A Novel Random Transition Based PSO Algorithm to Maximize the Lifetime of Wireless Sensor Networks

The lifetime of a wireless sensor network (WSN) is a critical aspect as, in most of the applications, it is not possible to replace or recharge the batteries of the sensor nodes. The lifetime of a WSN with redundant deployment can be significantly increased by dividing the sensors into disjoint sets such that each of these sets, when operated independently, provides complete coverage of the targets. In order to maximize the lifetime of the network, the maximum possible number of such sets needs to be created. The problem has been proved to be nondeterministic polynomial complete. In this paper, a hybrid approach based on combining particle swarm optimization (PSO) with random transition moves has been proposed to address this problem. A swarm of randomly initialized particles explores the entire solution space in search of an optimum solution. Three novel random transition moves have been designed to exploit the redundancy in deployment of sensors and used to guide the randomly scattered particles towards the potential optimum solutions in their neighborhood. The transition moves escalate the convergence of the algorithm. The proposed algorithm has been tested both for point coverage and area coverage applications. To authenticate and validate the results, the comparison of the results is performed with the latest existing techniques. The proposed algorithm always finds the optimum solution by making fewer fitness function evaluations. The sensitivity analysis of the control parameters has also been performed.