maximum residual energy based clustering scheme for wireless sensor networks

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Advanced Science FocusVol. 1, pp. 111–119, 2013(www.aspbs.com/asfo)

Maximum Residual Energy Based ClusteringScheme for Wireless Sensor NetworksPuneet Azad1, 2, ∗ and Vidushi Sharma2

1Department of Electronics and Communication, Maharaja Surajmal Institute of Technology,Janak Puri, New Delhi 110058, India2School of Information and Communication Technology, Gautam Buddha University,Greater Noida, Gautam Budh Nagar 201308, Uttar Pradesh, India

One of the major challenges in wireless sensor networks (WSNs) is uniform energy dissipation and lifetimeof the network. The nodes with higher energy dissipation lead to early death leaving a percentage of areaunattended for the entire lifetime of the network. This result in a shorter stability region as compared to otherprotocols designed for extending the lifetime of the network. This paper deals with a new scheme termed themaximum residual based clustering scheme (MREC) in which the entire network is re-clustered in each cycleof data transmission. With automatic rotation of cluster head having maximum residual energy, MREC achievesuniform energy dissipation through the whole network. The algorithm works in static and dynamic mode. Informer, the cluster remains fixed for entire lifetime and the cluster heads are rotated within the cluster, whilein later, re-clustering is done in each cycle with cluster head having the highest energy. The performance ofthe proposed method is compared with low-energy adaptive clustering hierarchy (LEACH) in homogeneousenvironment and with energy efficient heterogeneous clustered scheme (EEHC) and distributed energy-efficientclustering algorithm (DEEC) in heterogeneous environment. Our simulation results demonstrate that MREC ismore effective for prolonging the network lifetime, enhancing stability and has better reliability with estimatedconfidence bounds of network lifetime.

KEYWORDS: MREC, Wireless Sensor Networks, Clustering, Lifetime, Heterogeneous, Optimum.

1. INTRODUCTION

Wireless Sensor Networks (WSNs) consist of sensor hard-ware units called sensor nodes, capable of sensing, dataprocessing and transmitting the information.1 They havea wide range of applications in monitoring environmen-tal conditions,2 surveillance,3 audio and video retrieval,4

healthcare,5 forest fire detection, flood detection etc.WSNs process and transmit data efficiently using an inbuiltprocessor and consist of four components: sensor, proces-sor, transceiver and a power unit.1 Additional componentssuch as a GPS unit, power generator and mobiliser maybe added to enhance their performance. The major issuein WSN technology is the efficient management of energybetween randomly deployed sensor nodes so that their util-ity function can be maximised. The aim is to developan energy efficient protocol that avoids the gradual deathof nodes, thereby creating a uniform delivery of packets

∗Author to whom correspondence should be addressed.Email: [email protected]: 27 September 2012Accepted: 1 December 2012

for most of the lifetime of the network. In this context,the clustering technique can be used for designing energyefficient protocols for enhancing the lifetime of entirenetwork.6 A few clustering algorithms have been studiedin the area of wireless sensor networks.7�8 Sensor nodesare grouped into clusters based on their location, resid-ual energy, signal strength or connectivity etc. Clusteringenables bandwidth reuse, saves energy, increases systemcapacity, better resource allocation and reduces overheadcommunication. Each cluster has a CH which is responsi-ble for gathering the data from all the nodes in its clusterand subsequently transfers it to the base-station. A vari-ety of promising protocols such as LEACH,9 HEED,10

DHAC11 and ADRP12 have been reported in literature.These protocols operate in a homogeneous environmentwhere all the nodes are initially assigned equal energy.Whereas protocols like EEHC,13 DEEC,14 SEP15 are suit-able for a heterogeneous environment where a fixed per-centage of nodes have more energy then others; however,further work is required in order to improve the efficiencyof WSNs for better lifetime. In this paper, a new algorithmbased on the residual energy of the nodes is developed,which initially uses fuzzy c-means clustering16�17 and then

Adv. Sci. Focus, Vol. 1, No. 2, 2013 2330-0760/2013/1/111/009 doi:10.1166/asfo.2013.1021 111

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Maximum Residual Energy Based Clustering Scheme for Wireless Sensor Networks Azad and Sharma

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LEdevelops a new approach for the selection of cluster heads(CHs).The outline of this work is as follows. Section 2 dis-

cusses the related literature; Section 3 discusses the pro-posed methodology in detail; Section 4 demonstrates thesimulations and performance results; and finally conclu-sions are summarised in Section 5.

