Solving target coverage problem in directional sensor networks with ability to adjust sensing range using continuous learning automata

Abstract

Directional sensor networks (DSNs) are classified under wireless networks that are largely used to resolve the coverage problem. One of the challenges to DSNs is to provide coverage for all targets in the network and, at the same time, to maximize the lifetime of network. A solution to this problem is the adjustment of the sensors’ sensing ranges. In this approach, each sensor adjusts its own sensing range dynamically to sense the corresponding target(s) and decrease energy consumption as much as possible through forming the best cover sets possible. In the current study, a continuous learning automata-based method is proposed to form such cover sets. To assess the proposed algorithm’s performance, it was compared to the results obtained from a greedy algorithm and a learning automata algorithm. The obtained results demonstrated the superiority of the proposed algorithm regarding the maximization of the network lifetime.

Enhancing Barrier Coverage in Surveillance Applications Through Hybrid Directional Sensor Networks

“…The coverage problem in DSNs can be divided into three categories: target coverage, area coverage, and barrier coverage. The aim of the target coverage is to determine a subset of directional sensors to cover a set of targets [4,5]. The area coverage is related to the field coverage [6][7][8], where directional sensors are activated in such a way to ensure a full region coverage.…”