A. Abd Manaf et al. (Eds.): ICIEIS 2011, Part III, CCIS 253, pp. 498–507, 2011. © Springer-Verlag Berlin Heidelberg 2011

Cluster - Head Selection by Remaining Energy Consideration in a Wireless Sensor Network

Norah Tuah, Mahamod Ismail, and Kasmiran Jumari

Department of electrical, electronic and system engineering, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, 43600, Malaysia

{norah,mahamod,kbj}@eng.ukm.my

Abstract. Energy competence is a very important study in order to find ways to prolong the lifetime of a wireless sensor network. Therefore a good routing protocol and mechanism need to be design. Cluster based architecture is a well known method to optimize the energy competence in the network and have been applied in LEACH routing protocol. However the LEACH routing protocol that used a round concept have a problem because each node will suffer its rest energy in the current round and will die in the next round due to insufficient energy management in the network. Then, we make an alteration of LEACH’s cluster-head selection algorithm by considering an outstanding energy available in each node in order to extend the lifetime of the network. It is known as the Residual Energy (ResEn) algorithm. Consequently, at the end of this paper a comparison analysis for LEACH and ResEn has been simulated using Matlab. As a result, it shows that ResEn algorithm can extended the lifetime of the network.

Keywords: Energy, Cluster-based routing protocols, Wireless Sensor Networks.

1 Introduction

Wireless Sensor Networks (WSNs) are made up of many sensor nodes which work together in data transmission throughout the network. Each of the sensor nodes can sense environmental phenomena such as temperature, sound, wind, and pollution at different locations. So it has been widely used in military, environment, health, home and commercial application. However, each node in the wireless sensor network consumes more energy during data transmission compared to for sensing and computation. Therefore, the node required transmission power grows exponentially with an increase in transmission distance [1]. In order to prolong the network lifetime the amount of traffic and transmission distance has to be considered.

Data transmission over a wireless networks can be use a single hop or multi hop scheme. For short distance a single hop scheme is more practical then multi-hop distance. However, a multi-hop scheme that transmit data by each intermediate hop is more practical for long-data transmission which less costly in terms of energy consumption. A multi-hop scheme may be organized into flat and hierarchical architecture. In a flat network, each node uses its peer nodes as a relays when

Cluster - Head Selection by Remaining Energy Consideration in a WSN 499

communicating with the sink as shown in Fig. 1. Some examples of flat routing protocol are Flooding, Gossiping, Sequential Assignment Routing (SAR), Directed Diffusion and Sensor Protocol for Information via negotiation (SPIN).

In a hierarchical network, sensor nodes are structured into clusters, each member node in the cluster will send their data to the cluster heads which serve as relays for transmitting the data to the sink. Low Energy Adaptive Clustering Hierarchy (LEACH), Power Efficient Gathering in Sensor Information System (PEGASIS), Threshold Sensitive Energy Efficient sensor Network protocol (TEEN) etc is an example of hierarchical routing protocol. Fig. 2 and Fig.3 shows an example of two types of hierarchical architecture according to the distance between the cluster members and their cluster head.

Fig. 1. Flat Network architecture

Fig. 2. Single-hop clustering architecture

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Fig. 3. Multi-hop clustering architecture

1.1 Related Works

A cluster-based wireless sensor network has been the subject of widespread studies by considering the energy competence as the main focus of many clustering protocols proposed so far. Heinzelman et al. [2] were among the first researchers who worked on the cluster- based networks. They proposed a routing protocol with self-organizing and adaptive clustering that used randomization to distribute the energy load among the sensors in the network which was called Low-Energy Adaptive Clustering Hierarchy (LEACH). It used a localized coordination to enable scalability and robustness for active networks. It applied a data fusion in the network to reduce the amount of information that must be sent to the base station.

M.J.Handy et al [3] modified this LEACH protocol by extending LEACH’s stochastic cluster head selection algorithm using a deterministic component. With this selection method, the nodes only need local information and no global information (communication with base station) is necessary to become the cluster-head. With this modification, the network lifetime has been increased to 30%.

