Research Article An Efficient Cluster Head Selection...

Research ArticleAn Efficient Cluster Head Selection Approach for CollaborativeData Processing in Wireless Sensor Networks

Yan Qiang1 Bo Pei1 Wei Wei2 and Yue Li1

1College of Computer Science and Technology Taiyuan University of Technology Taiyuan 030024 China2School of Computer Science and Engineering Xirsquoan University of Technology Xirsquoan 710048 China

Correspondence should be addressed to Yan Qiang qiangsel163com

Received 1 June 2014 Revised 18 July 2014 Accepted 22 July 2014

Academic Editor Rongbo Zhu

Copyright copy 2015 Yan Qiang et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Since wireless sensor networks (WSNs) consist of nodes with limited battery power collaborative data processing and balancedenergy consumption should be considered as the key issue This paper proposes an efficient cluster head selection approach forcollaborative data processing in WSNs The proposed algorithm designs an effective energy-efficient model to select the optimalcluster heads among all nodes fairly which helps to reduce the impact of the monitoring scheme on the lifetime of networkExperimental results show that the proposed protocol is able to reduce energy consumption and obtain higher efficiency as well aseffectively prolonging the lifetime of network more than a few existing cluster-based routing protocols

1 Introduction

In recent years a considerable attention and research havebeen devoted to the deployment of sensors for distributemanagement collaborative information processing andmul-tihop communication [1] Wireless sensor networks integratethe most advanced technology such as sensor technologyembedded computing technology wireless communicationsand distributed information processingThrough a variety ofmicrosensors monitoring and collecting the environmentalinformation collaboratively the data that the target cus-tomer required can be transmitted to the embedded systemof information in time [2] Therefore collaborative dataprocessing and balanced energy consumption should beconsidered as the key issue in WSNs However collaborativedata processing has received little attention until recently

WSNs are formed by a large number of sensor nodeswhich deployed in a monitored area and each node forms amultihop self-organizing network [3] Depending on the sen-sors distributed spatially and working cooperatively we cangather and process data from the environment (eg mechan-ical thermal biological chemical and optical readings) [4]Since the sensors with limited battery power cannot beadded balanced energy consumption should be considered

as the crucial issue for energy-efficient management [5] Inrecent years the monitoring of the residual energy level isknown as the hot issues by the majority of scholars

Existing cluster-based routing protocols only considerbalanced energy consumption of nodes while ignoringcollaborative data processing between nodes The goal ofthis paper is to achieve collaborative data processing inWSNs thus prolonging the lifetime of network Thereforewe propose an efficient cluster head selection approach forcollaborative data processing (CHSCDP) in WSNs whichmeasure the residual energy of all candidates for the furtherclassification in the competition Firstly our approach electscluster heads with more residual energy through local radiocommunication to achieve even distribution of cluster headsSecondly the normal nodes situated in the area managed byseveral cluster heads should select the optimal cluster headthat have themore residual energyThirdly in order to reducethe energy consumption of the collaborative communica-tion interference and transmission latency we adopt OVSF(orthogonal variable spreading factor) rather than TDMA(time division multiple access) encoding in the intraclustercommunication In addition since the multihop routing ismore efficient than single-hop manner in terms of reducingenergy consumption we use the method of forwarding data

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2015 Article ID 794518 9 pageshttpdxdoiorg1011552015794518

2 International Journal of Distributed Sensor Networks

from the cluster heads to the nearer nodes that are far fromthe base station

Due to collecting data in its cluster and forwardingfrom other clusters the cluster heads near the base stationwill consume more energy and die in advance which willinfluence the life cycle of the whole network [6] In this paperan unequal clustering method is proposed to balance theenergy consumption among the cluster heads The proposedalgorithm offers a framework for collaborative data process-ing among researchers By adjusting competition radius ofdifferent candidate cluster heads the cluster size near the basestation is smaller comparatively in order to compensate forunbalanced communication overhead caused by interclustercommunications

The specific contributions of this paper include thefollowing

(i) A literature survey about various existing cluster-based routing protocols and an analysis of theiradvantages and disadvantages are presented

(ii) An effective energy-efficient optimization model forsolving the unequal clustering in WSNs is proposed

(iii) Theproposed protocol adopts the idea of energy grad-ing to select the cluster heads and the competitionprocess can obtain better convergence and cost lowermessage overhead

The rest of this paper is organized as follows a briefsurvey is given in Section 2 We study the CHSCDP protocoland formalize it in Section 3 Experimental results andcomparisonswith existing cluster-based routing protocols arepresented in Section 4 Finally Section 5 concludes the paperand discusses some future research directions

2 Related Works

The collaborative data processing has attracted much atten-tion of many domestic and international researchers inWSNs In this section we focus our discussion on the relatedworks on collaborative data processing and energy optimiza-tion in WSNs

In order to achieve high-energy efficiency and increasethe network scalability the nodes can be organized intoclusters and apply random selection method for cluster headelection in an epoch [7] After collecting all the data of itsmembers cluster head transfers them to the base stationDuring the phase of clustering the cluster head plays animportant role in providing data communication to nodesand the base station efficiently This method can increasethe energy consumption of the sensor network and acquirenetwork scalability

Low-energy adaptive clustering hierarchy (LEACH) is atypical clustering-based protocol [8] In order to save energyand enhance network lifetime the hierarchical routing isadopted Younis and Fahmy [9] proposed hybrid energy-efficient distributed clustering (HEED) which periodicallyselects cluster heads according to a hybrid of the noderesidual energy and a secondary parameter such as nodeproximity to its neighbors or node degree Hsin and Liu [10]

proposed a new technique to select the cluster heads inevery round which depends both on current state probabilityand on general probability Kar and Banerjee [11] proposeda distributive energy-efficient adaptive clustering (DEEAC)protocol which is having spatiotemporal variations in datareporting rates across different regions DEEAC selects anode to be a cluster head depending upon its hotness valueand residual energy Dr Li et al proposed a dynamic stochas-tic distributed energy-efficient clustering method where thecluster head election probability is more efficient [12] More-over it uses a stochastic scheme detection to extend thenetwork lifetime

Sim et al [13] proposed an energy-efficient cluster headerselection algorithm (ECS) which selects cluster head byutilizing only its information to extend network lifetimeand minimize additional overheads in energy limited sensornetworks M C M Thein and T Thein [14] proposed amodification of the LEACHrsquos stochastic cluster head selectionalgorithm by considering the additional parameters theresidual energy of a node relative to the residual energy ofthe network for adapting clusters and rotating cluster headpositions to evenly distribute the energy load among all thenodes Chen et al [15] proposed an energy consumptionmode of clustering protocol in the condition that nodesfollowed the Poisson distribution and analyzed the networkperformance which was impacted by Poisson distributiondensity of the nodes

