

Fig. 10: Relationship between the number of cluster headandRmax

The cluster head number of c=0.5 is larger than that ofc=0 because the competition radius of the candidate clusterhead is smaller with the creasing of c whenRmax is fixed,and the number of cluster head increases.




4.4 Simulation of Energy Consumed by ClusterHead

Since the energy consumed by the cluster head is the mostimportant part of the energy consumption of the network,we compare the sum of energy dissipated by the clusterhead in a round bySMEBUC, EBUCPand LEACHrespectively. In the experiment, we select 10 roundsrandomly and calculate the sum energy consumed by thecluster head in each round shown in Fig.11.

Fig. 11: the Energy Consumed by Cluster Head

From Fig.11, the energy consumed by the cluster headof SMEBUC is the lowest than that of LEACH andEBUCP because the cluster head sends data to the Sinkusing multi-hop communication, and reduces the energyconsumption. The energy consumed by that of LEACH isthe highest due to it’s sending data to the Sink node bysingle hop communication, and it constructs slightly morecluster heads which increase the frequency ofcommunication with the Sink node and increase theenergy consumption.

4.5 Simulation of Different Deployment ofStrategy

Through simulation experiments, we compare therelationship between the network life cycle and nodedisability ratio of the proposedSMEBUC withnon-uniform and uniform distribution strategy shown inFig.12.

With the operation of the network, the node energycontinuously decreases while increases the node disabilityratio. The node disability ratio of both non-uniform anduniform distribution strategy increases. At round 200, thenode disability ratio of uniform distribution is about 20%,while that of non-uniform distribution is around 15%. Atround 400, this value for uniform distribution is about40%, and 35% for non-uniform distribution. With theextension of the network running time, node disability

Fig. 12: Relationship between node disability ratio andrun-time

ratio of non-uniform distribution strategy is always lowerthan that of uniform distribution strategy. It is clear thatthe performance of energy-saving of non-uniformdistribution strategy is superior to that of uniformdistribution strategy.

4.6 Simulation of Lifetime with DifferentClusters under Mobile and Static Sink

To comply with the network connectivity, coverage,energy distribution, it dynamically adjusts the nodedeployment or location with sink mobility, which fills thenetwork routing hole and senses coverage gaps. The Sinkmoves to the node with the largest node degree, which hasnot been passed through by Sink. When Sink moves to anew location, it will inherit take charge of the fixed sensornode’s task of collecting and forwarding data, so that theenergy of the fixed sensor node is preserved, and thelifetime of the entire network is extended.

To illustrate the performance of SMEBUC with Sinkmobile, we simulate SMEBUC with the cluster numberchanging from 5 to 20 and compare it with that of sinkstatic under the uniform distribution of WSN.

Table 2 shows lifetime of the mobile and static SinkSMEBUC algorithm can improve the network lifetimethan that of static Sink.

Table 2: Lifetime comparison of static and mobile Sink

Number of clustersNetwork lifetimeTnet(Round)Sink Static Sink Mobile

5 161 21810 354 41515 610 76020 435 552



5 Conclusions and Future Work

In this paper, the clustering algorithm mainly takes intoaccount reducing the total energy consumption. Thispaper improved the SFLA local search mechanism todetermine the network clustering and cluster headreplacement to improve the problem of long single-chainin multi-hop routing algorithm. Correspondingly, thepaper proposed a Sink Mobility based and EnergyBalancing Unequal Clustering protocol, which is calledSMEBUC. SMEBUC can efficiently balance the energyconsumption of the entire network, decrease the deadspeed of the nodes and prolong the network lifetime. AlsoSMEBUC uses the Sink location mobile algorithm toeffectively avoid the emergence of the ”hot spots”. Thenumerical simulation results show that SMEBUC canbalance the network energy and prolong the networklifetime efficiently.

We note that although in this paper we specificallystudy the problem of effective balance the energyconsumption and prolong the network lifetime. However,the performance of WSN in terms of end-to-endtransmission delay and packet delivery ratio may bedegraded due to node redundancy and routing path loss.The approach can be extended to other relevant costmetrics, e.g., minimizing the end-to-end transmissiondelay or maximizing network throughput. Thequantitative analysis of energy-delay with the appropriatedefinition of delay is another research topic for futurework.

Acknowledgement

This work was supported in part by Natural ScienceFoundation of China under Grant 61202393,NaturalScience Basic Research Plan in Shaanxi Province ofChina under Grant 2014KW03-02.

The author is grateful to the anonymous referee for acareful checking of the details and for helpful commentsthat improved this paper.

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Xunli FAN receivedhis MS and PhD fromNorthwestern PolytechnicalUniversity in 1996 and 2000respectively. He is currentlyan associate professorat Northwest University.His current research interestsfocus on network QoS,wireless sensor networks.

Feifei DU is MasterStudent at NorthwestUniversity. She gother Bachelor Degree ofComputer Science from Xi’anUniversity of Technology.Her current researchinterests focus on networkQoS, Network Control.


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Shuffled Frog Leaping Algorithm based Unequal …...Shuffled Frog Leaping Algorithm based Unequal Clustering Strategy for Wireless Sensor Networks Xunli FAN∗ and Feiefi DU School