M.S.Ali et al. [4] proposed selecting the highest energy node as the cluster head to ensure that all nodes die at approximately the same time. This can be achieved by introducing new threshold equation of cluster head selection called general probability and current state probability. As a result, the death rate of the nodes is reduced which in turn prolongs the lifetime of the network.

M.C.M.Thein et al. [5] customized the LEACH’s stochastic cluster head selection algorithm according to the residual energy of a node in relation to the residual energy of a network. Their proposed model can stabilize the energy in the network, prolonging the network’s lifespan.

X.L.Long et al. [6] made an improvement algorithm which was based on multi-hop LEACH cluster head (LEACH-M) algorithm by considering current node energy taking into account in the cluster head election. Selecting the nodes with huge energy as the cluster head can resolve the problem of nodes with less energy being selected as the cluster head. This improved algorithm effectively extended lifetime of the network.

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2 The Developed ResEn Algorithm

In this section we describe ResEn algorithm, which improve the lifetime of the network. Generally ResEn algorithm is based on deterministic cluster-head selection [3] which inclusion of the remaining energy level available in each node. Consequently the network model, radio dissipation energy model and the working procedure have been explained in the following part.

2.1 Network Model

Some assumptions behind the implementation of this algorithm is:

1. The sensor node is homogeneous. 2. The BS located is fixed with far distance from the network area. 3. Immobility of sensor nodes.

2.2 Radio Energy Dissipation Model

A free-space energy model as defined in [7] was used, whereby the power expended conveying a k-bit message per distance d is calculated by equation 1, while power expended in receiving a k-bit message is calculated by equation 2. We assumed that the sensor nodes could make an adjustment to their transmission power based on the distance of the receiving node.

ET(k,d) = k ( ETx-elec + εamp.d2) (1)

ER(k) = k (ERx-elec) (2)

ETx-elec and ERx-elec means that the power dissipated to operate the transmitter or receiver circuitry and εamp is the power for transmitting the amplifier.

2.3 The Working Procedure

The algorithm operation can be split into three different phases which are cluster head selection, cluster creation and data transmission. All the phases are explained as follows:

a. Cluster head selection Each n node has a chance to be selected as the cluster head in each round. It will choose a random number between 0 and 1. If the selected random number is less than the threshold T(n), the node becomes a cluster-head for the present round. The threshold T(n) was calculated using the equation 3 below.

0 (3)

Where p is the preferred percentage of cluster heads, r is the current round, Ecur is the nodes’ current energy, Einit is the initial energy of the node and G is the set of nodes that have not become as cluster-heads in the last 1/p rounds. The algorithm for cluster head selection is shown in Fig. 4 below. The definition for terms used in the algorithm

502 N. Tuah, M. Ismail, a

is MaxInteral = total roungenerate threshold value of0 and 1 and Broadcast_ccluster head i

for round = 0 to MaxInte for every node i N if node i was CH in T(i) = 0 elseif Random (1,1) Broadcast_Cluster( end if end for end for

Fig.

b. Cluster creation After the cluster head nodenew cluster head to the othmessage which contains thID. The common nodes wiof the advertisement sigcorresponding cluster-headmessage from each node mschedule, informing each nformation is shown in Fig. Number of nodes in the nmessage and Follow_clstr_

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for every node i if node is CH Broadcast Hea Wait for follow End if end for for every node i if node is not CH Receive all He Compute the d Choose the CH End if End for

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d, NodeNums = Number of nodes in the network, T(if node i, Random (1,1) = generate random number betwluster(i) = broadcast cluster announcement message

eral NodeNums round then

) < T(i) then (i)

4. The cluster head selection algorithm

e is determined, the cluster head will advertise itself as her common nodes (not cluster head). It will broadcast he information qualifying itself as the cluster-head andll decide which cluster to follow according to the stren

gnal by sending a follow-req message back to d. After the cluster head has received the follow-

member in its cluster, the cluster head will create a TDMnode when it can transmit data. The algorithm for clu5 below. The terms used in the algorithm are NodeNumnetwork, CH = cluster head, Head_msg = Cluster hmsg = Following cluster message

Fig. 5. The cluster creation algorithm

deNums

msg ster

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msg nce for each CH th min(distance) and broadcast follow_clstr_msg

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c. Data transmission Data transmission starts after the cluster is formed and the TDMA schedule is fixed. In this work, a 10 TDMA frames each round have been set to reduce clustering cost. The cluster head will combine data from all common nodes in its cluster before sending it to the base station. It requires a high-energy consumption for transmission the data to the base station which is located far away.