Attea and Khalil proposed a new evolutionary basedrouting protocol for clustered heterogeneous WSNs [16]Ching et al proposed a novel hierarchical routing pro-tocol algorithm (NHRPA) for WSNs [17] Joe-Air et alpresented a QoS-guaranteed coverage precedence routingalgorithm [18] Jiang et al [19] proposed a distributed energy-balanced unequal clustering (DEBUC) routing protocol andan unequal clustering mechanism is adopted for interclustermultihop way

3 Collaborative Data Processingand CHSCDP Protocol

The cluster head will be selected randomly in a cyclicmanner in LEACH protocol and the energy of the wholenetwork load is evenly distributed to each node It can reducenetwork energy consumption and improve overall survivaltime of the network LEACH protocol has many advantagessuch as hierarchical structure local dynamic data fusionand cluster head selection [20] Although LEACH protocolcan effectively prevent data loss caused by redundant datatransmission there are also obvious shortcomings such asdata redundancy energy imbalance and poor stability [21]Therefore this paper proposes an improved cluster headselection based on LEACH which focuses on the residualenergy of the candidate nodes and the problem of energyconsumption for intercluster communication

In order to design an efficient clustering algorithm weneed a comprehensive analysis of the performance indicatorsas follows

Firstly energy efficiency means avoiding the end ofthe life cycle of the network caused by the early death of

International Journal of Distributed Sensor Networks 3

some nodes as well as reducing the energy consumption offormation and maintenance of clusters

Secondly the stability of cluster structure can reduce theadditional overhead caused by frequent clustering

Thirdly the cluster heads are always in the center ofthe cluster and have more powerful radios to be able tocommunicate with all adjacent cluster heads Then theproblem of the appropriate size of a cluster should be notifiedin the multihop data transmission

TheCHSCDPprotocol is divided into several stages in theapplication process including radio channel and energy dis-sipation cluster head election collaborative communicationand routing The implementation of CHSCDP protocol willbe described in detail in the following section

31 Radio Channel and Energy Dissipation We assume aneffective energy-efficient model for the radio channel andenergy dissipation the transmitter dissipates energy to runthe radio electronics and the power amplifier and the receiverdissipates energy to run the radio electronics The effectiveradio energy dissipation model is shown in Figure 1

In the effective radio energy dissipation model both thefree space (1198892 power loss) and themultipath fading (1198894 powerloss) channel models were used depending on the distancebetween the transmitter and the receiver If the distanceis less than a threshold 119889

0 the free space model is used

otherwise themultipathmodel is usedTherefore the energyconsumption for transmitting a 119896 bitsmessage over a distance119889 can be formulated as

119864Tx = 119896 times (119864elec + 119864fs times 119889

2) 119889 lt 119889

0

119896 times (119864elec + 119864amp times 1198894) 119889 ge 119889

0

(1)

where 119864fs is power consumption of the free space propaga-tion 119864amp is power consumption of multipath propagation119864elec represents the residual energy of networks and 119889

0=

radic119864fs119864ampTo receive 119896 bits of the information the radio will expend

as

119864Rx = 119896 times 119864elec (2)

32 Cluster Head Selection In beginning of each round forselecting cluster head the base station will collect the infor-mation of residual energy of all nodes accurately Accordingto the statistics the minimum energy 119864min and maximumenergy 119864max can be obtained and the energy level of node canbe divided into four categories by the threshold respectively

Level (119894) =

119864res isin (119864avg + 119864max

2 119864max] 119894 = 1

119864res isin (119864avg119864avg + 119864max

2] 119894 = 2

119864res isin (119864avg + 119864min

2 119864avg] 119894 = 3

119864res isin (119864min119864avg + 119864min

2] 119894 = 4

(3)

where 119864avg = (119864max + 119864min)2

Transmissionelectronics

Tx Receiveelectronics

k bitpacket

k bitpacket

d

ETx(k d) ETx(k)

Eelec lowast kEelec lowast k lowast k lowast dn120576elec

amplifier

Figure 1 The effective radio energy dissipation model

The probability 119875CH that a node is elected as cluster headis defined as follows

119875CH = max(120582 times1

Level (119894)times

119864res1198640

119875min) (4)

where 119864res is the node residual energy 1198640 is the node initialenergy and 120582 is a parameter of energy attenuation

In order to improve the convergence of the election ofcluster head we set119875min as a threshold which is theminimumprobability and is given by

119875min =

119875

1 minus 119875 times (119903 mod(1119875)) 119862119894(119905) = 1

0 119862119894(119905) = 0

(5)

where119875 is a constant and119862119894(119905)denoteswhether the node 119894has

been a cluster head in the most recent 119903 mod(1119875) rounds Ifthe node has been a cluster head 119862

119894(119905) = 0

In the phase of cluster formation the nodes will processas follows according to their own value 119875CH

(i) If 119875CH ge 1 the node will broadcast the messageof being candidate cluster head to its neighbors andwaiting for JOIN message As to the normal nodesthey may receive a few messages from several clusterheads and determine whether to join in which cancomprehensively depend on the indicators such asstability appropriate number of cluster heads andintracluster communication overhead

(ii) If 0 le 119875CH lt 1 and the normal nodes do not receivemessages from any other cluster head the value 119875CHwill be multiplied by itself and step into the nextiteration If the node receives a message sent by acluster head it will run into a plurality of candidateselection process

The nodes with higher residual energy should have moreadvantages than other nodes in the cluster head electionprocess As far as the nodes with the approximate residualenergy they may belong to the same level Therefore someother factors will be considered to determine which nodesshould be chosen as the cluster heads We explore the criticalfactors affecting the energy depletion as indicators includingdistance to base station and the number of rounds beingelected as cluster heads consecutively

As for the intercluster communication multihop way hasbetter results than single-hop approach in energy efficiencyIn this paper the multihop forwarding between the clusterhead and the base station will be discussed Since the head


near the base station consumed more energy we use an une-qual approach for clustering

Since the higher energy consumption in the process ofthe multihop forwarding the cluster heads that are closer tothe base station should get smaller cluster size than thosefar away Therefore the nodersquos competition radius shoulddecrease as its distance to the base station decreases Basedon the above analysis we describe the 119877 comp 119894 as follows

119877 comp 119894 = (1 minus 120591 times119889max minus 119889 (119894BS)119889max minus 119889min

