E 2 LBC: An Energy Efficient Load Balanced Clustering Technique forHeterogeneous Wireless Sensor Networks

L.S.JayashreeAssistant Professor, CSE DepartmentKumaraguru College of Technology

Coimbatore-641 006

Dr.S.ArumugamAdditional Director (Examination)Directorate of Technical Education

Chennai

N.RajathiFinal Year M.E.[CSE]

Kumaraguru College of TechnologyCoimbatore-641 006

Abstract -Energy efficiency is of paramount interest to most of the wireless sensor network applications. In a clustered sensor

network, some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. In thisstudy, we assume a heterogeneous network setup, where a fixed percentage of the population of sensor nodes is equipped withadditional energy resources thus acting as cluster heads (two level hierarchy). The behavior of such sensor networks becomes veryunstable once the first head dies. Classical clustering techniques for wireless sensor networks pay much importance to reducing theper-node energy consumption, which may not always guarantee a globally efficient solution. We propose E2LBC, which considersenergy efficiency as a system-wide issue that focuses on improving the overall stability of operation of a wireless sensor network. Weprolong the stability period of the network by balancing the load at each cluster head. We prove the effectiveness of the proposedalgorithm by comparing it against a hypothetical unbalanced clustering protocol.

Keywords- Ad hoc sensor networks, Clustering, Energy Efficiency, Load balancing, Network Stability.

I. INTRODUCTION

Wireless Sensor Networks are networks of tiny, batterypowered sensor nodes with limited on-board processing,storage and radio capabilities [1]. Nodes sense and send theirreports toward a processing center that is called a base stationor a sink. Designing protocols and applications for suchnetworks has to be energy aware in order to prolong thelifetime of the network. Sensor networks, once deployed, areleft unattended and expected to work for extended periods oftime. This is true under many real world application settings,rendering battery replacement out of question - the life of thebattery decides the life of the network. Owing to theimportance of the problem, there is a significant body ofresearch addressing different aspects of power controlproblem. [1] gives a detailed survey on sensor networks andthe open research problems.

An essential operation in sensor network clustering is toselect a set of cluster heads and assign nodes optimally tothese heads. Clustering is particularly useful for applications

requiring scalability to hundreds or thousands of nodes. Manyclustering algorithms have been proposed [8-19] in general,for wireless ad hoc networks either as stand-alone protocolsfor ad hoc networks or in the context of routing [18,19]. Mostof these clustering techniques [10-13] are specificallydesigned with an objective of generating stable clusters inmobile networks. But in a typical sensor networkenvironment, the sensors' locations are normally fixed andthe instability of clusters is not an issue. Here, the primeobjective is to organize nodes into clusters in such a way tominimize the energy spent in communication from nodes tothe sink where the information processing is done. The costof transmitting a bit is higher than a computation and hence itmay be advantageous to organize the sensors into clusters [2].Clustering increases network lifetime by (i) reducing thenumber of nodes contending for channel access, (ii)summarizing network state information, thus reducing theamount of raw data getting propagated across the network.The essential operation in sensor node clustering is to select aset of cluster heads (in case of homogenous networks where

1-4244-0340-5/06/$20.00 02006 IEEE

all nodes have equal energy level) and optimally assign nodesto these cluster heads. In case of heterogeneous networks,some nodes are assumed to have higher energy levels andbecome natural candidates to act as cluster heads. Here theproblem reduces to that of finding an optimal assignment ofnodes to cluster heads. We describe optimality in terms ofenergy efficiency and network stability. We assumer aheterogeneous network set up with a sink placed at the centerof the sensing field. In this setup, we propose our E2LBCalgorithm, a fully distributed technique to find an optimalmapping between nodes and cluster heads. In most of theprior related works, energy efficiency had been the primeconcern; but in this work we consider the combined effect ofcommunication distance and load at each cluster head and wefind an optimal assignment of a normal node to a cluster headthat maximizes the overall network utility time. We alsoshow how the balancing criterion improves the stability ofoperation of a sensor network by comparing the resultsobtained with unbalanced case. We show by simulation thatE2LBC provides longer stability period than those clusteringprotocols that are unbalanced in nature.

