Optimal Number of Cluster Head Selection for Efficient Distribution of Sources in WSNs

Abstract

In this paper, we compare problems of cluster formation and cluster-head selection between different protocols for data aggregation and transmission. We focus on two aspects of the problem: (i) how to guess number of clusters required to proficiently consume available sources for a sensor network, and (ii) how to select number of cluster-heads to cover up sensor networks more proficiently. A sensor in Wireless Sensor Networks (WSNs) can communicate directly only with other sensors that are within a radio range in a cluster. However, in order to enable communication between sensors not within communication range, they must form new clusters in distributed sensors. Several clustering algorithms such as LEACH, DEEC, and SEP have been proposed with the objectives of energy minimization, route-path selection, increased connectivity and network longevity. LEACH protocol and the similar ones assume an energy homogeneous system where a node is not likely to fail due to failure in connectivity and packet dropping. More recent protocols like SEP and TEEN considered the reverse that is energy heterogeneity which is more applicable to case of WSNs. We developed a bi-dimensional chain model to select average number of for DEEC. Simulation results are used to compare performance of different protocols to found optimal solutions of above mentioned problems.

Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks

“…The number of CHs selected by using randomized schemes [7,8,9,10,11], is not guaranteed to be equal to the expected optimal value. To analyze the clustering properties, and to address the problems in cluster formation, we employed a bi-dimensional Markov chain model [21,26] to inspect their cluster-forming behavior. We use the Markov model to analyze the stochastic properties of MOCHs for the number of CHs in terms of the PMF, the mean, the SD, and the COV of the number of CHs.…”

“…Controlling the size of the cluster is also important for balancing the load. In the proposed mechanism, we use the Markov chain model [21] to select an optimal number of CHs in the network according to the number of available sensor nodes for optimal resource utilization. In this designed framework, we also limit the number of $C_{MNs}$ in a cluster to divide the energy overhead and other CH-related responsibilities.…”

“…The current clustering protocols [7,8,9,10,11,12] utilize the distributed algorithm for CH selection that increase the computational overhead and also causes the resources to drain very quickly. We use Markov Bi-directional Chain Model (MBCM) [21,26] to equally divide the sensing area into clusters, and to examine the behavior of the cluster formation process. We also used this formulation [21,26] for optimal CH selection and to analyze the stochastic characteristics of the MOCHs in the sense of the mean value, the probability mass function (PMF), the standard deviation (SD), and the coefficient of variation (COV) for the optimal selection of CHs.…”

“…We use Markov Bi-directional Chain Model (MBCM) [21,26] to equally divide the sensing area into clusters, and to examine the behavior of the cluster formation process. We also used this formulation [21,26] for optimal CH selection and to analyze the stochastic characteristics of the MOCHs in the sense of the mean value, the probability mass function (PMF), the standard deviation (SD), and the coefficient of variation (COV) for the optimal selection of CHs. Through this formulation [21,26], the obtained CHs are optimal in number and well-distributed all across the network, which leads to higher energy efficiency, better fairness among nodes, and prolonged the network lifetime.…”

“…We also used this formulation [21,26] for optimal CH selection and to analyze the stochastic characteristics of the MOCHs in the sense of the mean value, the probability mass function (PMF), the standard deviation (SD), and the coefficient of variation (COV) for the optimal selection of CHs. Through this formulation [21,26], the obtained CHs are optimal in number and well-distributed all across the network, which leads to higher energy efficiency, better fairness among nodes, and prolonged the network lifetime. In our proposed clustering protocol, each sensor node consumes energy equally by revolving the CH’s responsibility between all the sensor nodes.…”