2. BACKGROUND AND RELATED WORK

2.1. Homogeneous Environment Protocols

LEACH9 is the most popular distributed cluster-basedrouting protocol in wireless sensor networks for a homo-geneous environment. In this paper, a central control algo-rithm is designed to form the clusters by dispersing thecluster head (CH) nodes throughout the network. Eachnode chooses to become cluster head based on a prede-fined threshold given whereby

T �S�=⎧⎨⎩

popt

1−popt ∗ �c ∗ mod1/popt�0

if S ∈G (1)

popt is the percentage of cluster heads, c is the currentcycle and G is the set of nodes that have not been clus-ter heads in the last 1/popt cycles. The cluster head nodesfuse and aggregate data arriving from nodes from everycluster and send the aggregated data to the base station(sink) in order to reduce the amount of data and avoidduplicate entry. Data collection is centralised to sink andperform periodically. In LEACH, the number of clusterschosen in an optimum manner is found to be about 5 per-cent of the total number of nodes. The nodes select acluster head based on the received signal strength of theadvertisement message and send data in their time slot ineach round. Leach-C an improvement over Leach, findsclusters using the simulated annealing algorithm and findk optimum clusters with minimum average energy dissi-pation per round. Whereas the hybrid energy-efficient dis-tributed (HEED)10 protocol is an energy-aware hierarchicalapproach that is an improvement on LEACH. This proto-col uses two radio transmission power levels for intra-andinter-cluster communication. Cluster heads are probabilis-tically selected based on their residual energy. Anotherparameter called neighbour proximity or node degree isused to find the best cluster head for a normal node tojoin. If a CH is far from the sink, it tries to send theaggregate data to another CH instead of sending to thesink directly. Zhou et al.11 reported taking an entirely dif-ferent approach called distributed hierarchical agglomera-tive clustering (DHAC) algorithm in which the clusters areformed before the selection of cluster head unlike in otherprotocols. The clusters are formed on the basis of quanti-tative data such as location of nodes and received signalstrength as well as qualitative data such as connectivity.

Clusters were formed using various hierarchical cluster-ing methods which include SLINK, CLINK, UPGMA, andWPGAM.6 The simulation of homogeneous sensor nodesreveals the improved lifetime of the network as comparedto LEACH and LEACH-C. Another important protocol isAdaptive Decentralised Re-Clustering Protocol (ADRP)12

in which the cluster heads and next heads are elected onthe basis of the residual energy of each node and the aver-age energy of each cluster. The selection of cluster headsand next heads are weighted by the remaining energyof sensor nodes and the average energy of each cluster.The sensor nodes with the highest energy in the clusterscan be cluster heads at different cycles of time. Thus therole of cluster heads can be switched dynamically. Zhuet al.18 presented a new clustering protocol based on haus-dorff distance and minimum energy routing for WSNs tomaximise the lifetime. Clusters once formed are basedon node locations, communication efficiency and networkconnectivity and the role of the cluster head is optimallyrotated among the cluster members. After cluster headsare selected, they form a network to periodically collect,aggregate, and forward data to the base station using min-imum energy (cost) routing. This method can significantlyincrease the network lifetime as compared to other knownmethods. Since the clusters are static, the selection of clus-ter heads is limited to nodes present in that cluster, whichmay not choose nodes that have maximum residual energy.Min et al.19 presented an energy efficient clustering algo-rithm for maximising lifetime of wireless sensor networkswith optimum parameters for reducing energy consump-tion and prolonging the system lifetime. All nodes aredivided into static clusters with optimum parameters withsmaller clusters close to the base station and larger clustersaway from it. Thus the cluster head near the base stationshould be kept alive avoiding the energy hole problem inmulti-hop communication. The disadvantage of such analgorithm is the incomplete coverage due to considerationof sector shape clusters.

