Weight Based Clustering In Wireless Sensor Networks

Ravi Tandon, Biswanath Dey and Sukumar Nandi Department of Computer Science And Engineering

Indian Institute of Technology Guwahati Guwahati, Assam 780139

Email: r.tandon@alumni.iitg.ernet.in. {bdey. sukumar}@iitg.ernet.in

Abstract-We study the impact of heterogeneity of energy in hierarchically clustered wireless sensor net­works. Heterogeneity in energy of the sensor nodes can be because of (i) differences in transmission levels of sensor nodes, (ii) differences in initial energies, or (iii) due to introduction of high energy sensor nodes within the sensor network to revitalize it [11]. Clustering protocols divide sensor nodes into groups of member nodes called clusters. Each cluster contains a cluster head. Cluster heads form an independent dominating set. We identify a potential drawback of such a scheme in heterogeneous sensor networks. In a heterogeneous sensor network, electing sensor nodes with high energy (called advanced nodes) which lie within the same transmission radius may be better than electing a single advanced node as cluster head. Cluster heads expend more energy than member nodes during each round. Therefore, election of advanced nodes as cluster heads becomes imperative for the performance of the sensor network. We propose a weighted cluster head election scheme, called Weight Based Clustering for Heteroge­neous sensor Networks (WBCHN) . WBCHN prolongs the stability period (time before the first node dies) through the election of high percentage of advanced sensor nodes as cluster heads.

Index Terms-Clustering algorithms, W ireless sensor networks, Network Lifetime.

1. INTRODUCTION

A wireless sensor network is a spatially distributed autonomous system of sensor nodes used for health moni­toring, environment surveillance, military intelligence etc. A wireless sensor network consists of a Base Station and a set of distributed sensor nodes. Sensor nodes collect data from their environment and send it to the Base Station. The Base Station acts as a sink. Some of the characteristic features of wireless sensor networks such as limitation of resources (processing power, energy ) make them different from other distributed networks. Consideration of network lifetime is essential for any protocol.

Works such as [3], [6], [12], [13] specifically consider homogeneous sensor networks. Homogeneous sensor net­works consist of sensor nodes with similar energy, hard­ware and transmission capacities. ·Works such as [1], [4], [11] consider a heterogeneous sensor network (sensor nodes vary in terms of battery powers) . Such networks generally

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consist of a mixture of high energy sensor nodes (advanced nodes) and low energy nodes (plain nodes) .

To improve network lifetime nodes organize themselves into a hierarchical arrangement to form localized groups called clusters. Each cluster consists of a cluster head and several member nodes. The member nodes collect data and send it to their cluster heads. The cluster head aggregates and transmits the data to the Base Station. The energy consumption of cluster heads is higher than that for member nodes. A heterogeneous energy distribution is proficient for clustered networks. However, a higher percentage of advanced nodes must be elected as cluster heads. The performance of a clustering protocol improves with the election of a higher fraction of advanced sensor nodes as cluster heads.

Heterogeneity in energy distribution is a natural con­sequence of several protocols such as Direct Transmission (DT), Minimum Transmission Energy (MTE) [8], [10]. In MTE sensor nodes which are closer to the Base Station get depleted of their energy faster. In DT sensor nodes farther away from the Base Station get depleted of their energy at a quicker rate. Re-energizing of sensor nodes requires introduction of advanced nodes within the sensor network. This necessitates the requirement of clustering protocols which can use non-uniform distribution of energy in an efficient manner. Heterogeneity in sensor nodes is a natural consequence of variation in energy consumption of sensor nodes. Energy consumption in sensor nodes may vary because of differences in geographical location, radio communication characteristics or randomness in the occurrence of events which they are monitoring.

Heterogeneous networks require different protocols than those for homogeneous networks. Sensor network clus­tering protocols are primarily based on the formation of an independent dominating set of cluster heads. In a heterogeneous environment there may be cases (refer Fig. lc) when two advanced nodes lie within the same neighborhood. We hypothesize that election of both the advanced nodes enhances network lifetime. Furthermore, the election of a low energy sensor node lying in the vicinity of a high energy sensor node must be avoided. We propose a protocol Weight Based Clustering in H eteroge­neous wireless sensor Networks (WBCHN), which utilizes

heterogeneity in a sensor network to extend its (i) lifetime and (ii) throughput by electing better cluster heads in a proficient manner.

Rest of the paper is organized as follows. Section II is a review of clustering protocols for vV SNs which adhere to heterogeneous networks. Section III describes the energy model. Section IV describes the network model. Section VI describes our simulation study. The work is concluded in section VII.

