A Coverage Enhancement Algorithm Based on Constrained Artificial Fish-Swarm in Directional Sensor Networks

Abstract

Area coverage is an essential issue for sensor networks. The majority of the existing studies on area coverage are based on omnidirectional sensing model. However, some popular sensors have a limited angle of sensing range. This paper investigates area coverage enhancement by directional sensors with tunable sensing orientations. Firstly, we model the deployment of directional sensors as a 2D stationary Poisson point process, and evaluate the relationship between the coverage probability and the number of directional sensors. We introduce the notion of ”sensing centroid,” which is the geometric center of a sensing sector to simplify the pending problem. Moreover, we regard ”sensing centroid” as artificial fish, which search an optimal solution in the solution space by simulate fish-swarm behaviors with a tendency toward high food consistence. Considering that AFs have to satisfy both kinematic constraint and dynamic constraint in the process of motion, we propose a constrained artificial fish-swarm algorithm, and discuss the control laws to guide the behaviors of AFs with quick convergence speed. Finally, mass of simulations validate the theoretical findings of our solution.