2.2. Heterogeneous Environment Protocols

EEHC13 adopt the heterogeneity of the nodes in terms oftheir initial energy i.e., a percentage of nodes are equippedwith more energy than others. The nodes play the roleof a cluster head based on weighted election probabilitiesaccording to the residual energy given by

pn =popt

1+m∗ ��+mo��(2)

where popt is the predetermined percentage of the clusterhead, m is the fraction of the total number of nodes, mo isthe percentage of m having � times more energy than oth-ers. The probability of the selection of a node (as a clusterhead) is more when the initial energy of the node is higher.It is reported that the lifetime of the network increased

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Azad and Sharma Maximum Residual Energy Based Clustering Scheme for Wireless Sensor Networks

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LEby 10 percent as compared with the results obtained byusing the LEACH method. However, Zhou et al.20 havefurther analysed computational heterogeneity in which fewnodes with powerful processing capability, more energyand storage can act as cluster heads for data aggregationand transmission in heterogeneous WSNs. There are threenode types; 0 type, 1 type and few management nodes.The cluster heads are selected based on energy dissipationforecast and clustering management (EDFCM), in whichthe energy consumption of the cluster heads in last roundis used as the forecast value for the next round. The clusterhead selection algorithm is based on the method of energydissipation forecast and clustering management (EDFCM).DEEC14 is an energy efficient clustering protocol in whichthe cluster-heads are selected by a probability based on theratio between residual energy of each node and the aver-age energy of the network. The nodes with high initial andresidual energy will have more chances to be the cluster-heads than the nodes with low energy. Another methodconsiders strategic deployment2 for selecting the clusterhead. The clusters are formed in the form of multiple-sized fixed grids while taking into account the arbitrary-shaped area sensed by the sensor nodes. Since deploymentis strategic, no separate algorithm is designed for the CHselection. This kind of scheme is only suitable for civilpurpose and cannot be applied for military or vigilancepurposes.

3. PROPOSED METHODOLOGY

The present methodology is based on selecting the opti-mum number of cluster heads (described in Section 4.2),forming the clusters and then transmitting the data sensedfrom the environment. The base station divides the net-work into clusters and selects optimum number of clus-ter heads. This is done initially by a well known fuzzyc-means (FCM) clustering technique, whereby centre ofeach cluster become the cluster head. Further after fewrounds, the cluster heads are selected based on their resid-ual energy and clusters are formed based on the shortestdistance between CHs and nodes. This algorithm consistsof set-up and steady state phases. In the set-up phase,clusters are formed using FCM clustering for few initialcycles and then, a new method is adopted for the for-mation of clusters based on nodes residual energy. In thesteady state, CHs are responsible for aggregating and send-ing the data to the sink. Thus static clusters are formedwith the cluster head being located nearest to the centroidof the cluster. After few cycles of data transmission (about100 cycles), the clusters formation adopts a new method-ology in which the position of CH is optimally sched-uled among the nodes present in the cluster based on theresidual energy discarding the traditional FCM method.This method is hereafter referred to as s-MREC (StaticMaximum Residual Energy based Clustering) in which the

clusters remain static throughout the lifetime of the net-work, but the role of CH is rotated among other nodesof the cluster. This strategy is further modified (explainedin the next section), where clusters are re-formed in eachcycle of data transmission unlike in s-MREC. This re-clustering approach creates a new method called MREC(Maximum Residual Energy based Clustering) wherein anoptimum number of cluster heads are selected in eachcycle of data transmission based on the maximum residualenergy in the whole network. Based on the selected CH,the nodes join a particular cluster based on the nearest dis-tance (Euclidean distance). The performance of these twotechniques (s-MREC and MREC) is compared with thatof the results obtained form other reported protocols. Westudy these two techniques (s-MREC and MREC) with thefollowing presumptions.1. Nodes are placed randomly in a 100× 100 region inuniform distribution. They are location aware and can getinformation through GPS. The role of nodes is to sensethe environment and send the data to their respective clus-ter head.2. The base station is also static and is located in thecentre of the field with no energy constraints.3. The initial number of clusters is fixed by taking opti-mum value (Section 4.2) and keeps on varying with thenode density.4. To ensure heterogeneity, two type of nodes are definedwith energies Eo and Eo (1+��, where � is the percentageincrease in energy.5. The nodes in a cluster can only communicate withtheir respective cluster head and not with the base stationdirectly.6. The transmission and processing capability of all thenodes is same.