The remainder of the paper is organized as follows: SectionII describes the models used in the problem addressed.Section III presents a brief review of related work. Section IVexplains the E 2LBC algorithm and shows how it achieves itsstated goals. Section V shows its effectiveness via simulationand contains discussions about the results. Finally section VIIpresents concluding remarks and outlines directions for futurework.

II. ENERGY EFFICIENT NETWORK DESIGN

In this section, we formally describe the problem ofmaximizing the network stability by means of (i) energyefficient assignment of nodes to cluster heads and (ii)balancing of load at cluster heads.

A. The Network Model

We assume that N nodes are distributed randomly in arectangular sensing field. The following system parametersare assumed:

* The nodes in the network are stationary (which istypical to sensor networks)

* Nodes are deployed randomly using a homogenousPoisson point process over the sensing region.

* The network is heterogeneous: Nodes of lowerenergy level Eo is deployed with intensity No(normalnodes) and nodes of higher energy level E1 isdeployed with an intensity N1 (Cluster heads).Nodes use omni directional antenna.Both member nodes and cluster heads use single hopcommunication. Cluster heads are well distributedwithin the sensing region.

* Nodes are left unattended after deployment.* Individual nodes periodically send their raw

measurement values to the cluster heads; eachcluster head then applies an appropriate aggregationfunction (which is dependent on the applicationunder consideration) to produce a higher levelmessage digest which is subsequently sent to thebase station for further analysis.

* Applications are of continuous data gathering type.

B. Energy Model

The amount of energy spent by a normal node to transmit apacket of unit length to its cluster head is,

B to elec Et amprkwhere,

(1)

E telec energy used by the transmittercircuitryEt amp energy used by the front endamplifier to counter the propagation lossk is the path loss gradient; 2<k<4,depending on the terrain used.r is the distance between a node and itscluster head

Energy spent by a cluster head to transmit a packet of unitlength to the base station is

E t1 E eec +Et ampD (2)where , D is the distance to the base station.

The energy required to receive a packet of unit length from anode is just,

Er1E'=ielec

Fig. I The Energy Consumption Model

I

(3)

A. The Clustering Problem:

Clusterhead

Clusterhead ; ............~~~~~......................... .. ~~~~~~o o 0. . 0

_W~~~~~~~~~......... ............_,..............

.........

Sensor

Fig.2 A Typical Clustered Sensor Network Setup

Assume that N=N0 +N1 are distributed randomly in thesensing field. Our goal is to identify an optimal mapping ofnodes to cluster heads. Each node Ni, where 1< Ni <No, ismapped exactly to one cluster head Nj, where 1< Nj<N1 Theclustering problem is completely distributed - each nodemakes its own decision only based on its local knowledge.Similar to [23], the clustering process in the E LBC

algorithm also is terminated within O(1) iterations,irrespective of the size of the network and the diameter of thesensing field. The algorithm ensures that the load at eachcluster is balanced and the total distance between the membernodes and their corresponding head nodes is minimized.Balancing the clusters is needed for evenly distributing therelay load on all head nodes. Minimizing the total distancehelps in reducing the communication overhead and hence theenergy dissipation. Most of the existing clustering techniques[10-19,21,22] do not enforce this balancing constraint, whichmay lead to unstable operation of the network because of thefaster expiry of certain popular head nodes (those with very

high node degree), because of their over relaying burden.

