An efficient partial coverage algorithm for wireless sensor networks

Abstract

Wireless sensor networks (WSNs) are currently adopted in a vast variety of domains. Due to practical energy constraints, in this field minimizing sensor energy consumption is a critical challenge. Sleep scheduling approaches give the opportunity of turning off a subset of the nodes of a network- without suspending the monitoring activities performed by the WSN-in order to save energy and increase the lifetime of the sensing system. Our study focuses on partial coverage, targeting scenarios in which the continuous monitoring of a limited portion of the area of interest is enough. In this paper, we present PCLA, an efficient algorithm based on Learning Automata that aims at minimizing the number of sensors to activate, such that a given portion of the area of interest is covered and connectivity among sensors is preserved. Simulation results show how PCLA can select sensors in an efficient way to satisfy the imposed constraints, thus guaranteeing better performance in terms of both working-node ratio and WSN lifetime. Also, we show how PCLA outperforms state-of-the-art partial-coverage algorithms.