The following messages are used in the formation ofclusters during the set-up phase:a. Hello Message: This message is sent by all the nodes tothe base-station in the beginning. It contains the locationinformation obtained through GPS.b. Broadcast Message: Each elected CH broadcasts itsnode-id to all the nodes present in the network in such away that they should reach the farthest node in the net-work.c. CH_Join Message: Each non-CH node then measuresthe distance with the available list of elected CHs andchooses the one at minimum distance. Then a CH_Joinmessage is sent from each node to the respective clusterhead to be joined.

3.1. Static Maximum Residual Energy BasedClustering (s-MREC)

s-MREC consists of the following steps:Step 1: Obtain an input data set

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LEAn input data set for s-MREC is a data matrix that

consists of nodes placed randomly in an area followinga uniform distribution method. Figure 1 shows a typical100 node network in a 100× 100 m2 area. Each nodesends its location information to the base station througha “Hello” message containing the node id and physicallocation.Step 2: Initialize the number of clusters and assign clus-

ter headsThe basic purpose of the s-MREC is to generate an opti-

mum number of clusters (described in Section 4.2) usingthe FCM technique. The cluster heads are chosen as thenode nearest to the centre as determined by this technique.In this method, each data point has a degree of belongingto clusters rather than belonging completely to just onecluster. It was developed in 1973 by Dunn15 and improvedin 1981 by Bezdek.16 This method obtains the best loca-tion of clusters in an optimum manner by minimising anobjective function as given by

J =N∑i=1

C∑j=1

umij ��xi− cj ��2 (3)

where xi is the set of data points, N is the number of dataset, cj is the center of the clusters, C is the number of clus-ters, uij is the degree of membership of xi in the clusterj , �∗� is any norm expressing the similarity between anymeasured data and the centre (which is the distance here)and m is the weighing exponent on each fuzzy member-ship. Fuzzy partitioning is carried out through an iterativeoptimisation of the objective function shown above, withthe update of membership uij and the cluster centres cj by

uij =1∑C

k=1��xi− cj��xi− ck��2/�m−1��

where cj =∑N

i=1 umij ·xi∑N

i=1 umij

(4)

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

X

Y

Fig. 1. A 100-node network.

These iterations stop, when maxij ��u�k+1�ij − u

�k�ij �� < ,

where is a termination criterion between 0 and 1,whereas k signifies the iteration steps. In the present study,sensor nodes are grouped into the ten clusters (Fig. 2(a)),which is the optimum value in a scenario of 100 nodes asdiscussed later in Section 4.2. Each cluster contains a clus-ter head (red square in Fig. 2(a)) nearest to the centroidof the cluster. Each selected CH broadcasts its node id inits respective cluster in such a way that it should reachthe farthest node in the cluster. The nodes then send thejoin-request message to their CH.Step 3: Data transmission and CH re-assignmentAfter the cluster head is allocated and clusters are

formed, all the nodes are assigned a Time DivisionMultiple Access (TDMA) schedule in each cycle of datatransmission. There is no collision between neighbour-ing clusters as they do not have a specified boundaryinstead the nodes present in a cluster are connected totheir respective cluster head as shown in Figure 2(a). Thenodes continuously monitor the environment and send thedata to their respective cluster head in the assigned slot-subsequently the data can be transferred to the base sta-tion from all the cluster heads. After few cycles of data

0 10 20 30 40 50 60 70 80 90 1000

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100

Base Station

X

Y

0 10 20 30 40 50 60 70 80 90 1000

10

20

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60

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80

90

100

Base Station

X

Y

(a)

(b)

Fig. 2. Cluster formation with (a) CH nearest to the centre using FCM,(b) Maximum residual energy node (in the same cluster) as a CH.

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LEtransmission, it is found that cluster heads deplete theirenergy drastically and lose their efficiency to act as a clus-ter head. In this situation, new cluster heads are selectedbased on maximum residual energy among all the nodesin respective clusters (Fig. 2(b)). It is to be noted that ini-tially the number of clusters remains same during the datatransmission. As the energy of nodes become less than athreshold value required for sending the data packet, theyare declared dead and no longer take part in the data trans-mission. If any cluster contains fewer nodes than definedby a threshold value, then it merges into the neighbour-ing cluster. Also the two CHs in different clusters may bevery close to each other and the nodes present in the cor-responding clusters have to choose a farther distance forthe transmission resulting in higher energy expenditure.This drawback is avoided in MREC (next section) as theclusters are re-formed each having selected CHs havingminimum Euclidean distance in every cycle. In this study,simulations are performed in both a homogeneous and aheterogeneous environment.