iii. RELATED WORK

The first work that questioned the behavior of clusteringprotocols in the presence of heterogeneity in clusteredwireless sensor networks was [3]. In this work Heinzelmananalyzed a method to elect cluster heads according to theenergy left in each node. The drawback of this method is thatthis decision was made per round and assumed that the totalenergy left in the network was known. The complexity andthe assumption of global knowledge of the energy left for thewhole network makes this method difficult to implement.Even a centralized approach of this method would be very

complicated and very slow, as the feedback should be reliablydelivered to each sensor in every round. In [4], Duarte-Meloand Liu examined the performance and energy consumptionof wireless sensor networks, in a field where there are twotypes of sensors. They consider nodes that are fewer but morepowerful that belong to an overlay. All the other nodes haveto report to these overlay nodes, and the overlay nodes

aggregate the data and send it to the sink. The drawback ofthis method is that there is no dynamic election of the clusterheads among the two types of nodes, and as a result nodesthat are far away from the powerful nodes will die first. Theauthors estimate the optimal percentage of powerful nodes inthe field, but this result is very difficult to use whenheterogeneity is a result of operation of the sensor networkand not a choice of optimal setting.

In [5], Mhatre and Rosenberg presented a cost basedcomparative study of homogeneous and heterogeneousclustered wireless sensor networks. They proposed a methodto estimate the optimal distribution among different types ofsensors, but again this result is hard to use if theheterogeneity is due to the operation of the network. [6]proposes a heterogeneous-aware stable cluster head electionprotocol that aims at longer stability period their protocol isbased on weighted election probabilities of each node tobecome cluster head according to the remaining energy ineach node. But [6] does not consider how nodes could beassigned optimally to -cluster heads. In [7], the authorsconsider a heterogeneous network and determine theoptimum node intensities and node battery energies. Theyconclude that cluster head intensity scales approximately assquare root of node intensity and we make use of thisobservation to decide the number of cluster heads in ourwork.

[20] illustrates a balanced k-clustering algorithm, forclustering sensor nodes into k number of clusters such thateach cluster is balanced and the total distance between sensornodes and the head nodes is minimized. Minimizing the totaldistance helps in reducing the communication overhead andhence energy dissipation. The authors in [20] have shown thatthe balanced k-clustering problem can be solved optimallyusing min-cost network flow but it should be noted that [20]does not contain any experimental evaluation and that it hasassumed the number of nodes as always a multiple of k,which may not always be feasible in practice. A related work[21], proposes an algorithm to maximize the network lifetimeby determining the optimal assignment of nodes to heads.The authors assume that cluster heads are chosen a priory andare fixed throughout the network lifetime. This eliminates theneed for cluster head re-election. Their work balances theload on cluster heads by dynamically adjusting the node-assignment to cluster heads based on the residual energy ofthe cluster heads. However, the important limitations of theirapproach are (i) the algorithm requires prior knowledge of thetopology state information, which may not be possible intypical wireless sensor network environments and (ii) giventhe number of cluster heads, the complexity of the algorithmgrows linearly with the number of member nodes.

Another clustering technique, HEED [22], uses residualenergy of cluster heads and their node degree as clusteringparameters. Node degree of a cluster head is decided by the

number of neighbours. [22] does not make any assumptionsabout the distribution or density of nodes, or about node'scapabilities. The authors claim that the clustering processterminates in 0(1) iterations i.e., it is independent of thenetwork topology or size, unlike [21]. The authors also arguethat, a careful selection of secondary clustering parameter canbalance load among cluster heads. Both [21] and [22] assumequasi-stationary network mobility models.

iv. THE E2 LBC ALGORITHM

In this section, we describe our E2LBC protocol in detail.We'll begin with the introduction to the clustering processand then proceed towards the protocol design and thesimulation results..

A. The Clustering Process

The overarching goal of our algorithm is to prolong thenetwork life time. For this reason, the protocol is designed soas to minimize the communication cost of a node. Asdiscussed earlier in section 2, the communication cost of anode is primarily decided by the distance over which it needsto transmit. Hence, it becomes obvious that minimizing thecommunication distance saves communication cost. Keepingthis simple observation in mind, we use the distance as theprimary metric to decide the mapping of a given node to acluster head. That is, a node associates with a cluster headthat is available in the shortest distance, if there are multipleheads in its communication vicinity. A cluster head acceptsthe association of a member node only if its inclusion doesnot exceed its capacity constraint. Thus load is used as asecondary metric and this constraint automatically balancesthe load on each cluster head, .thus improvising the overallnetwork usefulness.

