A learning automata-based algorithm for solving the target [formula omitted]-coverage problem in directional sensor networks with adjustable sensing ranges

Abstract

In order to find a solution for the target coverage problem in directional sensor networks (DSNs), some researchers have recently introduced several efficient algorithms. These sensors are conventionally supposed to have a single power level and a single coverage is just needed for targets. In other words, there are various sensing ranges and power consumptions for these sensors under real conditions and at least k times monitoring is required for each target. The present paper addresses this issue as the target k-coverage with adjustable sensing range, which has not been already studied in DSNs. To solve this problem, two learning automata-based algorithms (Algorithms 1 and 2) are proposed and equipped with a strong pruning rule that facilitates the selection of appropriate sensor directions capable of providing the targets with k-coverage. After evaluating the efficiency of the algorithms’ performance by conducting several experiments, the results were compared to those ones obtained by a greedy-based algorithm, which is discussed in the literature. Finding indicated that algorithms have superiority over their rivals regarding the prolonged lifetime of their network.