3.2. Maximum Residual Energy BasedClustering (MREC)

In this section, the above mentioned protocol (s-MREC)is modified, however the methodology for cluster headselection is same as used in s-MREC. The difference liesin the formation of clusters, which are reformed aftereach cycle of data transmission. It is to be noted thatthe MREC method is investigated in heterogeneous mode,where fewer nodes are assigned more initial energy (in apredefined ratio) than other nodes. The explicit methodol-ogy of MREC is as follows:Step 1: Initial cluster formation and selection of cluster

headThe input dataset is obtained in the same manner as

discussed in step 1 of Section 3.1. The methodology forelecting the cluster head is same as followed in s-MREC(using the FCM technique and designating the node near-est to the centre of the cluster as cluster head).Step 2: Re-selection of cluster head and data

transmissionThis step mainly differentiates MREC from s-MREC in

which the clusters are formed dynamically after the selec-tion of CH in each cycle. After receiving the data fromthe nodes for a few cycles, the node with the maximumresidual energy in its respective clusters is elected as a CHat the end of each cycle. Subsequently, new clusters areformed around all elected CHs using Euclidean distance.Thus nodes lying in the vicinity of any CH form a newcluster in each cycle. It is to be noted that the re-clusteringmethodology is also adopted in LEACH protocol, whereCHs are elected by using the probabilistic approach ratherthen based maximum residual energy criterion (MREC).The nodes are re-clustered based on the distance with the

selected cluster head using a distance matrix, DM (m×n)given as follows;

DM =

⎡⎢⎢⎣dCH1� x1 dCH1� x2 dCH1� xn

dCH2� x1 dCH2� x2 dCH2�xn

� � �dCHm�x1 dCHm�x2 dCHm�x3 dCHm�xn

⎤⎥⎥⎦ (5)

where d is the Euclidean distance between CH and a nodebased on its location information. If y and z represent thelocation of two nodes p and q, then the Euclidean dis-tance is

dp�q = ��px −qx�2+ �py −qy�

2 1/2 (6)

Each element di� j in the distance matrix represents thedistance between the ith clusterhead and j node. The col-umn containing the minimum value represents the clusternumber to be joined by the corresponding node. For exam-ple, if dCH2� x1 is the minimum value in the first column,in this situation the node x1 gets associate with the secondcluster where CH2 is cluster head.The operation of re-clustering and data transmission

continues for many cycles (as discussed above) until thedeath of first node. If the size of the cluster is smaller thenthe predefined threshold, the cluster merges with the neigh-bouring clusters shown in the dotted lines in Figure 3. Withthe start of the death of nodes, it is found that there are alesser number of nodes present in each cluster now. Thusas the number of alive nodes starts decreasing with cycles,the number of clusters also decreases and the decrease inthe number of alive nodes eventually results in the reduc-tion in number of clusters as in Figure 3. The amount ofinformation also decreases with the fewer nodes left inthe physical area. The typical schematics of MREC ands-MREC are shown in flow chart (Fig. 4). The techniquefor data transmission is similar to that used in s-MRECtechnique.

0 10 20 30 40 50 60 70 80 90 1000

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100

Base Station

X

Y

Fig. 3. Merging of small clusters (as shown in dotted lines) and reduc-tion in clusters with 24 nodes left.

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Invite nodes to join CH with min distance to build new Clusters

Initialize number of

clusters

Y

s-MREC

Assign it as CH for the corresponding

cluster

Y

N

YIncr C

Y

N

Cycle, C =1

Broadcast CH ids to all the nodes

Generate Clusters with center using

FCM

Find number of nodes in cluster,

nodes_cluster

Is nodes_cluster

> 1

Find nearest cluster and

merge

Is there a node coinciding with

the center?

Find nodenearest to the

center

N

Find node with max residual energy in all

existing clusters

Transmit data using TDMA

Start sensing data for next cycle

Incr CIs C>100 ?

N

Find nodes with maximum residual energy in all the clusters for the next cycle

Re-generate clusters by building distance matrix

Is nodes_cluster > 1

Is nodes_cluster <10

Find nearest cluster and

merge

Reduce number of

clusters by 1

Transmit data using TDMA

Start sensing data for next cycle

Split clusters

Y

MREC

Fig. 4. Flowchart for s-MREC and MREC.