B. Protocol Design

Initially, once the network is deployed, each normal nodebroadcasts an ASSO REQ message, which then is receivedby all the heads in its vicinity.(in order to avoid collisions, anappropriate MAC scheduling such as TDMA may be used).Each head receiving the message sends an ASSO RESmessage containing its ID and geographical location. A nodemay receive multiple such association responses but decidesto associate with the nearest head by calculating theEuclidean distance between itself and each of the heads in itsneighbourhood. It notifies the selected head with theASSO_CNF_REQ message. Upon receiving the confirmationrequest, the head node checks whether it has reached itscapacity limit. If so, it replies with an ASSO REJ message orelse with an ASSO CNF message and increases its membercount by 1.The node that receives the rejection message,repeats the same procedure with the next nearestneighbouring head.

CH1 (selected) Normal Node CH2 (Not selected)

Start node association

TIME I

ASSO_REQ

ASSO_RES(distance=X)

ASSO_REQ

ASSO_RESA

- (distance=Y)

Collection of replies from CHs

ASSO CNFREQ ASSO_CNC s

CH1 verifies its channel capacity

ASSO_CNF

Association with CH1 upon channel availability

Association completed; update channel capacity of CH1

Fig. 3. E2LBC Protocol- Sequence of Operation

CancelsCH2'sresponsesince Y>X

Note that we have assumed that each node has at least onehead in its neighbourhood. That is, each node reaches a headnode in a single hop. In case if no head is available in anode's vicinity, it may try multi hopping (as in DistanceVector Routing algorithm), to reach the nearest head byassociating with a node that is already attached to a clusterhead. Details of building the routing tree is anyway beyondthe scope of this paper.

V. PERFORMANCE EVALUATION OF E2LBC

A. Cluster Head life time model

Let E1 be the amount of initial energy supplied to a clusterhead and P1 be the energy spend by the head in each round.Here, a round specifies the reception of packets containingraw measurement values from all its member nodes,aggregate it to a single packet and send it to the remote basestation. The number of such rounds a head node withstandsbefore depleting its energy decides its lifetime. Hence,

T= E1/PI (4)

where T denotes the life time.

PI is the amount of energy spent by a head node whichincludes the energy spent in receiving packets from all thenormal nodes in its cluster, energy spent in aggregating thesepackets into a single packet and transmitting it to the basestation.

p1=E(Nv)[ Er]+Ef +Et (5)

where, E(N v) is the expected number of nodeslying in each clusterE r1 i the energy spent in receiving packetsfrom all the normal nodes in its clusterEf is the energy spent in fusing theindividual nodes' packets into a singleaggregateE t1 is the energy spent in transmitting theaggregate value to the base station

Let L be the size of packet in bits. Therefore, according to (3)

E = [E elec] (No / NI) L

Similarly, from (2) it is evident that,E t1= [Et elec+ E t ampD ] L

(6)

(7)

In the following section, we show that the experimentalresults match with the theoretical values derived.

B. The Simulation Environment

We simulate a sensing region of dimension IOOX100 mwith a population of sensor nodes ranging from 50-150deployed randomly (uniformly) over that region. Thehorizontal and vertical coordinates of each sensor are selectedrandomly between 0 and the maximum value of thedimension. A base station is assumed to be available at thecenter. This setting is realistic for most of the applications,which are non-hostile in nature like precision agriculture etc.The initial energy of a normal node was set to Eo =0. 5J (equalto one AA battery) and that of a cluster head to 1 J. Althoughthis value is arbitrary for the purpose of this study, this doesnot affect the behavior of our method. The radiocharacteristics used in our simulations are summarized inTable 1. The size of the message that nodes send to theircluster heads as well as the size of the (aggregated) messagethat a cluster head sends to the sink is set to 4000 bits.

