Cluster Head Selection Using Decision Trees forWireless Sensor Networks

Ghufran AhmedCentre for Research on Wireless

Mobility and Networks (CReWMaN)University of Texas at Arlington (UTA)

ghufran@uta.edu

Noor M KhanMuhammad Ali Jinnah University (MAJU)

Department of Electronics EngineeringIslamabad, Pakistan

noormkhan@jinnah.edu.pk

Zubair KhalidGIK Institute of Eng. Sci. & Tech.

Department of Electronics EngineeringTopi,Swabi, Pakistan

zubairkhalid@giki.edu.pk

Rodica RamerUniversity of New South Wales (UNSW)

Sydney, NSW 2052, Australiaror@unsw.edu.au

Abstract— Wireless Sensor Network (WSN) is the hot researchtopic in the civil as well as military applications. Researchersare working in this area to boost it up according to the currentneeds and requirements. An efficient way to enhance the lifetimeof the WSN is to partition the network into distinct clusterswith a high-energy node called gateway as cluster-head. In thispaper we present the cluster head selection scheme based on fourmajor factors. We are using decision tree algorithm to selectthe best node as a cluster head. Simulation results show thatthe performance of this scheme is better than the cluster headselection using both the AHP (Analytical Hierarchy Process) andthe LEACH (Low Energy Adaptive Clustering Hierarchy).

Keywords: Wireless Sensor Network; Network clustering

I. INTRODUCTION

Wireless Sensor Networks (WSNs) is an active researcharea, used in the military as well as civil applications. Oneof the main objectives of WSNs is to monitor a particularregion or environment. It does this by collecting the datafrom the environment, process it and send it to the userthrough gateway. WSNs are resource constrained networks asit consists of sensor nodes with low power batteries which arenot rechargable [1].

Thus in order to save the energy of the WSNs, cluster-based WSN systems are in use. Each cluster is controlled by aspecial powerful node, called cluster head (CH). These clusterheads can communicate directly with the base station (BS), asshown in Fig. 1. Other nodes send their data, sensed from theenvironment to these CHs. CHs first aggregate the data fromthe multiple sensor nodes, and then finally send it directly tothe BS. There are many existing schemes for the selection ofcluster heads in a cluster-based WSN. But most of them donot provide long lifetime system in a dynamic environment,where the sensors are unattended and it is very difficult torecharge their batteries. Therefore, there is a great need of aprotocol which can extend the life of the sensor network.

In addition, current clustering algorithms usually utilize twotechniques; selecting cluster heads with more residual energy,and rotating cluster heads periodically to distribute the energyconsumption among nodes in each cluster and extend the

network lifetime [2]. However, they rarely consider the factthat the nodes which join two or more subnets (a subnet is apart of the entire network) should be avoided to act as CH. It isdue to the fact that the cluster heads are burdened with heaviertraffic and tend to die much faster, leaving areas of the networkuncovered and causing network partitions or disconnection ofthe entire network.

Clustering protocols have been investigated in the contextof routing protocols [3], [4], [5], [6], [7], or independentof routing [8], [9], [10], [11], [12], [13]. In this paper, weonly focus on the issue of CH selection. It is an extensionof the AHP algorithm in [14]. We are applying decision treealgorithm for cluster head selection and introduce a new factor,called vulnerability index, which has already been discussedin [15]. Using this factor, we can avoid those nodes to actas CHs, removal of which renders the network disconnected.We tested the proposed method as a CH selection algorithmand compared it with two other sensor network CH selectionalgorithms, AHP [14](Analytical Hierarchy Process) and theLEACH [16] (Low Energy Adaptive Clustering Hierarchy).The experiments results not only illustrate that the proposedalgorithm could result in long lifetime clusters than otherswith any density of sensor networks, but also that the perfor-mance is more stable, which is also verified through repeatedexperiments.