) times 1199030times radic

119864res 1198941198640

(6)

where 1199030is the predefined maximum competition radius and

119889(119894BS) is the distance from node 119894 to the base station 119889maxand 119889min are the maximum and minimum distance betweenthe node and the base station respectively

33 Selection from Candidate Cluster Heads The proposedmethod performs clustering in the initial network environ-ment and selects cluster heads considering the distance to thebase station the number of times for being ever selected andresidual energy Here cluster heads are selected by comparingtheir critical values instead of the conventional method ofselecting candidate cluster heads in the course of cluster headselection For optimizing the selection of cluster heads wedescribe the function cos 119905 as follows

cos 119905 (lowast) = 1205821times 1205781+ 1205822times 1205782+ 1205823times 1205783

1205781=

119889 (119894 119895)

radic119864 (119889119894)

1205782=

10038161003816100381610038161198640 minus 119864res (119895)1003816100381610038161003816

radic119864 (119864res (119878119894))

1205783=

119879119895

1199032

1205821+ 1205822+ 1205823= 1

(7)

where 119894 denotes the normal node 119878119894denotes the set of

candidate cluster heads near node 119894 119889(119894 119895) is the distancefrom node 119894 to the cluster head 119895 and 119864(119889

119894) denotes the

mathematical expectation of distance from all the candidatecluster head to the node 119894 119864res(119895) denotes the remainingenergy of the cluster head 119895 and 119864(119864res(119878119894)) denotes allthe candidate cluster head residual energy of mathematicalexpectation 120582

1 1205822 and 120582

3are used to describe the cost

proportion 0 lt 1205821 1205822 1205823lt 1

From (7) when cos 119905 obtains the minimum value we canobtain the most optimal cluster heads The weighted valueshave a great impact on multiattribute decision-making Gen-erally speaking the smaller the difference in property valuesis the less the impact of decision is For these attributes wecan set lower weighted value and vice versa

In this paper we use the standard deviation and meandeviation to measure the difference of attributes The stan-dard deviation is defined as follows

120590119895= radic

1

119899

119899

sum

119894=1

(

1003817100381710038171003817100381710038171003817100381710038171003817

119908119895120578119894119895minus

1

119899

119899

sum

119896=1

119908119895120578119894119895

1003817100381710038171003817100381710038171003817100381710038171003817

)

2

+ 119873 (0 1) (8)

The mean deviation is defined as follows

119872119895=

1

119899

119899

sum

119894=1

(

10038171003817100381710038171003817100381710038171003817100381710038171003817

119908119895120578119894119895minus

1

119899

119899

sum

119895=1

119908119895120578119894119895

10038171003817100381710038171003817100381710038171003817100381710038171003817

) + 119873 (0 1) (9)

where 119873(0 1) denotes the standard normal distribution and120578119895= (1119899)sum

119899

119894=1120578119894119895

The objective function can be defined as follows

max119865 (119908) =

119899

sum

119895=1

[120572 times radic120590119895(119908) + (1 minus 120572) times radic119872

119895(119908)] (10)

Thus the weights can be converted into a single problemof solving nonlinear programming problem properties andwe calculate

119908119895=

120572 times radic120590119895(119908) + (1 minus 120572) times radic119872

119895(119908)

sum119899

119895=1[120572 times radic120590

119895(119908) + (1 minus 120572) times radic119872

119895(119908)]

(11)

From (11) we can obtain the optimal weight vector 119908 =

1199081 1199082 1199083

34 Collaborative Communication and Routing At the stageof the intracluster communication each cluster head trans-mits a time table to its cluster members with TDMA technol-ogy which incises time into many cyclical frames Althoughgenerally used in wireless network protocol TDMAhas somedisadvantages Firstly in every round all themembers shouldsend data to their cluster head which cause high-energyconsumption Secondly each user is allowed to transmitonly within specified time intervals and it will increase thetotal delay of the network Thirdly time synchronization isrequired at the beginning of each round which will result inplenty of unnecessary message exchanges

Therefore we use an improved mechanism based onOVSF Firstly the cluster head sends the OVSF matrix to itscluster members And then the nodes add a group of OVSFcodes ahead of every datagramWhen receiving all messagesthe cluster head uses the same OVSF matrix multiplied bythe received data so that we could utilize the characteristicof orthogonality and incoherence of each node not onlyto realize the indiscriminate data transmission but also togreatly reduce the delay and improve the efficiency of energyconsumption

Cluster routing is an energy-efficient routing model ascompared with direct routing and multihop routing InLEACH the sensing nodes sense the environment and thentransmit the data towards the cluster head and then thecluster head aggregates them and transmits to the base stationwith single hop The problem of this mechanism lies in


the fact that the cluster head far from the base station costshigh energy and moves into death rapidly which results innetwork fractional nonconnectivity

A technique for intercluster communication is presentedfor WSNs in which each cluster head sends its data to itsneighbor cluster head which is nearer to the base stationto achieve load balancing in network We assume the dataredundancy is limited and the intermediate cluster head onlyforwards the data to the next hop node instead of doing datafusion

In the process of establishing the intercluster communi-cation channel cluster head 119894 will choose the cluster head inthe neighboring cluster in terms of the cost function 119864relayWe describe the 119864relay as follows

119864relay = (radic119889 (119894 119895) + radic119889 (119894BS)) timessum119896isin119894 RSCH 119864res 119896

119864res 119895

times119899119895

sum119896isin119904119894 RSCH 119899

119896

+ 120577

(12)

where 119894 RSCH = 119895 | forall119895 119889(119895BS) lt 119889(119894BS) 119894 119895 = 1 2

119899119896 119864res 119895 is the current residual energy of cluster head 119895 119899

119896is

the number of the members in cluster 119896 and 120577 is energy errorvariable In order to cost less overhead the cluster head withhigh residual energy and owning relative few members canbecome the next-hop intermediate node

4 Simulation Experiment

In this section we evaluate the performance of our protocolimplemented with MATLAB We assume the probability ofsignal collision and interference in the wireless channel isignorable Cluster-based routing algorithms have differentconfiguration parameters which may affect the experimentalresults In order to reflect the fairness of algorithms thispaper will take the same configuration parameters in [8]Thespecific experimental parameters are shown in Table 1

Figure 2 shows the change in the aspect of the minimumdistance between the cluster heads in the first 50 rounds Ascan be seen from the result the distance between the clusterheads is about 30m in LEACH protocol while it is about60m in our protocol It means that the cluster heads must bemuch concentrated in some area of the network In CHSCDPthe competition radius is set reasonably to guarantee thedistribution of cluster heads evenly