TABLE 1THE SIMULATION SCENARIO

Parameter ScenarioArea 100 X 100 m

Location ofBS Center

N 50-150Path loss 2 & 4

exponent (k)Initial Energy 0.5 J 1 J

(Normal (Clusternode) Head)

E elec 50 nJ/bitE amp 5.46 pJ/bitEf 0.021 nJ/bit

Packet size 4000 bits

C. Results and Discussions

The energy spent by a head is updated in each round untilits residual energy falls below a threshold. Specifically, foreach cluster head we calculate:The data reception energy using the formula (6)The data aggregation energy (assumed as 5 nJ/msg)The transmission energy to the BS by using the formula (7).

We use the following performance measures to prove theeffectiveness of the algorithm:

* Stability Period: is the time interval from the start ofnetwork operation until the death of the first headnode. We also refer to this period as "stable region."

* Instability Period: is the time interval from the deathof the first head until the death of the last head. Wealso refer to this period as "unstable region."

* Network lifetime: is the time interval from the startof operation of the sensor network till the death ofthe last head, beyond which the network not usable.

We generated 100 different topologies and the resultsshown in graphs are the averages of 100 simulation runs.According to our hypothesis, the average lifetime of eachhead is almost uniform as illustrated in Fig. 4. We ague that,this is due to load balancing, wherein all the head nodes havenearly uniform amount of energy consumption. This alsoprolongs the time interval before the death of the first node(stability period), which is crucial for many applicationswhere the feedback from the sensor network must be reliable.We show by simulation that E2LBC provides longer stabilityperiod than other clustering protocols. After the death of thefirst node, all the remaining nodes are expected to die onaverage within a small number of rounds as a consequence ofthe uniformly remaining energy due to the well-distributedenergy consumption. This is shown in figs.5 and 7, for k=2and 4 respectively. Also as is apparent, in the unbalanced

case, the network reaches unstable region very soon whencompared to the balanced case. Though, the lifetime of thenetwork is slightly above for unbalanced case as shown infigs. 6 and 8, most of the time the network operates inunstable region and the report from such a network would beinconsistent in nature.

N=50,k-2

140012001000800600400200

01 2 3 4 5 6 7

CH Id

Balanced Clustering Unbalanced Clustering

Fig. 4 CH Lifetime Distribution

500400300200 m M - m100

050 75 100 125

No.of Nodes

--Balanced Clustering Unbalanced C

Fig.7 Stability ofNetwork

k=4

40003000

0 2000a 1000

0

50 75 100 125 150No. of Nodes

800600400200

0

50 75 100 125 150No. of Nodes

--B alanced Clustering UnBalanced Clustering

Fig.5 Stability ofNetwork

k=2

3000

= 2000

a 1000

0

50 75 100 125No. of Nodes

UBalanced Clustering

150

- UnBalanced Clustering

Balanced Clustering -Unbalanced Clustering

Fig.8 Lifetime ofNetwork

V. CONCLUSION

We proposed E2LBC an energy efficient protocol forclustered ad hoc wireless sensor networks. It is not onlyenergy efficient but also prolongs stability period of theoverall network by means of balancing the relaying load atcluster heads. E2LBC does not also require re-balancing dueto power depletions, since the cluster heads are welldistributed and all the nodes as well as the cluster headsexperience almost uniform energy expenditure, which was

evident from the performance graphs shown. However,topology maintenance and re-clustering may be required incases where topology changes occur due to node failures or

physical node damages. We extend the current work in thisdirection and look for more efficient clustering technique,which are driven by the trend and pattern of the underlyingdata. This is the subject of a future paper.

Fig.6 Lifetime ofNetwork

k=4

150

Clustering

Zs0

4zZs

4z0

REFERENCES

[1]. Ian F.Akylidiz, Shankarasubramaniam,"A Survey on Sensor Networks,"IEEE Communications Magazine, August 2002.

[2]. G. J. Pottie and W. J. Kaiser, "Wireless Integrated Network Sensors",Communications of the ACM, Vol. 43, No. 5, pp 51-58, May 2000.