Decision Trees (DTs) are used in Decision Support Systems.The basic objective of DTs is to choose from various possiblecourses of action. Using DTs, we can layout the differentpossible options in a very effective manner, that leads to theanalysis and investigation of these possible outcomes. DTs alsogive us a balanced picture of risks and rewards associated witheach possible outcome [17].

Our proposed scheme is based on four factors:1) distance of a node from the cluster centroid,2) its remaining battery power,3) its degree of mobility, and4) its vulnerability index.As all of sensor nodes can access the BS for communication

978-1-4244-2957-8/08/$25.00 © 2008 IEEE ISSNIP 2008173

Cluster Head Sensor Node Cluster

Base Station

Fig. 1. Cluster-based Wireless Sensor Network

with the sink node; however, simultaneous connections orcommunications of all of these nodes with the BS wouldresult in the congestion and clogging of bandwidth. Thereforeselection of some responsible nodes (CHs in our case) fromthe specific group of nodes or clusters is made. The soleresponsibility of CHs is thus to communicate the selectedand aggregated data to the BS, exploiting the bandwidthefficiently and avoiding the traffic congestion resulted fromthe broadcasting of the multiple copies of the same data fromindividual nodes.

In the rest of the paper, we define the architectural modeland summarize the related work. A little bit explanationof the four factors that influence network lifetime can befound in Section II. Section III describes the system model;section IV describes our approach to CH selection in sensornetworks and section V describes the implementation of theproposed approach. Description of the simulation environmentand analysis of the experimental results can be found insection VI. Finally, section VII concludes the paper anddiscusses our future research plan.

II. FOUR FACTORS THAT INFLUENCE NETWORKLIFETIME

A brief discussion of four factors is given below:

1) Distance of a node from the cluster centroid: The BScalculates the distance of each node to its cluster r cen-troid. The lesser the distance, the higher the probabilitythat the node will become CH.

2) Remaining battery power: Obviously, the higher thebattery power, the higher the probability that the nodewill become CH.

3) Degree of mobility: The mobility of the node has greatimpact on the network lifetime. The topology of thenetwork will be change very frequently due to the highmobility of nodes, which leads to reselection of CHsrapidly.

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Fig. 2. Logical Network Abridgement [18]

4) Vulnerability index: In order to prevent the networkfrom disconnection, we calculate the vulnerability ofeach node, through which we can minimize the usage ofthose nodes, removal of which leads to the disconnectionof the entire network. Hence, this factor tells us howmuch vulnerable a node is. If it is high for a particularnode, then that node should not be selected as a CH.We can calculate this factor by using the followingprocedure:

Step 1:Transform the non-planar graph into planar. Someexisting approaches are already present in the liter-ature; for example, reduce the weights on the crossover links. This transformation is beyond the scopeof this paper.

Step 2:Finding cycles (loops) in the graph. Replace eachcycle with a logical node. Join the two logical nodesif there is a common edge between them.

Step 3:Repeat step 2 until the graph is cycle (loop) free.Name the graph as level (n-1) after each iteration ofstep 2.

Step 4:Now, we can find the vulnerability factor of eachnode i using the following formula:

IV,i =N i

before

N iafter

× Libefore + 1

Liafter + 1

(1)

Where,

• N ibefore is the number of nodes before removing ith node

• N iafter is the number of nodes after removing ith node

• Libefore is the number of levels before removing ith node

• Liafter is the number of levels after removing ith node.

Intuitively, a node that has high value of IV means themore vulnerable it is. So failure of such node leads to thedisconnection of the entire network. Thus, from the aboveformula, we can conclude that we should avoid those nodes tobe CHs having higher vulnerability (IV ) values, since it leadsto the disconnection of the entire network.

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III. MODEL ASSUMPTIONS

Consider hundreds of sensor nodes dispersed in a field area,where each node belongs to a particular cluster. Each clusterhas one cluster head which manages and controls the overallcluster. Following assumptions are made; most of them aretaken from [14].