Furthermore we report result for the comparison of aver-age energy consumption of cluster heads Figure 3 shows theaverage energy consumption of cluster heads for the two pro-tocols As shown in Figure 3 the average energy consumptionof cluster heads in LEACH fluctuates in the range of 033 Jwhich is higher than that of the CHSCDPThis is mainly dueto the superiority of multihop transmission which can saveenergy greatly in comparison with the single hop

Figure 4 shows the comparison of the energy consump-tion in clustered phase for the two protocols in case of notconsidering the energy loss in the stable communicationphase It can be observed that the CHSCDP can be slightly

Table 1 Experimental parameters

Parameters ValueSimulation area (mtimesm) 400 times 400Number of nodes 200The station of base station (200 200)Initial energy 2 J119864fs 10 PJbitsdotm2

119896 1000 bits119864DA 5 nJ(bitsdotsignal)Eelec 50 nJbit119864amp 00013 PJbitsdotm4

1198890

90m

CHSCDPLEACH

90

80

70

60

50

40

30

20

10

010 15 20 25 300 35 40 45 50

Round5

The m

inim

um d

istan

ce b

etw

een

cluste

r hea

ds

Figure 2 The minimum distance between cluster heads

larger than LEACHwith respect to the total energy consump-tion in the formulation of cluster The proposed CHSCDPgives the network a more uniform distribution of clusterheads As the candidate nodes need to compete in limitedarea they should consume more energy However ADV isa low capacity message so the power consumption is verysmall Although the energy consumption of CHSCDP hasincreased in clustered phase it does not affect the overallefficiency of the protocol

Obviously lifetime is the criterion for evaluating theperformance of sensor networks In the simulation wemeasure the life cycle by rounds and it is defined as the totalamount of time before the first sensor node runs out of powerFigure 5 shows the simulation curve of different protocollike LEACH LEACH-M and our protocol From Figure 5we can see that the BECCS clearly improves the networklifetime over LEACH and LEACH-M The death time of


0 5 10 15 20 25 30 35 40 45 500

01

02

03

04

05

Round

CHSCDPLEACH

The a

vera

ge en

ergy

cons

umpt

ion

of C

Hs (

J)

Figure 3 The average energy consumption of cluster heads for thetwo protocols

0 5 10 15 20 25 30 35 40 45 50997

9975

998

9985

999

9995

100

CHSCDPLEACH

Round

Ener

gy co

nsum

ptio

n of

clus

ter f

orm

ulat

ion

Figure 4 Comparison of the energy consumption of the twoprotocols in clustered phase

cluster head of LEACH LEACH-M and BECCS is 311 334and 402 rounds respectively It can be observed that BECCSprotocol can quickly converge when the network failed SinceWSNs have high fault tolerance self-organization and othercharacteristics the failure of some node does not affect theoverall network performance But when most of the nodeslapsed the network presence has no meaning and thereforethis protocol can be more suitable for WSNs

Figures 6 and 7 show the number of each node beingelected as cluster head when the first node died for LEACH

0 100 200 300 400 500 600 7000

02

04

06

08

1

Round

CHSCDPLEACH-MLEACH

Aliv

e nod

es (

)Figure 5 The percentage of alive nodes

0 20 40 60 80 100 120 140 160 180 2005

55

6

65

7

75

8

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH

Figure 6 The number of each node being elected as cluster headwhen the first node died in LEACH

and CHSCDP As we can see in Figure 6 the number ofelections as cluster head of each node exhibits a narrowrange of fluctuation between 6 and 7 However LEACH usesuniform clustering and single-hop communication betweencluster heads and base station which have fast energyconsumption for cluster heads far away from the base stationso as to resulting in premature death In CHSCDP because itemploys extra cluster heads to afford themultihop forwardingtraffic in the areas closer to the base station the nodes in these


0 20 40 60 80 100 120 140 160 180 2000

2

4

6

8

10

12

14

16

18

20

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH

Figure 7 The number of each node being elected as cluster headwhen the first node died in BECCS

areas usually gain the higher choice to be selected as clusterheads

Figure 8 shows the residual energy distribution of thewhole network As it can be seen from Figure 8 the nodesrsquoresidual energy distribution of CHSCDP gets smaller fluctu-ations than LEACH and the node average residual energy ofCHSCDP is much higher Because CHSCDP uses multihopcommunication and unequal clustering strategy makingclusters near the base station is relatively small and there arerelatively few members in the cluster

Finally we compared the effects of parameter values 120593 onthe networkrsquos overall energy consumption and the averagedelay Figure 9 shows the residual energy of the whole net-work with the different parameter values It can be seen thatCHSCDP is better than the traditional LEACH algorithm interms of energy balance The multihop routing forwardingand the use ofOVSF coding contribute to reducing the energyconsumption From the experimental result in Figure 9 wealso can observe that the value of 120593 is inversely proportionalto the nodersquos communication radius thus influencing theenergy consumption

The transmission delay is usually the amount of timewhile the information submitted to the network until beingreceived by the destination and it is defined as the averagedelay for all nodes in a certain period We compare thenetwork delay between LEACH and CHSCDP under thesame simulation environment In analysis the greater thevalue of 120593 is the less the communication radius of eachnode will be Figure 10 shows the average delay for the twoprotocols with the different clusters of expectations It can beobserved that the average transmission delay varies inverselyas the number of clusters and the average transmission delayof CHSCDP is slightly larger than LEACH The reason isthe multihop routing which results in a certain delay that is

0 20 40 60 80 100 120 140 160 180 2000

005

01

015

02

025

03

035

04

Node

CHSCDPLEACH

The r

esid

ual e

nerg

y (J

)Figure 8 The residual energy distribution of the whole network

200 220 240 260 280 300 320 340 360 380 400100

120

140

160

180

200

220

240

260

Round

LEACHCHSCDP (120593 = 06)CHSCDP (120593 = 04)

CHSCDP (120593 = 02)

The r

esid

ual e

nerg

y of

all n

does

(J)

Figure 9 The residual energy of the whole network

inevitable It can be seen that as the cluster of expectations isincreased the average transmission delay of the two routingalgorithms gradually closes and the difference tends to lessen

Through the above experimental results it can beobserved that the CHSCDP is able to reduce energy con-sumption and obtain higher efficiency as well as effectivelyprolonging the lifetime of network more than a few existingcluster-based routing protocols The proposed CHSCDP


2 4 6 8 10 12 14 16 1804

06

08

1

12

14

16

18

2

Clustering expectation P

Aver

age d

elay

(s)


