Stochastic barrier coverage in wireless sensor networks based on distributed learning automata

Abstract

Barrier coverage is one of the most important applications of wireless sensor networks. It is used to detect mobile objects are entering into the boundary of a sensor network field. Energy efficiency is one of the main concerns in barrier coverage for wireless sensor networks and its solution can be widely used in sensor barrier applications, such as intrusion detectors and border security. In this work, we take the energy efficiency as objectives of the study on barrier coverage. The cost in the present paper can be any performance measurement and normally is defined as any resource which is consumed by sensor barrier. In this paper, barrier coverage problem is modeled based on stochastic coverage graph first. Then, a distributed learning automata-based method is proposed to find a near optimal solution to the stochastic barrier coverage problem. The stochastic barrier coverage problem seeks to find minimum required number of sensor nodes to construct sensor barrier path. To study the performance of the proposed method, computer simulations are conducted. The simulation results show that the proposed algorithm significantly outperforms the greedy based algorithm and optimal method in terms of number of network barrier paths.