• BS is far away from the sensor nodes and stationary.• Sensor nodes are quasi-stationary and energy-constrained.

Also, they are left unattended after dispersion.• Links are symmetric, i.e., two nodes n1 and n2 can

communicate using the same transmission power level.• Cluster: multi-hop• Propagation channel: symmetric

It is important to note that compared with existing models, noassumptions in our model are made about:

• homogeneity of node category,• little mobility of node and• homogeneity of node dispersion radius.

IV. CLUSTER HEAD SELECTION USING DECISIONTREE

Before starting, a certain number of clusters must be con-firmed and set in advance. We can describe our proposedscheme with the help of an example. Let’s label the four factorsas:

X1 distance of a node from the cluster centroid,X2 its remaining battery power,X3 its degree of mobility, andX4 its vulnerability index.

Possible values of the four attributes are set with respectto an intermediate line between their minimum and maximumprobable values. These values are given in Table I.

TABLE I

POSSIBLE VALUES FOR THE ATTRIBUTES

X1 Near FarX2 High LowX3 Low HighX4 Low High

Digital Value 1 0

There are two possible classes and each node can belong toonly one of them:

ClassI:can be a Cluster HeadClassII:cannot be a Cluster Head

A set of training data, obtained from a group of networkexperts, is shown in Table II.

According to the possible values of the attributes (seeTable I) in the light of class description, the digital valuesin binary form are shown in Table III.

Number of 1’s in Table III are then counted and entered inTable IV, under either of two class categories.

We can see that the maximum discriminate attributes areX3 and X4, on the basis of absolute difference between thecorresponding elements.We choose X4 for example. The arcs

TABLE II

TRAINING DATA

Node X1 X2 X3 X4 Class1 Near Low Low High I2 Far High High High I3 Far Low High Low I4 Far Low High High II5 Far High Low Low II6 Near Low Low Low II

TABLE III

BINARY FORM OF THE TRAINING DATA

Node X1 X2 X3 X4 Class1 1 0 1 0 I2 0 1 0 0 I3 0 0 0 1 I4 0 0 0 0 II5 0 1 1 1 II6 1 0 1 1 II

TABLE IV

COUNT OF 1’S IN THE BINARY FORM OF TRAINING DATA

X1 X2 X3 X4 Class1 1 1 1 I1 1 2 2 II

(edges) of the decision tree for X4 are X4=1 and X4=0, shownin Tables V and VI respectively. The related hierarchy isdepicted in Fig. 3.

TABLE V

ARC X4=1

Node X1 X2 X3 X4 Class3 0 0 0 1 I5 0 1 1 1 II6 1 0 1 1 II

TABLE VI

ARC X4=0

Node X1 X2 X3 X4 Class1 1 0 1 0 I2 0 1 0 0 I4 0 0 0 0 II

As a next step, X4 is nullified and X4=1 from Table V isfocused. Number of 1’s in the columns of Table V are countedand entered in Table VII.

TABLE VII

COUNT OF 1S FOR ARC X4=1

X1 X2 X3 X4 Class0 0 0 X I1 1 2 X II

In Table VII, maximum discriminate attribute is selectedagain, which in our case, is X3. The previous procedure isrepeated for X3. The arcs (edges), X3=1 and X3=0, are again

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X4 =1 X4 =0

Fig. 3. Decision Tree in which X4 is selected as the root node

X4 =1 X4 =0

X3 =1 X3 =0

Fig. 4. Decision Tree in which X4 is the root node and X3 is the leaf node

computed and entered in Tables VIII and IX respectively.Related hierarchy is depicted in Fig. 4

TABLE VIII

ARC X3=1

Node X1 X2 X3 X4 Class5 0 1 1 X II6 1 0 1 X II

Observing the entries of all tuples of Table VIII, X3 attributeis regarded as belonging to Class II, which in turn means thatX3 is the leaf node.