CHSCDP (120593 = 02)

Figure 10 Comparison of network average delay

protocol is well enhanced and balanced on exploration andexploitation and has better stability and scalability

5 Conclusions

In this paper we propose an efficient cluster head selectionapproach for collaborative data processing in WSNs Theenergy grading concept is applied to select the cluster headsand the competition process can obtain better convergenceand cost lower message overhead Furthermore for thenoncluster heads which locate in overlapping area coveredby several cluster heads we proposed a novel approach toevaluate the optimal cluster head in accordance with thefactors such as residual energy distance and the numberof rounds for being selected The approach also producesan unequal clustering to balance the overload among clusterheads CHSCDP is fully distributed and more energy effi-cient In the future we will improve the proposed protocolby minimizing the communication cost and also increasingthe reliability of the network to make further works morepractical

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This work is partially supported by the National ScienceFoundation of China (no 20120313032-3) the Natural Sci-ence Foundation of Shanxi Province (no 2012011015-1) andthe National Natural Science Foundation (nos 61202163

61240035 and 61373100) This work is also supported byScientific Research Program Funded by Shaanxi ProvincialEducation Department (no 2013JK1139) and supported byChina Postdoctoral Science Foundation (no 2013M542370)and the Specialized Research Fund for the Doctoral Programof Higher Education of China (no 20136118120010) Theauthors would like to thank the anonymous reviewers fortheir insightful comments and constructive suggestions thathave improved the paper

References

[1] N A Jamal and A E Kamal ldquoRouting techniques in wirelesssensor networks a surveyrdquo IEEEWireless Communications vol11 no 6 pp 26ndash28 2004

[2] J Yick B Mukherjee and D Ghosal ldquoWireless sensor networksurveyrdquoComputerNetworks vol 52 no 12 pp 2292ndash2330 2008

[3] C-F Lai R Zhu B F Chen and Y Lee ldquoA 3D falling recon-struction system using sensor awareness for ubiquitous health-carerdquo Sensor Letters vol 11 no 5 pp 828ndash835 2013

[4] Y THou Y Shi andHD Sherali ldquoRate allocation and networklifetime problems for wireless sensor networksrdquo IEEEACMTransactions on Networking vol 16 no 2 pp 321ndash334 2008

[5] Y Wang C Gong B Su and Y Wang ldquoDelay-dependentrobust stability of uncertain T-S fuzzy systems with Time-varying delayrdquo International Journal of Innovative ComputingInformation and Control vol 5 no 9 pp 2665ndash2674 2009

[6] C Intanagonwiwat and R Govindan ldquoDirected diffusion ascalable and robust communication paradigm for sensor net-worksrdquo in Proceedings of the 6th Annual International Confer-ence onMobile Computing andNetworking (MOBICOM rsquo00) pp56ndash67 Boston Mass USA August 2000

[7] Y Wang P Chen and Y Jin ldquoTrajectory planning for anunmanned ground vehicle group using augmented particleswarm optimization in a dynamic environmentrdquo in Proceedingof the IEEE International Conference on Systems Man andCybernetics (SMCrsquo09) pp 4341ndash4346 San Antonio Tex USAOctober 2009

[8] S Zairi B Zouari and ENiel ldquoNodes self-scheduling approachfor maximizing WSN lifetime based on remaining energyrdquoTheInstitution of Engineering and Technology vol 2 no 1 pp 52ndash622012

[9] O Younis and S Fahmy ldquoHEED a hybrid energy-efficientdistributed clustering approach for ad hoc sensor networksrdquoIEEE Transactions on Mobile Computing vol 3 no 4 pp 660ndash669 2004

[10] C F Hsin and M Liu ldquoRandomly duty-cycled wireless sensornetworks dynamics of coveragerdquo IEEE Transactions onWirelessCommunications vol 5 no 11 pp 3182ndash3192 2006

[11] K Kar and S Banerjee ldquoNode placement for connected cov-erage in sensor networksrdquo in Proceedings of the Modeling andOptimization in Mobile Ad Hoc and Wireless Networks pp 50ndash52 Sophia-Antipolirsquos France 2003

[12] Y Li C Vu C Ai G Chen and Y Zhao ldquoTransformingcomplete coverage algorithms to partial coverage algorithms forwireless sensor networksrdquo IEEE Transactions on Parallel andDistributed Systems vol 22 no 4 pp 695ndash703 2011

[13] I Sim K Choi K Kwon and J Lee ldquoEnergy efficient clusterheader selection algorithm in WSNrdquo in Proceedings of theInternational Conference on Complex Intelligent and Software


Intensive Systems (CISISrsquo09) pp 584ndash587 Fukuoka JapanMarch 2009

[14] M C M Thein and T Thein ldquoAn energy efficient cluster-headselection for wireless sensor networksrdquo in Proceedings of the1st International Conference on Intelligent Systems Modellingand Simulation (ISMS rsquo10) pp 287ndash291 Liverpool UK January2010

[15] H Chen K Li and X Sun ldquoPerformance analysis of wsnsclustering protocol based-on poisson distributionrdquo ComputerMeasurement amp Control vol 12 no 9 pp 2590ndash2593 2012

[16] B A Attea and E A Khalil ldquoA new evolutionary based routingprotocol for clustered heterogeneous wireless sensor networksrdquoApplied Soft Computing Journal vol 12 no 7 pp 1950ndash19572012

[17] H-B Ching G Yang and S-J Hu ldquoNHRPA a novel hierar-chical routing protocol algorithm for wireless sensor networksrdquoThe Journal of China Universities of Posts and Telecommunica-tions vol 15 no 3 pp 75ndash81 2008

[18] J Joe-Air L Tzu-Shiang C Cheng-Long et al ldquoA QoS-guaranteed coverage precedence routing algorithm for wirelesssensor networksrdquo Sensors vol 11 no 4 pp 3418ndash3438 2011

[19] C J Jiang W R Shi X L Tang P Wang and M XiangldquoEnergy-balanced unequal clustering routing protocol for wire-less sensor networksrdquo Journal of Software vol 23 no 5 pp1222ndash1232 2012

[20] W B Heinzelman A P Chandrakasan and H Balakrish-nan ldquoAn application-specific protocol architecture for wirelessmicrosensor networksrdquo IEEE Transactions onWireless Commu-nications vol 1 no 4 pp 660ndash670 2002

