Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks

Abstract

Sensor networks have been employed in a variety of applications. Directional sensor networks (DSNs) are a class of sensor networks that have emerged more recently and received noticeable attention from scholars. One of the most significant challenges associated with DSNs is designing an effective algorithm to cover all the targets and, at the same time, retain connectivity with the sink. As sensors are often densely deployed, employing scheduling algorithms can be considered as a promising approach. In this paper, we use distributed learning automata (DLA) to design a new scheduling algorithm for solving the connected target coverage problem in DSNs. The proposed algorithm employs DLA to determine the sensors that should be activated at each stage for monitoring all the targets and transmitting the sensing data to the sink. In addition, we devise several pruning rules in order to maximize network lifetime. Extensive simulation experiments were carried out to evaluate the performance of the proposed algorithm. Simulation results demonstrated the superiority of the proposed algorithm over a greedy-based algorithm in terms of extending network lifetime