TABLE IX

ARC X3=0

Node X1 X2 X3 X4 Class3 0 0 0 X I

Observing the entries of all tuples of Table IX, X3 attributeis regarded as belonging to Class I, which in turn means thatX3 is the leaf node.

The above procedure is repeated again till the completionof the decision tree.

V. IMPLEMENTATION

There are two phases of cluster-based WSN:

1) Startup Phase2) Steady-state Phase

A. Startup Phase

During the startup phase, a random set of nodes is selectedas CHs. At first, the BS broadcasts the inquiry message toevery node of the WSN. Each node in turn replies to theBS via the set of CHs and send their control information.After receiving the control information from each of the sensornodes, BS runs the decision tree algorithm in order to selecta new set of proper CHs among all the sensor nodes. Thenit sends the list of the CHs to each of the sensor nodes. CHsbroadcast the notification to all sensor nodes. After this everysensor node attaches itself with only one CH. This attachmentcan be done by evaluating the received signal strength fromthe CHs. At the end of the startup phase, each sensor nodesends the request for the attachment to a particular CH andCHs in turn broadcast the list of the members of their clustersto other nodes [14].

B. Steady-state Phase

The steady-state phase is divided into frames, in whichnodes send their data to the CH and CHs transfer the collectedand aggregated data to the remote sink node. After the end ofeach round, the role of being a CH rotates to balance theburden of acting as a CH. This CH selection process wouldbe repeated either on prescribed interval basis or by fulfillingthe criteria of a threshold value.

VI. SIMULATION RESULTS

A. Simulation Setup

To investigate the performance and the scalability of theproposed technique, we generate sensor networks with 100nodes in Matlab 7.0, as shown in Fig. 5 and carry out extensivesimulation. The numerical values chosen for our simulationscan be seen in Table X.

TABLE X

DEFAULT VALUES USED IN SIMULATIONS

Parameters ValueNumber of nodes 100

Initial energy 5JCommunication Range 15m

Sensor field size 100x100m2

Data rate 40kbps

In Fig. 6, we compare the DT (Decision Tree) approach withtwo cluster-based protocols: AHP [14] (Analytical HierarchyProcess) and LEACH [16] (Low Energy Adaptive ClusteringHierarchy). The X-axis represents the number of rounds (eachround occurs after a certain period of time, in which basedon the four factors a new CH is selected), while the Y-axisrepresents the number of nodes alive. We see that using DTapproach the nodes remain alive for longer time, improving thelifetime of the whole network. In other words, our approachperforms much better than other approaches in prolonging thenetwork lifetime. The reason behind this improvement lies inthe addition of a fourth decision factor, while other approacheslike LEACH [16] focuses only on energy factor and tries to

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evenly distribute the energy load and ignores other factorslike mobility, vulnerability and distance of CH from centroid.Similarly AHP [14] considers only the three factors (mobility,energy and distance of CH from centroid), and ignores thevulnerability factor of nodes, which is indeed an importantfactor in terms of network lifetime.

VII. CONCLUSION

In this paper, we have proposed an energy-efficient andvulnerability-aware clustering algorithm for WSNs. We haveexploited four factors on which CH selection is based. Ineach round, BS runs the decision tree algorithm and selectsthe nodes suitable for being CHs. Nodes with high energy,low mobility, low vulnerability, their closeness to the clustercentroid are declared to be CHs. Simulation results show thatthe proposed scheme produces well-distributed clusters andthus prolonging the network lifetime.

Moreover, decision tree algorithm depends mostly on thecorrectness of the training data which is usually available fromthe past experiments. The more perfect the training data, themore accurate decision tree we get.

As future work, this work could be extended to achieve per-formance optimization by using Quasi-Centralized Clusteringapproach [19].

ACKNOWLEDGEMENT

The authors would like to thank Dr. Alex Kavokin, Professorin Computer Sciences at GIK Institute of Engineering Sciences& Technology, Topi, Pakistan for his assistance in improvingthe quality of the paper.

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