Novel RPSO Based Strategy for Optimizing the Placement and Charging of a Large-Scale Camera Network in Proximity Service

Abstract

Sensor placement and charging in proximity service are becoming critical issues. In this paper, novel methods are proposed to address the coverage optimization problem and charging problem of camera networks with mobile nodes. Because the sensing angle of a camera is limited, the placement of a camera network is more complicated compared with an omnidirectional sensor network. Aiming at finding the best positions and working angles of all camera nodes in the coverage optimization problem, we propose a novel resampling particle swarm optimization (RPSO)-based process. First, the RPSO is introduced, which has better precision and efficiency than the PSO and the genetic algorithm. Second, because of the huge number of variables in the problem, we propose a hierarchical strategy, which divides the whole optimization process into several sub-processes in consideration with overlay redundancy and coverage area of all camera nodes, reducing the variables at each step. In this way, the precision of optimization results could be improved significantly. After deploying the camera network, we consider charging it to extend its lifetime. Fuzzy c-means, a fuzzy clustering algorithm, is used to classify the sensor nodes according to their positions. Then, a charging station is set in each cluster, the position of which is determined based on the RPSO to minimize the distance from it to all of the sensor nodes in the cluster. So, the charging efficiency is improved than before. The experimental results show that the proposed methods achieve the goal of maximizing the coverage rate and improving the charging efficiency of the camera network in comparison with the traditional methods. INDEX TERMS Smart world, sensor placement and charging, hierarchical strategy, resampling particle swarm optimization, fuzzy clustering.

Solving Optimal Camera Placement Problems in IoT Using LH-RPSO

“…The only different is that there is a resampling operation before updating the position and velocity vectors in the RPSO. Comparing with the PSO, the advantages of the RPSO are mainly reflected in the following aspects: improving the premature defects, avoiding the waste of computing resources, and improving the efficiency to a certain extent [28]. And the RPSO algorithm has been successfully and efficiently applied to the coverage control of sensor networks, and the virtual resource allocation in cloud computing.…”