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

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. (C) 2020 Elsevier B.V. All rights reserved.