IMPROVING NETWORK LIFETIME USING HIGH ENERGYFIRST CLUSTERING

J. SASIKALA1, N. GOBINATHAN2 & S. P. THILIBAN2

1Assistant Professor, 2PG Scholar, Department of Computer Science and Engineering, Annamalai University,Annamalai Nagar-6008002, India

(E-mail: sasikala_07@rediffmail.com, gobiruban.net@gmail.com)

Abstract: Network lifetime predictability is an essential system requirement for the type of wireless sensor network(WSN) used in safety-critical and highly-reliable applications. Energy efficiency is a very important issue for sensornodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetimethe whole network is divided into different clusters. In this paper, the High Energy First (HEF) clustering algorithmis chosen as a design reference model that is proved to be an optimal clustering strategy under certain ideal conditions.Efficient way to enhance the lifetime of the system is to partition the network into distinct clusters with a high energynode as cluster head. The network simulator 2 (NS2) is used to verify the proposed network lifetime predictabilitymodel, and the results show that the proposed model enhances the system lifetime.

Index Terms: Cluster head selection, network lifetime, timing constraint, Wireless Sensor Network.

1. INTRODUCTION

Wireless Sensor Networks (WSNs) comprise agreat number of nodes with sensing, computing,and wireless communication capabilities. Often,when WSNs are used in safety-critical or highly-reliable applications, lifetime constraints areconsidered. Network lifetime is most widelydefined as:

• The time span from the deployment of thenetwork to when the first node runs outof energy.

• The time duration from the deploymentof the network to when a certainpercentage of the nodes die due to energyresource exhaustion.

• The time taken from the deployment ofthe network to when the network is notable to fulfill designed requirements suchas coverage, packet loss, and connectivity.

A hard K-of-N lifetime requirement for a WSNmeans that the system must guarantee that atleastK nodes are active at each round during the periodfrom the start of operation to the end of thedesignated lifetime. In certain sensor networks,the base station is fixed and located far from the

sensors. In addition all nodes in the network arehomogeneous and energy constrained.

Several methods such as leach and theclustering algorithms for lifetime enhancement ofthe networks have been suggested in the recentyears. Due to random selection of cluster headsby leach methods, the packets may loss due tolower energy, more traffic occurs and the timedelay increases. In this paper, an attempt is madeto develop a new algorithm based on HEF with aview to overcome the drawbacks of the existingapproaches. The proposed strategy lowers therouting table size, reduces the redundancy ofexchanged messages, minimizes the energyconversion and extends the network’s lifetimethrough partitioning the network into severalclusters.

2. PROPOSED WORK

There are two different kinds of sensors in thenetwork. One sensor acts as the cluster head andother sensors play the role of cluster members.The message is transmitted from the clustermembers to the cluster head. Each cluster memberquantizes its observation to bits and sends thequantized data to the cluster head. The cluster

I J I T E • ISSN: 2229-73673(1-2), 2012, pp. 371-374

372 J. Sasikala, N. Gobinathan & S. P. Thiliban

head performs source extraction based on its ownobservation and the received quantized data fromthe cluster members. However, the distributionsof sources are not specified in this work so asto accommodate different kinds of sources.Figure 1. Shows the hierarchical clustering WSNs.

• All sensors perform its sensing andprocessing and communication taskscooperatively at this clock cycle (round).

• Each sensor sends its energy informationto its cluster head at the end of this clockcycle.

• Upon collecting cluster members’information at a given period, the clusterhead sends the summative report to thebase station.

Figure 1: Environment of the Hierarchical ClusteringWSNs (HC-WSN).

HEF is a centralized cluster selectionalgorithm but it can also be implemented in adistributed fashion with the synchronizationapproach. It selects a set of M highest rankingenergy residue sensors for cluster heads at round�, where M denotes the required cluster numbersat round �. Figure 2 depicts the information flowof the centralized HEF system. Each roundcomprises the following three phases: ClusterHead Selection (CHS) Phase, Cluster Formation(CFM) Phase, and Data Communication (DCM)Phase. The interactions and detailed operationsbetween components are summarized below,

• The cluster head of each group broadcaststhe “setup” message inviting theneighbour sensor nodes to join its group.

• After receiving the “setup” message atthis round, the regular sensors send the“join” message to its correspondingcluster head to associate with the group.

• Each cluster head acknowledges thecommitment, and sends TDMA scheduleto its cluster members.

Figure 2: Information Flow of the Centralized HEF System

In the Deployment Planning stage, efforts aremade to plan the shape of the network topology.In Energy Estimation stage, the initial energy andresidue energy level of sensor nodes are toperform the necessary configuration. It comprisesthe following three phases:

• Cluster formation• Cluster head selection• Data communication.

2.1. Cluster Formation

Desired number of clusters, each comprisingcluster head and cluster members, are to beformed in the square region.

2.2. Cluster Head Selection

HEF selects cluster heads according to the energyremaining for each sensor node, and then the“setup” message is sent to the cluster head of eachcluster. The cluster head may be special node with

Improving Network Lifetime Using High Energy First Clustering 373

higher energy or normal node depending on thealgorithm and applications. Each cluster headperforms computational functions such as dataaggregation and data compression in order toreduce the number of transmission to the basestation (or sink) thereby saving energy. Clusteringalong with reduction in energy consumptionimproves bandwidth utilization by reducingcollision.