4. RESULTS AND DISCUSSION

The simulated results in MATLAB of the proposed s-MREC and MREC are compared with previously reportedprotocols, which include LEACH, DHAC, EEHC andDEEC. The protocols are executed 500 times for differ-ent deployment of nodes and mean results are used forcomparison. The performance of the proposed protocols ismeasured in terms of following metrics:1. Network lifetime: It shows the time interval from thestart of operation until the death of all the nodes presentin the network.

2. Confidence bounds: It is an interval estimate of the life-time of the nodes, which is used to indicate the reliability.The network lifetime lies in the range of the confidenceinterval.3. Stability region: It is the total time before the death ofthe first node occurs.4. Number of packets received: It shows the number ofpackets received by the base station from all the CHs.

4.1. Setup

The WSN consists of 100 sensor nodes placed in a100× 100 area. The simulation is conducted for both a

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LEhomogeneous and a heterogeneous environment. A simpleradio energy dissipation model1 in transmitting a l bit mes-sage over a distance d is presented in this section usingthe simulation parameters of practical interest shown inTable I. Free space and multi path fading model21 are usedfor the data transmission of packets from nodes to clusterhead and further to base station. The threshold value ofdistance of transmission is given by

dt =√fs/mp (7)

Where fs and mp are the radio energy parameters(Table I) for free space and multi path fading model.If the transmission distance, d is less then the dt thresh-

old, free space model is adopted, otherwise the multi-pathmodel is used. The energy used to send a l bit data at adistance d is given by

ETX ={l ∗Eelec+ l ∗fs ∗d2 if d ≤ dt

l ∗Eelec+ l ∗mp ∗d4 if d ≥ dt(8)

For receiving a l bit message, the energy consumed is

ERX = l ∗Eelec (9)

where Eelec is the energy consumed in the electronic circuitto transmit or receive the signal, fs and mp are the energyconsumed by the amplifier for transmission at shorter orlonger distance.

4.2. Optimum Number of Cluster Heads

The selection of the number of clusters is a critical taskin any clustering algorithm in a general size network asit depends upon how many cluster heads are chosen in anoptimum manner. Each cluster has a cluster head that isresponsible for the data aggregation of the data receivedfrom its cluster members and does not take part in thesensing operation. In this algorithm, the average energy perround is plotted versus the number of clusters as shownin Figure 5. For our experiment, two ranges of distancesbetween nodes and base station are observed when thebase station is placed firstly in the centre of the field and

Table I. Simulation parameters for transmission.

Description Symbol Value

Number of nodes in the system N 100Initial energy EInit 2 J/1.5 JSize of the data packet — 500 bytesHello/Broadcast/CH_Join message — 25 bytesEnergy consumed by the amplifier to fs 10 pJ/bit/m2

transmit at a short distanceEnergy consumed by the amplifier to mp 0.0013 pJ/bit/m4

transmit at a longer distanceEnergy consumed in the electronics circuit Eelec 50 nJ/bit

to transmit or receive the signal

2 3 4 5 6 7 8 9 10 11 1230

35

40

45

50

55

60

65

70

75

Number of Clusters

Ave

rag

e en

erg

y d

issi

pat

ion

per

ro

un

d (

mJ)

Fig. 5. Average energy dissipated per round.

secondly far away from the field: 9 m < disttoBS < 63 mand 53 m< disttoBS < 151 m. An estimate of the optimumnumber of clusters, kopt

22 is given by

kopt =√

fs

��mpd4toBS−Eelec�

·M√N (10)

Using the above equation, we calculate the optimumnumber of clusters to be 8 < kopt < 10. These analyticalresults are verified using simulations on a 100 node net-work by considering the average energy dissipation perround as shown in Figure 5. This graph shows that theoptimum number of clusters are around 9–12 for the 100-node network with minimum energy dissipation. It is seenthat the simulation results of energy dissipation cover therange as obtained through analytical results of Eq. (10).Thus for this algorithms, we set the number of clusters tobe 10.

4.3. Confidence Bounds

Each time the simulations are conducted for a differentplacement of nodes, the distance between nodes and CHsis different, which results in different network lifetime.The data of network lifetime is collected which varies withthe number of cycles. In this context it is necessary toestimate the confidence bounds for network lifetime. Thebounds measure the confidence that the obtained networklifetime lies within a range regardless of the random dis-tribution of nodes.The bounds are defined with a level of desired certainty.

