Decomposition-based Multi-Objective Evolutionary Optimization for Cluster-Head Selection in WSNs

Abstract

A Wireless Sensor Network (WSN) is composed of a set of energy- and processing-constrained devices that gather data about a set of phenomena (e.g., temperature, humidity, pressure, etc.). An efficient way to extend the lifetime of a WSN is using a clustering organization, which hierarchically structures the sensors in groups and one of them is chosen as a cluster head (leader). Such cluster head performs specific tasks such as gathering data from other cluster sensors (cluster members) and resending this data through the network to the base station. By using a cluster-head organization, data gathering process is improved and, by extension, the network lifetime is extended. In this paper, we formulate a multi-objective approach for the optimal cluster-head selection in a WSN aiming to minimize: i) the distance from the cluster head to the base station, ii) the distance from cluster members to their leader, and iii) the residual energy of the leaders. The goal of our study is two-fold. First, as currently it is not known the conflict relation among those objectives, we carry out an analysis to discover which objectives are essential for solving the cluster-head selection problem. Second, we investigate the performance of multi-objective evolutionary approaches based on decomposition and the impact of their main parameters when solving the multi-objective cluster-head selection problem.