3 Simulation and Results

Table 1 shows the parameters that have been used in the simulation using MATLAB.

Table 1. Lists of simulation parameters

Parameter Value The size of the network [0,150]2 Number of sensor nodes 100 Location of BS [75,200] Original Energy 2J Eelec 50 nJ/bits Εamf 100 pJ/bit/m2

Data Size 2000 bits Probability 0.05 Communication range 10m

We simulated the network for 1000 rounds and calculated the average lifetime, the

energy consumption in each round and the average remaining energy for the cluster head. Communication between sensors and their cluster head and between cluster heads to base Station was single-hop. The radio model was similar to that of [2], in which Eelec = 50 nJ/bits, Εamf = 100 pJ/bit/m2 and data size was 2000 bits.

To analyze the performance of ResEn algorithm, we compared it with LEACH. LEACH is a routing protocol with self-organizing and adaptive clustering that uses randomization to distribute the energy load among the sensors in the network. Fig. 6 shows the energy dissemination for each node during the setup phase. The setup phase occurs throughout the cluster head selection and cluster formation. The node uses energy to receive and transmit data. From the graph, it shows that the ResEn , as an algorithm with remaining energy among cluster node members consideration, has a better energy consumption capacity compared to LEACH.

Fig. 7 shows the average remaining energy of the chosen cluster head nodes over time. The LEACH graph was decreased slightly until it reached the minimum average remaining energy of 0.2 J after 500 rounds. For ResEn, the graph was decreased until it reached the minimum average remaining energy between 1 J to 0.6J after 300 rounds. When LEACH is used, it is not considered the remaining energy in the network during selecting the nodes as the cluster head. Comparatively, ResEn, which considers the remaining energy in the network in selecting the cluster head nodes, has shown a better performance than LEACH.

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Fig. 6. Energy dissemination for each node during the setup phase

Fig. 7. Average remaining energy of the cluster head

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Fig. 8 shows the comparison of the lifetime of the nodes of both routing protocols after 1000 rounds. According to this graph, ResEn may expand the lifetime of the network longer than LEACH. In LEACH, each time a node becomes a cluster head, it dissolves the same amount of energy. As a result, it leads to inefficient selection of heads which depletes the network faster.

Fig. 8. Number of live sensors

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Fig. 9 shows the comparison of energy consumption with respect to the number of rounds for both protocols. The energy consumption decreased with the reduction of the number of live nodes with each round (as shown in Fig.8), nodes that transmit data are reduced in number. This indicates that ResEN is more energy efficient compared to LEACH.

According to Fig. 10, if the number of TDMA frames is increased to 20, the network lifetime is reduced to almost half. It occurs because the cluster head has to send more messages to the sink during each round. So the cluster head has to use twice the amount of energy in each round. From the graph, it shows that the ResEn graph decreased earlier than LEACH before it went back to its normal ability to extend the network’s lifetime after 450 rounds.

Fig. 10. Number of sensors alive for TDMA with 20 frames

4 Conclusion

The cluster head generation algorithm with the original LEACH clustering protocol may lead to the redundancy of cluster heads in a small region which causes a significant energy loss. To overcome this problem, residual energy has been consider during cluster head selection algorithm in this paper. As a result, it shows that ResEn algorithm can extended the lifetime of the network. For future work, we plan to do some consideration on the network as: 1. In order to increase the lifetime of the network, we will work in intra and inter- cluster communication (Hierarchical architecture) 2. The improvement of our proposed algorithm by combining different approaches introduced by other researchers such as distance, voting-based clustering, optimal cluster number selection and others. 3. Network coverage consideration in a cluster head determination for wireless sensor networks. Acknowledgments. We would like to thank the reviewers for their comments. This research was supported by research grant UKM-OUP-ICT-36-185/2011 and Universiti Teknologi MARA Malaysia.

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References

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