[3]. W. R. Heinzelman. Application-Specific Protocol Architectures forWireless Networks, Ph.D. thesis, Massachusetts Institute of Technology,2000.

[4]. E. J. Duarte-Melo and M. Liu. Analysis of energy consumption andlifetime of heterogeneous wireless sensor networks. In Proceedings ofGlobal Telecommunications Conference (GLOBECOM2002),pages 21-25, November 2002.

[5].V. Mhatre and C. Rosenberg. Homogeneous vs. heterogeneous clusteredsensor networks: A comparative study. In Proceedings of2004 IEEEInternational Conference on Communications (ICC 2004), June 2004.

[6]. Georgios Smaragdakis Ibrahim Matta Azer Bestavros' "SEP: A StableElection Protocol for clustered ", Technical Report BUCS-TR-2004-022.

[7]. Vivek Mhatre, Catherine Rosenberg, Daniel Kofman, RaviMazumdar, and Ness Shroff," A Minimum Cost Heterogeneous SensorNetwork with a Lifetime Constraint", IEEE Transactions On MobileComputing, Vol. 3, No. 3, July-September 2004.

[8]. D. J. Baker and A. Ephremides, "The Architectural Organization of a

Mobile Radio Network via Distributed Algorithm", IEEE Transactionson Communications, Vol. 29, No. 1 1, November 1981.

[9]. A. Ephremides, J.E. Wieselthier and D. J. Baker, "A Design concept forReliable Mobile Radio Networks with Frequency Hopping Signaling",Proceeding of IEEE, Vol. 75, No. 1, pp. 56-73, 1987.

[10].S.Basagni, "Distributed Clustering Algorithm for Ad hoc Networks",International Symposium on Parallel Architectures, Algorithms andNetworks (I_SPAN), 1999.

[11]. M.Chatterjee, S.K.Das and D.Turgut," WCA: A Weighted ClusteringAlgorithm for Mobile Adhoc Networks", Cluster Computing,pp. 193-204,2002.

[12]. A. D. Amis, R. Prakash, T. H. P. Yuong and D. T. Huynh, " Max-MinD-Cluster Formation in Wireless Ad Hoc Networks", in Proceedings ofIEEE INFOCOM, March 2000.

[13]. A. D. Amis, R. Prakash, " Load Balancing Clusters in Wireless Ad HocNetworks", in Proceedings ofASSET 2000, March 2000.

[14]. A.B.McDonald and T.Znati, " A Mobility based framework forAdaptive Clustering in Wireless Ad hoc Networks", IEEE Journal onSelected Areas in Communications, Vol.17, No.8, Aug.1999.

[15]. S.Banerjee and S.Khuller, " A Clustering Scheme for HierarchicalControl in Multi Hop Wireless Networks", in Proceedings ofIEEEINFOCOM, 2001.

[16]. T.J.Kwon and M.Gerla, "Clustering with power Control", inProceedings of MILCOM,1999.

[17]. V.Kawadia and P.R.Kumar, " Power control and Clustering in AdhocNetworks", in Proceedings of IEEE INFOCOM, April 2003.

[18]. C.R.Lin and M.Gerla, " Adaptive Clustering for Mobile WirelessNetworks", in IEEE J.Selected Areas in Communication, Sep. 1997.

[19]. M.Gerla, T.J.Kwon and G.Pei, " On demand Routing in Large AdhocWireless Networks with Passive Clustering", in Proceedings ofWCNC,2000.

[20]. Soheil Ghiasi et al., "Optimal Energy Aware Clustering in SensorNetworks, Journal of Sensors,2002, vol.2,pp 258-269.

[21]. Chiasserini et al., "An Energy-Efficient Method for Node Assignmentin Cluster-Based Ad Hoc Networks", Wireless Networks, Vol.10,Kluwer Academic Publishers, Netherlands, 2004.

[22]. Ossama Younis and Sonia Fahmy, "Distributed Clustering for Scalable,Long-Lived Sensor Networks," Purdue University, TechnicalReport, CSD TR-03-026, June 2003.