Barrier coverage in adjustable-orientation directional sensor networks: A learning automata approach

Abstract

Barrier coverage is one of the main applications of wireless sensor networks. There are two kinds of barrier coverage: weak and strong. While weak barrier coverage can only guarantee detecting intruders moving along predetermined or congruent paths, strong barrier coverage guarantees detecting all intruders regardless of their crossing paths. In this paper, we first present a centralized algorithm based on distributed learning automata to find strong barrier lines in adjustable-orientation directional sensor networks in which nodes can adjust their orientation in a non-overlapping form such as camera sensor networks. Then, we will present the distributed version of the proposed algorithm to be used in practical sensor networks. Moreover, the results of extensive simulations will be presented to compare the performance of proposed algorithms against the optimal algorithm, a greedy algorithm and a previously-proposed algorithm. The results confirm that the proposed algorithms achieve near-optimal results and outcome other algorithms completely.