2.3. Data Communication

All sensors perform its sensing, processing andcommunication tasks cooperatively at this clockcycle (round). Each sensor sends its energyinformation to its cluster head at the end of thisclock cycle. Upon collecting cluster membersinformation at a given period, the cluster headsends the summative report to the base station.

3. SIMULATION RESULTS

The proposed method has been tested in anetwork comprising 23 nodes. The nodes areorganized in a random topology and deployed ina square region. The base station is located at aposition of (50,180). Figure 3 shows the

throughput that defines the average rate ofsuccessful message delivery over a communicationchannel of the proposed method, which clearlyindicates that the proposed method offersincreased throughput. Figure 4 shows the graphindicating network lifetime at different values ofmean and variances. It is seen from this figure thatthe proposed method increases the networklifetime.

4. CONCLUSION

Providing a trustworthy system behavior with aguaranteed hard network lifetime is a challengingtask to safety-critical and highly-reliable WSNapplications. It is important to be aware ofwhether all sensors can meet their mandatorynetwork lifetime requirements for missioncritical WSN applications. The issue of thepredictability of collective timeliness for WSNs ofinterests is addressed in this paper. The HEFalgorithm is found to be an optimal cluster headselection algorithm that maximizes a hard K-of-N lifetime for HC-WSNs under the ICOHcondition. HEF algorithm achieves significantperformance improvement in terms of lifetimeover LEACH.

ACRONYM

CFM Cluster FormationCHS Cluster Head SelectionDCM Data CommunicationHC-WSN Hierarchical Clustering WSNHEF High Energy FirstICOH Ideal Conditions for Optimality of

HEFLEACH Low-Energy Adaptive Clustering

HierarchicalLEACH-C LEACH-CentralizedNs2 Network Simulator 2TDMA Time Division Multiple AccessWSN Wireless Sensor Network WSNs Wireless Sensor Networks

References[1] Abhyankar, S., D.P. Agrawal, “Distributed Mobility-

aware Route Selection for Wireless Ad hoc Networks”,Proceedings of the 4th IEEE Computing andCommunications Conference, pp. 241–247, 2002.

Figure 3: Throughput of the Proposed Strategies

Figure 4: Increasing Network Lifetime

374 J. Sasikala, N. Gobinathan & S. P. Thiliban

[2] Camp, T., J. Boleng. and V. Davies, “A Survey ofMobility Models for Ad hoc Network Research WirelessCommunications & Mobile Computing (WCMC),Special Issue. To appear. Charles, E., 2003. Perkins,editor. Ad Hoc Networking. Addison-Wesley, 2003.

[3] Dube, R., C. D. Rais, K.Y. Wang, S. K. Tripathi, “SignalStability-based Adaptive Routing (SSA) for Ad hocMobile Networks”, IEEE Personal Communications, pp.36–45, 1997.

[4] Johnson, D. B., D. A. Maltz, “Dynamic Source Routingin Ad Hoc Wireless Networks”, Kluwer, 1996.

[5] Ko, Y.B., N.H. Vaidya, ”Location-aided Routing inMobile Ad hoc Networks”, ACM Wireless Networks,6(4), pp. 307– 321, 2000.

[6] Kim, D., C.K. Toh, Y. Choi, “Location-aware Long-lifeRoute Selection in Wireless Ad hoc Networks”, 2000.

[7] Kim, W. I., D.H. Kwon, Y. J. Suh, “A Reliable RouteSelection Algorithm Using Global Positioning Systemsin Mobile ad-hoc networks”, Proceedings of the 2001IEEE International Conference on Communications, pp.3191–3195.

[8] Kyoung-Jin, K.I.M., Y.O.O. Sang-Jo, “Power-EfficientReliable Routing Protocol for Mobile Ad-HocNetworks”, The Institute of Electronics, Information andCommunication Engineers, 2005.

[9] Liao, W.H., Y.C. Tseng, J.P. Sheu, GRID, “A FullyLocation-aware Routing Protocol for Mobile Ad hocNetworks”, Telecommunication Systems, pp. 37–60, 2001.

10 Murthy, S., J.J. Garcia-Luna-Aceves, “A RoutingProtocol for Packet Radio Networks, Proceedings ofACM First International Conference on MobileComputing and Networking”, Berkeley, CA, USA,November, pp. 86-95, 1995.

[11] Murthy, S., J. J. Garcia-Luna-Aceves, “An EfficientRouting Protocol for Wireless Networks, ACM, 1996.

[12] Murthy, S., J. J. Garcia-Luna-Aceves, “A RoutingProtocol for Packet Radio Networks”, Proceedings,1995.

[13] Perkins, C. E. and E.M. Royer, “Ad-Hoc On-DemandDistance Vector Routing (Internet-Draft),” 1999.

[14] Perkins, C.E., P. Bhagwat, “Highly Dynamic DestinationSequenced Distance-vector Routing (DSDV) for MobileComputers”, Proceedings of the ACM Special InterestGroup on Data Communication, London, UK, and pp.234–244, 1994.

[15] Perkins, C. E., E. Royer, “Ad-hoc on-demandDistance vector routing”, Proceedings of theSecond IEEE Workshop on Mobile Computing Systemsand Applications, New Orleans, LA, USA., pp. 90–100,1999.