Three particle swarm algorithms to improve coverage of camera networks with mobile nodes

Abstract

This paper studies how to improve the coverage of camera networks where the locations and orientations of the cameras can be adjusted. As the power energy limits the distance that each camera can move, the coverage problem is a constrained optimization. We provide three particle swarm optimization (PSO) algorithms for the problem, which are the penalty PSO, the absorbing PSO, and the reflecting PSO. The penalty PSO adds penalty to the coverage when a camera violates the distance constraint, while the last two PSO algorithms use the feasibility operators. By the absorbing PSO, the cameras out of the distance limit will stay at the boundary of the limit, while by the reflecting PSO their locations are randomly reinitialize into the feasible area. The three PSO algorithms are tested on the benchmarks, and the statistical analysis shows that their performance is in the descendant order of absorbing PSO, penalty PSO, and reflecting PSO. The results suggest that the absorbing PSO is a good choice for the coverage problem of camera networks.