The level of certainty is often 95%, but it can be any valuesuch as 99%, 99.9%, and so on. The method for findingthe confidence bound is based on the mean of the datawhich is given by

x =∑n

i=1 xin

(11)

The standard deviation of the data values is

s =√∑n

i=1 �xi−x�2

n−1(12)

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LETable II. Network lifetime within 95% and 99% confidence bounds.

s-MREC (cycles) MREC (cycles)

Homogeneous Heterogeneous Homogeneous Heterogeneous

UCL99% 3622 4388 3827 5306LCL99% 3597 4297 3719 5055UCL95% 3619 4377 3814 5276LCL95% 3600 4307 3732 5085

This estimated standard deviation is used to create thelower confidence limit (LCL) and upper confidence limit(UCL) about the mean given by

x± t��n−1

s√n

(13)

where t in the above equation represents the student −tdistribution and � is 0.05 for 95% confidence and 0.01 for99% confidence. The calculated values of LCL and UCLfor overall network lifetime in cycles in our experimentsare shown in Table II.

4.4. Performance

For comparison, computed WSN lifetimes for s-MREC,MREC (99 % confidence bound) and LEACH protocolsare depicted in Figure 6 (for 2 joules of initial energy ofthe nodes) in a homogeneous environment. It is clear fromthe figure (Fig. 6) that the nodes remain alive for longerduration for s-MREC and MREC as compared to LEACH.Also MREC has the longest stability region compared tothe other two protocols.While for the heterogeneous environment, an equal

number of advanced and normal nodes are randomly dis-tributed over the field with the sink placed in the centre.The initial energy of normal nodes is 1.5 J and that ofadvanced nodes is 3 J. The results for proposed MRECand s-MREC for an average cycle within the 99% con-fidence bound (as shown in Table II) are compared withEEHC and DEEC (for similar environment) as shown inFigure 7 where the total initial energy of the system is1.5nEinit (n is the total number of nodes, Einit is the initialenergy of each normal node and Einit is the initial energy

0 1000 2000 3000 4000 5000 6000 70000

102030405060708090

100

Time (Cycles)

Nu

mb

er o

f A

live

No

des MREC

s-MRECLEACH

Fig. 6. Network lifetime for homogeneous environment.

0 1000 2000 3000 4000 5000 6000 7000 80000

10

20

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50

60

70

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90

100

Time (Cycles)

Nu

mb

er o

f A

live

No

des

MRECs-MRECDEECEEHC

Fig. 7. Network lifetime for heterogeneous environment.

of each advanced node). The stability region, in which allthe sensor nodes are sensing the environment, is found tobe significantly higher for MREC and s-MREC than forother protocols (DEEC and EEHC). It is observed thatalmost 88% of the nodes in MREC die quickly within atime span of 26 cycles, which shows that the dissipationof energy in the majority of nodes is uniform. Particu-larly, the death of the first node occurs after 3561 cyclesin MREC; 3239 cycles in s-MREC, 1500 cycles in DEECand 1008 cycles in EEHC. In EEHC, the death of thenode starts very early in the 1000th cycle, due to whichthere is a less number of nodes collecting the informationmost of the time as compared to other protocols (MREC,s-MREC and DEEC). Interestingly, the longest lifetime(5264 cycles) is associated with MREC protocol, whichis almost 22% higher than s-MREC (4325 cycles), 32%than EEHC (4000 cycles) and 42% higher than DEEC(3714 cycles).Figure 8 shows the total number of data packets received

at the base station from different cluster heads which isfound to be higher for MREC and s-MREC protocols

0 0.5 1 1.5 2 2.5 3 3.5 4

×105

0

10

20

30

40

50

60

70

80

90

100

Number of data packets received at Base Station

Nu

mb

er o

f N

od

es A

live

MRECs-MRECDHAC

Fig. 8. Number of nodes alive versus number of data packets receivedat base station.

