Constrained Artificial Fish-Swarm Based Area Coverage Optimization Algorithm for Directional Sensor Networks

In this paper, we explore the area coverage optimization problem by directional sensors with tunable sensing orientations. We firstly introduce the concept of "sensing centroid", which is the geometric center of a sensing sector to simplify the pending problem. Particularly, we regard "sensing centroid" as artificial fish (AF), and search an optimal solution in the solution space by simulating fish swarm behaviors (such as prey, swarm and follow) with a tendency toward high food consistence. Fully 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 (CAFSA), and discuss the control laws to guide the behaviors of AFs with high convergence speed. Finally, we evaluate the effect of some primary parameters on the performance of our solution through extensive simulations.