II. RELATED WORK

Significant research efforts have been devoted to the design of network clustering protocols. Sensor Network clustering protocols can be categorized into two broad categories. They are homogeneous [6], [12], [13] and hetero­geneous clustering protocols [4], [11], [13], [14]. Heinzelman [7] analyzed the effect of heterogeneity in wireless sensor networks. Cluster heads are elected in a stochastic and pe­riodic manner. However, the work assumes uniform distri­bution of energy usage within the sensor network through­out network lifetime. Hybrid Energy Efficient Distributed clustering protocol [13] uses a probabilistic method of electing cluster heads. It guarantees a well distributed election of cluster heads. However, election of low energy cluster heads may lead to poor stability period. Generic Clustering algorithms [3] consider residual energy for the election of cluster heads. A neighborhood with more than a single advanced node will elect a single cluster head. It may lead to the election of plain nodes as cluster heads (see Subsection V-A).

Duarte-Melo and Liu [5] consider two type of sensor nodes according to their energy. They assume a set of iden­tical sensor nodes (homogeneous nodes) and an overlay of heterogeneous (high energy) sensor nodes. Using their model, an optimal number of clusters can be elected. The assumption of an overlay formation amongst the advanced nodes reduces the applicability of such a model. Such an assumption may not be valid in a network in which heterogeneity evolves as a consequence of the sensor nodes' uneven energy consumption. Mhatre and Rosenberg [9] provide a cost based comparative analysis of homogeneous and heterogeneous sensor networks. They consider two distinct types of sensor nodes. However, only the advanced nodes can become cluster heads. Such a network formation is not very robust to the failure of advanced nodes.

\¥eight based clustering approaches have been discussed in WCA [3] and DCA [2]. Chatterjee et. al. [3] provide a weight based election of cluster heads. The weights depend upon several factors viz.(i) duration for which a sensor node has been a cluster head, (ii) its proximity to neigh­borhood nodes, (iii) node's mobility and (iv) deviation from average degree. It forms an independent dominating set. Election of an independent dominating set may lead to a suboptimal election of cluster heads as more than one heterogeneous sensor node may lie within the same neighborhood (see Fig. lc).

III. ENERGY MODEL

In this study, we have assumed a simple model for the radio hardware. The transmitter dissipates energy for running the radio electronics to transmit and amplify the signals, and the receiver runs the radio electronics for reception of signals [6]. We use both the multipath fading model (d4 power loss) for large distance transmissions and the free space model (d2 power loss) for proximal transmissions. Thus to transmit an I-bit message over a distance d, the radio expends:

ETx (l, d) = ETx-elec (l) + ETx-amp (l, d) (1)

ETx-elec (l) = I * Eelec (2)

ETx-amp = I * Efs * d2, when d < do (3)

ETx-amp = I * Emp * d\ when d;::: do (4)

Eelec (electronic energy) = 50nJ /bit, e f s (amplification energy for free space model) = 10pJ/bit/m2, emp (am­plification energy for multi path fading model) = 0.0013 pJ /bit/m4, do is the threshold distance. To receive an I-bit message the receiver expends:

(5)

To aggregate n data signals of length l-bits, the energy consumption was calculated as:

EDA-expend = I * n * EDA (6)

Energy required for data aggregation (EDA) is 5nJ /bit/signal. The initial energy of low energy sensor nodes is 0.5 Joule. The high energy sensor nodes have 2.5 Joules of initial energy. The radio channel is assumed to be symmetric, so the cost of transmitting a signal from A to B is same as that of transmitting a signal from B to A.

IV. NETWORK MODEL

\¥e consider a sensor network of area Anet with n

sensor nodes. The sensor nodes are randomly deployed in a circular area around the sink. The Base Station is located at the center of the network. We make the following assumptions about the sensor nodes:

• The sensor nodes are heterogeneous. There are two kinds of sensor nodes, High Energy Sensor Nodes (advanced nodes) and Low Energy Sensor Nodes (plain nodes).

• The sensor nodes can communicate with other nodes using variable power levels, according to the distance with the neighboring nodes.

• The sensor nodes are stationary. • All the sensor nodes within transmission radius (de­

noted by Clusterradius) of each other are considered one hop neighbors.

