MEP-PSO Algorithm-Based Coverage Optimization in Directional Sensor Networks

As a sub-class of internet of things (IoTs), wireless sensor networks (WSNs) are becoming ubiquitous in recent years, which makes the efficient coverage of sensors challenging. Traditionally, WSNs are composed of omni-directional sensors, which, however, are still limited to unadjustable sensing angle and superfluous energy consumption. Fortunately, these limitations can be overcome by deploying directional sensors in WSNs, thus forming directional sensor networks, namely DSNs. Therefore, it is necessary to propose efficient coverage optimization methods for DSNs to solve the minimum exposure path (MEP) problem that refers to a path along which the intruder can go through WSNs with lowest detection probability. In this paper, a novel MEP-PSO algorithm-based coverage optimization mechanism is proposed to improve the coverage quality in DSNs. With our coverage optimization mechanism, the traditional MEP problem is analyzed by means of discrete geometric theories while the path searching performance is improved based on the particle swarm optimization (PSO) algorithm. Specifically, the deployment scenario is firstly discretized into multiple square grids with uniform sizes. The weighted undirected graph is thus constructed in which the path segment exposure of MEP can be analyzed by discrete geometric theory. Based on the analysis, the feasibility of PSO is evaluated and enhanced in terms of MEP searching. Using our algorithm, the coverage performance of DSNs can be improved significantly by dynamically adjusting the positions of directional sensors. Finally, we conduct extensive experiments to validate the effectiveness of our work.