118 Adv. Sci. Focus, 1, 111–119, 2013




ARTIC

LE

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

2

4

6

8

10

12

14

Cycles

Nu

mb

er o

f C

lust

ers

MRECEEHC

Fig. 9. Comparison between EEHC and MREC: Number of clusters perround.

as compared to DHAC in an homogeneous environment.More specifically, the number of data packets received(till the death of all nodes) at the base station is 1,49,309in MREC, 1,48,242 for s-MREC; and 26,500 in DHACprotocol. Figure 9 shows the variation in the number ofclusters for the entire life of the network. It is found thatnumber of clusters is invariant up to 1000 cycles usingMREC protocol. Also the stability region is significantlyhigher in comparison to EEHC which indicates a higherlifetime in MREC.

5. CONCLUSIONS

In this paper, we have investigated the problem of non-uniform energy dissipation and the limited lifetime ofthe network by developing an energy efficient clusteringscheme based on maximum residual energy. The initialnumber of clusters is fixed and optimum and varies inaccordance with the node density in each cluster with thestart of reduction in number of nodes. The simulations instatic and dynamic mode show that the proposed protocolis able to extend the lifetime of the network as comparedto the LEACH method in a homogeneous environment andDEEC and EEHC protocols in a heterogeneous environ-ment for same input parameters. It is also found that the

MREC has highest stability region and a 32% longer life-time than EEHC and 42% more than DEEC.

References and Notes

1. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, Com-put. Networks 38, 393 (2002).

2. A. Cerpa, J. Elson, M. Hamilton, and J. Zhao, Habitat monitoring:Application driver for wireless communications technology, ACMSIGCOMM’2000, Costa Rica, April (2001).

3. E. P. de Freitas, T. Heimfarth, and C. E. Pereira, Evaluation ofcoordination strategies for heterogeneous sensor networks aiming atsurveillance applications, Proceedings of IEEE Sensors (SENSORS),Christchurch, New Zealand (2009), pp. 591–596.

4. M. Rahimi, R. Baer, O. Iroezi, J. Garcia, J. Warrior, D. Estrin, andM. Srivastava, Cyclops: In situ image sensing and interpretation inwireless sensor networks, Proceedings of the ACM Conference onEmbedded Networked Sensor Systems San Diego, CA (2005).

5. J. M. Corchado, J. Bajo, D. I. Tapia, and A. Abraham, IEEE Trans.Inf. Technol. Biomed. 14, 234 (2010).

6. H. C. Romesburg, Cluster Analysis for Researchers, Lifetime Learn-ing Publications, Belmont, California (1990).

7. J. Y. Yu and P. H. J. Chong, IEEE Commun. Surv. Tutor. 7, 32 (2005).8. A. A. Abbasi and M. Younis, Comput. Commun. 30, 2826 (2007).9. W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, IEEE

Trans. Wirel. Commun. 1, 660 (2002).10. O. Younis and S. Fahmy, IEEE Trans. Mob. Comput. 3, 366 (2004).11. C. H. Lung and C. Zhou, Ad Hoc Networks. 8, 328 (2010).12. F. Bajaber and I. Awan, J. Comput. Syst. Sci. 77, 282 (2011).13. D. Kumar, T. C. Aseri, and R. B. Patel, Comput. Commun. 32, 662

(2009).14. L. Qing, Q. Zhu, and M. Wang, Comput. Commun. 29, 2230 (2006).15. G. Smaragdakis, I. Matta, and A. Bestavros, SEP: A stable elec-

tion protocol for clustered heterogeneous wireless sensor networks,Proceedings of Second International Workshop on Sensor and ActorNetwork Protocols and Applications (SANPA) (2004).

16. J. Dunn, Cybernet. Syst. 3, 32 (1973).17. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function

Algorithms, Plenum Press, New York (1981).18. X. Zhu, L. Shen, and T. S. P. Yum, IEEE Trans. Veh. Technol. 58, 990

(2009).19. X. Min, S. W. Ren, J. C. Jiang, and Z. Ying, Int. J. Elect. Commun.

64, 289 (2010).20. H. Zhou, Y. Wu, Y. Hu, and G. Xie, Comput. Commun. 33, 1843

(2010).21. T. Rappaport, Wireless Communications: Principles and Practice,

IEEE Press, Piscataway, NJ, USA (1996).22. F. Comeau, S. C. Sivakumar, W. Robertson, and W. J. Phillips,

Energy conserving architectures and algorithms for wireless sensornetworks, Proceedings of the 39th Annual Hawaii International Con-ference on System Sciences (2006), Vol. 9, p. 1.

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