[21] W Shu and J Wang ldquoAn optimized multi-hop routing algo-rithm based on clonal selection strategy for energy-efficientmanagement in wireless sensor networksrdquo Sensors and Trans-ducers vol 22 no 6 pp 8ndash14 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



from the cluster heads to the nearer nodes that are far fromthe base station

Due to collecting data in its cluster and forwardingfrom other clusters the cluster heads near the base stationwill consume more energy and die in advance which willinfluence the life cycle of the whole network [6] In this paperan unequal clustering method is proposed to balance theenergy consumption among the cluster heads The proposedalgorithm offers a framework for collaborative data process-ing among researchers By adjusting competition radius ofdifferent candidate cluster heads the cluster size near the basestation is smaller comparatively in order to compensate forunbalanced communication overhead caused by interclustercommunications

The specific contributions of this paper include thefollowing

(i) A literature survey about various existing cluster-based routing protocols and an analysis of theiradvantages and disadvantages are presented

(ii) An effective energy-efficient optimization model forsolving the unequal clustering in WSNs is proposed

(iii) Theproposed protocol adopts the idea of energy grad-ing to select the cluster heads and the competitionprocess can obtain better convergence and cost lowermessage overhead

The rest of this paper is organized as follows a briefsurvey is given in Section 2 We study the CHSCDP protocoland formalize it in Section 3 Experimental results andcomparisonswith existing cluster-based routing protocols arepresented in Section 4 Finally Section 5 concludes the paperand discusses some future research directions

2 Related Works

The collaborative data processing has attracted much atten-tion of many domestic and international researchers inWSNs In this section we focus our discussion on the relatedworks on collaborative data processing and energy optimiza-tion in WSNs

In order to achieve high-energy efficiency and increasethe network scalability the nodes can be organized intoclusters and apply random selection method for cluster headelection in an epoch [7] After collecting all the data of itsmembers cluster head transfers them to the base stationDuring the phase of clustering the cluster head plays animportant role in providing data communication to nodesand the base station efficiently This method can increasethe energy consumption of the sensor network and acquirenetwork scalability

Low-energy adaptive clustering hierarchy (LEACH) is atypical clustering-based protocol [8] In order to save energyand enhance network lifetime the hierarchical routing isadopted Younis and Fahmy [9] proposed hybrid energy-efficient distributed clustering (HEED) which periodicallyselects cluster heads according to a hybrid of the noderesidual energy and a secondary parameter such as nodeproximity to its neighbors or node degree Hsin and Liu [10]

proposed a new technique to select the cluster heads inevery round which depends both on current state probabilityand on general probability Kar and Banerjee [11] proposeda distributive energy-efficient adaptive clustering (DEEAC)protocol which is having spatiotemporal variations in datareporting rates across different regions DEEAC selects anode to be a cluster head depending upon its hotness valueand residual energy Dr Li et al proposed a dynamic stochas-tic distributed energy-efficient clustering method where thecluster head election probability is more efficient [12] More-over it uses a stochastic scheme detection to extend thenetwork lifetime

Sim et al [13] proposed an energy-efficient cluster headerselection algorithm (ECS) which selects cluster head byutilizing only its information to extend network lifetimeand minimize additional overheads in energy limited sensornetworks M C M Thein and T Thein [14] proposed amodification of the LEACHrsquos stochastic cluster head selectionalgorithm by considering the additional parameters theresidual energy of a node relative to the residual energy ofthe network for adapting clusters and rotating cluster headpositions to evenly distribute the energy load among all thenodes Chen et al [15] proposed an energy consumptionmode of clustering protocol in the condition that nodesfollowed the Poisson distribution and analyzed the networkperformance which was impacted by Poisson distributiondensity of the nodes

Attea and Khalil proposed a new evolutionary basedrouting protocol for clustered heterogeneous WSNs [16]Ching et al proposed a novel hierarchical routing pro-tocol algorithm (NHRPA) for WSNs [17] Joe-Air et alpresented a QoS-guaranteed coverage precedence routingalgorithm [18] Jiang et al [19] proposed a distributed energy-balanced unequal clustering (DEBUC) routing protocol andan unequal clustering mechanism is adopted for interclustermultihop way

3 Collaborative Data Processingand CHSCDP Protocol

The cluster head will be selected randomly in a cyclicmanner in LEACH protocol and the energy of the wholenetwork load is evenly distributed to each node It can reducenetwork energy consumption and improve overall survivaltime of the network LEACH protocol has many advantagessuch as hierarchical structure local dynamic data fusionand cluster head selection [20] Although LEACH protocolcan effectively prevent data loss caused by redundant datatransmission there are also obvious shortcomings such asdata redundancy energy imbalance and poor stability [21]Therefore this paper proposes an improved cluster headselection based on LEACH which focuses on the residualenergy of the candidate nodes and the problem of energyconsumption for intercluster communication

In order to design an efficient clustering algorithm weneed a comprehensive analysis of the performance indicatorsas follows

Firstly energy efficiency means avoiding the end ofthe life cycle of the network caused by the early death of











2) 119889 lt 119889

0


0

(1)


0=


as

119864Rx = 119896 times 119864elec (2)


Level (119894) =

119864res isin (119864avg + 119864max

2 119864max] 119894 = 1


2] 119894 = 2

119864res isin (119864avg + 119864min

2 119864avg] 119894 = 3


2] 119894 = 4

(3)

where 119864avg = (119864max + 119864min)2



k bitpacket

k bitpacket

d

ETx(k d) ETx(k)


amplifier



119875CH = max(120582 times1

Level (119894)times

119864res1198640

119875min) (4)



119875min =

119875

1 minus 119875 times (119903 mod(1119875)) 119862119894(119905) = 1

0 119862119894(119905) = 0

(5)



119894(119905) = 0











119864res 1198941198640

(6)





1205781=

119889 (119894 119895)

radic119864 (119889119894)

1205782=

10038161003816100381610038161198640 minus 119864res (119895)1003816100381610038161003816

radic119864 (119864res (119878119894))

1205783=

119879119895

1199032

1205821+ 1205822+ 1205823= 1

(7)



119894) denotes the


1 1205822 and 120582


proportion 0 lt 1205821 1205822 1205823lt 1



120590119895= radic

1

119899

119899

sum

119894=1

(

1003817100381710038171003817100381710038171003817100381710038171003817

119908119895120578119894119895minus

1

119899

119899

sum

119896=1

119908119895120578119894119895

1003817100381710038171003817100381710038171003817100381710038171003817

)

2

+ 119873 (0 1) (8)


119872119895=

1

119899

119899

sum

119894=1

(

10038171003817100381710038171003817100381710038171003817100381710038171003817

119908119895120578119894119895minus

1

119899

119899

sum

119895=1

119908119895120578119894119895

10038171003817100381710038171003817100381710038171003817100381710038171003817

) + 119873 (0 1) (9)