V. WEIGHT BASED CLUSTERING IN HETEROGENEOUS

NETWORKS-WBCHN

A. Motivation

The objective of W BCHN is (i) to enhance the stability period of sensor network by electing sensor nodes with higher residual energy as cluster heads, (ii) to elect cluster heads in a deterministic manner based on the residual en­ergy of nodes and (iii) to prevent the election of low energy sensor nodes (as cluster heads) which lie in the vicinity of a high energy sensor node. The energy of sensor nodes depletes dynamically therefore static election of advanced nodes as cluster heads (as in [9]) is suboptimal. HEED [13] elects cluster heads stochastically based on the residual energy of the sensor nodes. In a heterogeneous sensor network, sensor nodes with higher residual energy should be elected as cluster heads more often. Protocols such as HEED and GC have certain drawbacks. Firstly, HEED provides no guarantee on the election of high energy nodes as cluster heads. Secondly, HEED and GC protocols form an independent dominating set consisting of cluster heads elected on the basis to their residual energy. However, the issue with such a dominating set formation is that if two or more advanced nodes lie in the same neighborhood then only one of them gets elected. In a heterogeneous environment, it is expected that low energy sensor nodes would die first [11]. In an independent dominating set formation, a node with lesser energy may become the cluster head of a node with more energy(refer Fig. lc). vV BHCN elects cluster heads in a non-independent man­ner and ensures that high energy nodes get elected as cluster heads, even when they lie within the neighborhood of other high energy sensor nodes. This prevents other lower energy sensor nodes from becoming cluster heads. Thus, it enhances the life of sensor network.

B. Design Philosophy

'¥e conjecture that in a heterogeneous distribution of sensor nodes, optimal consumption of energy might not be possible if an independent dominating set of cluster heads is elected. vV BCHN elects sensor nodes that have resid­ual energy more than a fraction of the average residual energy within its neighborhood. This ensures that even if advanced nodes lie in the transmission radius of each other both may get elected as cluster heads, if their residual energy is sufficiently high enough. A major advantage of this scheme is that the probability of low energy sensor nodes getting elected as cluster heads decreases. This reduces the probability of their death. The drawback of such a scheme is that a larger number of sensor nodes may get elected as cluster heads. This might be unnecessary as fewer cluster heads would have been sufficient for serving the entire network. However, the load of data aggregation and forwarding it to Base Station is evenly distributed. Thus, lifetime of sensor network increases.

Fig. lc shows a case where election of an independent dominating set of cluster head may become disadvanta­geous. It consists of two plain nodes LI and L2 and two advanced nodes HI and H2. Either HI or H2 can become cluster head employing protocols based on Independent Dominating Set. Therefore either of LI, L2 has to become a cluster head. Our protocol does not necessitate the election of an independent dominating set of cluster heads. Thus both HI and H2 can get elected as cluster heads. This improves the stability of sensor network.

C. Proposed Protocol

'¥eight Based Clustering for heterogeneous sensor net­works takes into account (i) the residual energy, (ii) number of live neighbors of a sensor node and (iii) its distance from the Base Station. vV BCHN uses a forecast based method to approximate the residual energy present in each sensor node after the current round. A sensor node elects itself as a cluster head if it has residual energy greater than the average residual energy amongst its neighborhood. The only exception to this rule of is if the residual energy after becoming a cluster head becomes lesser than the minimum energy (Emin) in a sensor node (Emin is the threshold energy below which a sensor node becomes dead). At the end of each round every sensor node broadcasts a "I am alive" message to all its neighbors. This helps each sensor node predict the number of live neighbors accurately. Each node then approximates the energy usage for the current round and broadcasts a weight (residual energy remaining after the current round). This helps in the election of cluster heads with higher residual energy. This is calculated as follows:

Here, (NCMNeighborhoodCount) is the number of alive nodes in the neighborhood of sensor node with id i.

if (DtoBS)i ?: do

(CosttoBS)i = l * cmp * (DtoBs); (9)

Sensor nodes that become cluster heads broadcast an advertisement. This advertisement contains the residual energy of the cluster head. Sensor nodes join the cluster head with the highest residual energy in their neighbor­hood.

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4 Ratio of Energy of Advanced Nodes To Normal Nodes Ratio of Energy of Advanced Nodes To Normal Nodes

(a) (b) (C )

Fig. 1: (a) Shows the fraction of cluster heads that are advanced nodes as a function of a (b) Comparison of the ratio of average energy of cluster heads to the average energy of the nodes alive (c) Case when two advanced nodes lie in a common neighborhood and only one gets elected by an Independent Dominating Set Protocol

