Multi-objective cluster head election in cluster-based wireless sensor networks

Abstract

Owing to good coverage preservation, cluster-based wireless sensor networks (WSNs) have been widely explored in the context of various potential applications. Yet, selecting the optimal candidates as the cluster heads in clusters that require complete coverage of the monitored area over long periods of time still remains a significant challenge, and needs to be reasonably solved to operate the large-scale WSN systems in an optimal fashion. To address this issue, a multi-objective cluster head election scheme is proposed in this paper, in which, by taking the network coverage and load balance as the optimisation objectives and deducing the objective ranges, cluster head election is conducted as a problem of multi-objectives combination optimisation. And then, by introducing the Metropolis rule of simulated annealing algorithm, an improved particle swarm algorithm is developed to solve this problem, which could restrict the position change of original and new particles in the iteration process and accelerate the convergence speed of the algorithm. The simulation and analytical results are shown to demonstrate the performance on coverage optimisation, and load balance.