119899

119894=1120578119894119895


max119865 (119908) =

119899

sum

119895=1


119895(119908)] (10)


119908119895=


119895(119908)

sum119899

119895=1[120572 times radic120590


119895(119908)]

(11)


1199081 1199082 1199083









119864res 119895

times119899119895

sum119896isin119904119894 RSCH 119899

119896

+ 120577

(12)



119896is










1198890

90m

CHSCDPLEACH

90

80

70

60

50

40

30

20

10

010 15 20 25 300 35 40 45 50

Round5

The m

inim

um d

istan

ce b

etw

een

cluste

r hea

ds





0 5 10 15 20 25 30 35 40 45 500

01

02

03

04

05

Round

CHSCDPLEACH

The a

vera

ge en

ergy

cons

umpt

ion

of C

Hs (

J)


0 5 10 15 20 25 30 35 40 45 50997

9975

998

9985

999

9995

100

CHSCDPLEACH

Round

Ener

gy co

nsum

ptio

n of

clus

ter f

orm

ulat

ion




0 100 200 300 400 500 600 7000

02

04

06

08

1

Round

CHSCDPLEACH-MLEACH

Aliv

e nod

es (


0 20 40 60 80 100 120 140 160 180 2005

55

6

65

7

75

8

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH




0 20 40 60 80 100 120 140 160 180 2000

2

4

6

8

10

12

14

16

18

20

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH






0 20 40 60 80 100 120 140 160 180 2000

005

01

015

02

025

03

035

04

Node

CHSCDPLEACH

The r

esid

ual e

nerg

y (J


200 220 240 260 280 300 320 340 360 380 400100

120

140

160

180

200

220

240

260

Round


CHSCDP (120593 = 02)

The r

esid

ual e

nerg

y of

all n

does

(J)





2 4 6 8 10 12 14 16 1804

06

08

1

12

14

16

18

2


Aver

age d

elay

(s)


CHSCDP (120593 = 02)



5 Conclusions




Acknowledgments



References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation



















2) 119889 lt 119889

0


0

(1)


0=


as

119864Rx = 119896 times 119864elec (2)


Level (119894) =

119864res isin (119864avg + 119864max

2 119864max] 119894 = 1


2] 119894 = 2

119864res isin (119864avg + 119864min

2 119864avg] 119894 = 3


2] 119894 = 4

(3)

where 119864avg = (119864max + 119864min)2



k bitpacket

k bitpacket

d

ETx(k d) ETx(k)


amplifier



119875CH = max(120582 times1

Level (119894)times

119864res1198640

119875min) (4)



119875min =

119875

1 minus 119875 times (119903 mod(1119875)) 119862119894(119905) = 1

0 119862119894(119905) = 0

(5)



119894(119905) = 0











119864res 1198941198640

(6)





1205781=

119889 (119894 119895)

radic119864 (119889119894)

1205782=

10038161003816100381610038161198640 minus 119864res (119895)1003816100381610038161003816

radic119864 (119864res (119878119894))

1205783=

119879119895

1199032

1205821+ 1205822+ 1205823= 1

(7)



119894) denotes the


1 1205822 and 120582


proportion 0 lt 1205821 1205822 1205823lt 1



120590119895= radic

1

119899

119899

sum

119894=1

(

1003817100381710038171003817100381710038171003817100381710038171003817

119908119895120578119894119895minus

1

119899

119899

sum

119896=1

119908119895120578119894119895

1003817100381710038171003817100381710038171003817100381710038171003817

)

2

+ 119873 (0 1) (8)


119872119895=

1

119899

119899

sum

119894=1

(

10038171003817100381710038171003817100381710038171003817100381710038171003817

119908119895120578119894119895minus

1

119899

119899

sum

119895=1

119908119895120578119894119895

10038171003817100381710038171003817100381710038171003817100381710038171003817

) + 119873 (0 1) (9)


119899

119894=1120578119894119895


max119865 (119908) =

119899

sum

119895=1


119895(119908)] (10)


119908119895=


119895(119908)

sum119899

119895=1[120572 times radic120590


119895(119908)]

(11)


1199081 1199082 1199083









119864res 119895

times119899119895

sum119896isin119904119894 RSCH 119899

119896

+ 120577

(12)



119896is










1198890

90m

CHSCDPLEACH

90

80

70

60

50

40

30

20

10

010 15 20 25 300 35 40 45 50

Round5

The m

inim

um d

istan

ce b

etw

een

cluste

r hea

ds





0 5 10 15 20 25 30 35 40 45 500

01

02

03

04

05

Round

CHSCDPLEACH

The a

vera

ge en

ergy

cons

umpt

ion

of C

Hs (

J)


0 5 10 15 20 25 30 35 40 45 50997

9975

998

9985

999

9995

100

CHSCDPLEACH

Round

Ener

gy co

nsum

ptio

n of

clus

ter f

orm

ulat

ion




0 100 200 300 400 500 600 7000

02

04

06

08

1

Round

CHSCDPLEACH-MLEACH

Aliv

e nod

es (


0 20 40 60 80 100 120 140 160 180 2005

55

6

65

7

75

8

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH




0 20 40 60 80 100 120 140 160 180 2000

2

4

6

8

10

12

14

16

18

20

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH






0 20 40 60 80 100 120 140 160 180 2000

005

01

015

02

025

03

035

04

Node

CHSCDPLEACH

The r

esid

ual e

nerg

y (J


200 220 240 260 280 300 320 340 360 380 400100

120

140

160

180

200

220

240

260

Round


CHSCDP (120593 = 02)

The r

esid

ual e

nerg

y of

all n

does

(J)





2 4 6 8 10 12 14 16 1804

06

08

1

12

14

16

18

2


Aver

age d

elay

(s)


CHSCDP (120593 = 02)



5 Conclusions




Acknowledgments



References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation














119864res 1198941198640

(6)





1205781=

119889 (119894 119895)

radic119864 (119889119894)

1205782=

10038161003816100381610038161198640 minus 119864res (119895)1003816100381610038161003816

radic119864 (119864res (119878119894))

1205783=

119879119895

1199032

1205821+ 1205822+ 1205823= 1

(7)



119894) denotes the


1 1205822 and 120582


proportion 0 lt 1205821 1205822 1205823lt 1



120590119895= radic

1

119899

119899

sum

119894=1

(

1003817100381710038171003817100381710038171003817100381710038171003817

119908119895120578119894119895minus

1

119899

119899

sum

119896=1

119908119895120578119894119895

1003817100381710038171003817100381710038171003817100381710038171003817

)