VI. EXPERIMENTAL EVALUATION

In this section we evaluate the performance of the W BCHN protocol via simulations. vVe have performed three different sets of simulation studies to compare the performance of W BCNH, HEED and GC (Generic Clus­tering based on residual energy ) . The study takes into account (i) variation in cluster radius (the radius of each cluster) , (ii) heterogeneity of sensor nodes and (iii) vari­ation in the ratio of initial energy of advanced and to that of plain nodes. The size of the sensor network is 1002 m2, with 100 nodes distributed randomly. Advanced nodes have 2.5J and plain nodes have 0.5J of initial energy. For the first experiment we vary the cluster radius from 5 to 105 m, uniformly over 20 different values. Let frac be the percentage of total nodes which are advanced nodes. For the second experiment we vary frac from 5 to 100, uni­formly over 20 different values. For the third experiment we vary the ratio (a) of initial energy of advanced nodes to that of the plain nodes. Every result is an average of 20 experiments. vVe study (i) cluster head characteristics, (ii) throughput and (iii) lifetime of the sensor network. The power attenuation is proportional to the square of distance covered by a signal. We have neglected the effects of obstacles, interference etc. on signal transmission. Each experiment uses a different randomly generated topology. The analysis does not take into account the overhead of energy consumption during the formation of clusters since it is minimal. The simulations have been done using Matlab.

A. Cluster Head Characteristics - Fraction of cluster heads that are advanced nodes

,;Ve compare the fraction (fraction) of cluster heads that are advanced nodes for W BCHN, GC and HEED. Let a be the ratio of initial energy of an advanced node to that of a plain node. The average values of fraction for W BCHN, HEED and GC are 0.87, 0.85 and 0.57 respectively. Election of advanced nodes as cluster heads is essential for the enhancement of stability period of a sensor

network. ,;V BCHN elects advanced nodes as cluster heads with a higher probability primarily because advanced nodes get elected as cluster heads. Some of the advanced nodes may become members of other clusters in GC and HEED. Fig. 1a shows fraction as a function of a. vVe vary the parameter a from 5 to 9. We observe that W BCHN elects 6% and 16% more advanced nodes than HEED and GC, respectively. Election of normal sensor nodes as cluster heads results in early death of network as energy requirement of cluster heads is higher. However, advanced nodes cannot be statically elected as cluster heads because their energy may become lesser than that for a plain sensor node. In such a case election of a plain sensor node is better.

B. Cluster Head Characteristics - Average Cluster Head Energy

Cluster head energy improves as the percentage of high energy sensor nodes increase. vV BCHN's success lies in its ability to elect cluster heads with a higher average residual energy. The average energy of cluster heads in ,;V BCHN is 60% higher than those elected in HEED and GC, averaged over a variation in cluster radius. W BCHN improves the cluster head energy by 17% in comparison to HEED and GC when heterogeneity is varied (see Fig. 2a) .

C. Throughput - Total Data Sent To Base Station

,;V BCHN transmits almost 3.37 times more data than HEED and GC to the Base Station averaged over a variation in cluster radius. A higher stability period cor­responds to a higher throughput. W BCHN improves the stability period by about 3.7 times over HEED and GC averaged over a variation of cluster radius. Since a large part of the sensor network remains alive for a longer period of time the overall data collected is significantly large. W BCHN improves the overall data transmitted to the Base Station by 24% when heterogeneity is varied (see Fig. 2b) . This suggests that W BCHN utilizes the heterogeneity in energy better than HEED and GC.

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Fig. 2: Comparison of average cluster head energy, throughout and stability period as a function of heterogeneity. (a) Shows the average cluster head energy as a function of percentage of advanced sensor nodes. (b) Shows the throughput of the sensor network as a function of percentage of advanced sensor nodes. (c) Shows the stability period of the sensor network as a function of the percentage of advanced sensor nodes in the network.

D. Lifetime - Stability Period

W BCHN improves stability period by 3.7 as compared to that of HEED and GC over a variation in cluster radius. The probability of a cluster head death is higher during the stability period due to uneven energy consumption between a member node and a cluster head. Higher clus­ter head energy prolongs the network stability period. W BCHN improves the stability period by 19% over GC and 3.7 times over HEED when heterogeneity is varied (see Fig. 2c) . In general the stability period improves as the percentage of high energy nodes increase.

VII. CONCLUSION

In this work we have proposed an energy efficient clustering protocol called Weight Based Clustering Hier­archical In Wireless Sensor Networks (WBCHN) for dis­tributed ad-hoc sensor networks. Sensor nodes with more than average residual energy (in their neighborhood) get elected as cluster heads. Simulation results indicate that W BCHN enhances the stability period and throughput by electing better cluster heads than other competitive clustering protocols. We have shown that the election of an independent dominating set of cluster heads may not always be beneficial for the sensor network. Election of sensor nodes with high residual energy, even when they lie within the same neighborhood, can lead to better performance. We are currently working on extending this protocol to a homogeneous environment.

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