Learning Automata-Based Algorithms for Solving the Target Coverage Problem in Directional Sensor Networks

Recently, directional sensor networks have received a great deal of attention due to their wide range of applications in different fields. A unique characteristic of directional sensors is their limitation in both sensing angle and battery power, which highlights the significance of covering all the targets and, at the same time, extending the network lifetime. It is known as the target coverage problem that has been proved as an NP-complete problem. In this paper, we propose four learning automata-based algorithms to solve this problem. Additionally, several pruning rules are designed to improve the performance of these algorithms. To evaluate the performance of the proposed algorithms, several experiments were carried out. The theoretical maximum was used as a baseline to which the results of all the proposed algorithms are compared. The obtained results showed that the proposed algorithms could solve efficiently the target coverage problem.