2

+ 119873 (0 1) (8)


119872119895=

1

119899

119899

sum

119894=1

(

10038171003817100381710038171003817100381710038171003817100381710038171003817

119908119895120578119894119895minus

1

119899

119899

sum

119895=1

119908119895120578119894119895

10038171003817100381710038171003817100381710038171003817100381710038171003817

) + 119873 (0 1) (9)


119899

119894=1120578119894119895


max119865 (119908) =

119899

sum

119895=1


119895(119908)] (10)


119908119895=


119895(119908)

sum119899

119895=1[120572 times radic120590


119895(119908)]

(11)


1199081 1199082 1199083









119864res 119895

times119899119895

sum119896isin119904119894 RSCH 119899

119896

+ 120577

(12)



119896is










1198890

90m

CHSCDPLEACH

90

80

70

60

50

40

30

20

10

010 15 20 25 300 35 40 45 50

Round5

The m

inim

um d

istan

ce b

etw

een

cluste

r hea

ds





0 5 10 15 20 25 30 35 40 45 500

01

02

03

04

05

Round

CHSCDPLEACH

The a

vera

ge en

ergy

cons

umpt

ion

of C

Hs (

J)


0 5 10 15 20 25 30 35 40 45 50997

9975

998

9985

999

9995

100

CHSCDPLEACH

Round

Ener

gy co

nsum

ptio

n of

clus

ter f

orm

ulat

ion




0 100 200 300 400 500 600 7000

02

04

06

08

1

Round

CHSCDPLEACH-MLEACH

Aliv

e nod

es (


0 20 40 60 80 100 120 140 160 180 2005

55

6

65

7

75

8

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH




0 20 40 60 80 100 120 140 160 180 2000

2

4

6

8

10

12

14

16

18

20

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH






0 20 40 60 80 100 120 140 160 180 2000

005

01

015

02

025

03

035

04

Node

CHSCDPLEACH

The r

esid

ual e

nerg

y (J


200 220 240 260 280 300 320 340 360 380 400100

120

140

160

180

200

220

240

260

Round


CHSCDP (120593 = 02)

The r

esid

ual e

nerg

y of

all n

does

(J)





2 4 6 8 10 12 14 16 1804

06

08

1

12

14

16

18

2


Aver

age d

elay

(s)


CHSCDP (120593 = 02)



5 Conclusions




Acknowledgments



References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation














119864res 119895

times119899119895

sum119896isin119904119894 RSCH 119899

119896

+ 120577

(12)



119896is










1198890

90m

CHSCDPLEACH

90

80

70

60

50

40

30

20

10

010 15 20 25 300 35 40 45 50

Round5

The m

inim

um d

istan

ce b

etw

een

cluste

r hea

ds





0 5 10 15 20 25 30 35 40 45 500

01

02

03

04

05

Round

CHSCDPLEACH

The a

vera

ge en

ergy

cons

umpt

ion

of C

Hs (

J)


0 5 10 15 20 25 30 35 40 45 50997

9975

998

9985

999

9995

100

CHSCDPLEACH

Round

Ener

gy co

nsum

ptio

n of

clus

ter f

orm

ulat

ion




0 100 200 300 400 500 600 7000

02

04

06

08

1

Round

CHSCDPLEACH-MLEACH

Aliv

e nod

es (


0 20 40 60 80 100 120 140 160 180 2005

55

6

65

7

75

8

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH




0 20 40 60 80 100 120 140 160 180 2000

2

4

6

8

10

12

14

16

18

20

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH






0 20 40 60 80 100 120 140 160 180 2000

005

01

015

02

025

03

035

04

Node

CHSCDPLEACH

The r

esid

ual e

nerg

y (J


200 220 240 260 280 300 320 340 360 380 400100

120

140

160

180

200

220

240

260

Round


CHSCDP (120593 = 02)

The r

esid

ual e

nerg

y of

all n

does

(J)





2 4 6 8 10 12 14 16 1804

06

08

1

12

14

16

18

2


Aver

age d

elay

(s)


CHSCDP (120593 = 02)



5 Conclusions




Acknowledgments



References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










0 5 10 15 20 25 30 35 40 45 500

01

02

03

04

05

Round

CHSCDPLEACH

The a

vera

ge en

ergy

cons

umpt

ion

of C

Hs (

J)


0 5 10 15 20 25 30 35 40 45 50997

9975

998

9985

999

9995

100

CHSCDPLEACH

Round

Ener

gy co

nsum

ptio

n of

clus

ter f

orm

ulat

ion




0 100 200 300 400 500 600 7000

02

04

06

08

1

Round

CHSCDPLEACH-MLEACH

Aliv

e nod

es (


0 20 40 60 80 100 120 140 160 180 2005

55

6

65

7

75

8

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH




0 20 40 60 80 100 120 140 160 180 2000

2

4

6

8

10

12

14

16

18

20

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH






0 20 40 60 80 100 120 140 160 180 2000

005

01

015

02

025

03

035

04

Node

CHSCDPLEACH

The r

esid

ual e

nerg

y (J


200 220 240 260 280 300 320 340 360 380 400100

120

140

160

180

200

220

240

260

Round


CHSCDP (120593 = 02)

The r

esid

ual e

nerg

y of

all n

does

(J)





2 4 6 8 10 12 14 16 1804

06

08

1

12

14

16

18

2


Aver

age d

elay

(s)


CHSCDP (120593 = 02)



5 Conclusions




Acknowledgments



References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










0 20 40 60 80 100 120 140 160 180 2000

2

4

6

8

10

12

14

16

18

20

Node

CHSCDP

The n

umbe

r of e

ach

node

bei

ng el

ecte

d as

CH






0 20 40 60 80 100 120 140 160 180 2000

005

01

015

02

025

03

035

04

Node

CHSCDPLEACH

The r

esid

ual e

nerg

y (J


200 220 240 260 280 300 320 340 360 380 400100

120

140

160

180

200

220

240

260

Round


CHSCDP (120593 = 02)

The r

esid

ual e

nerg

y of

all n

does

(J)





2 4 6 8 10 12 14 16 1804

06

08

1

12

14

16

18

2


Aver

age d

elay

(s)


CHSCDP (120593 = 02)



5 Conclusions




Acknowledgments



References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










2 4 6 8 10 12 14 16 1804

06

08

1

12

14

16

18

2


Aver

age d

elay

(s)


CHSCDP (120593 = 02)



5 Conclusions




Acknowledgments



References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation





















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









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