Multiobjective Optimization Strategy of WSN Coverage Based on IPSO-IRCD

Abstract

The nonuniform distribution characteristic of randomly deployed mobile nodes will lead to the coverage hole and redundancy in wireless sensor networks (WSNs). To solve this problem, we propose a multiobjective optimization algorithm for WSN based on Improved Particle Swarm Optimization-Increment of the Ratio of Coverage Rate to Move Distance (IPSO-IRCD), and a network node coverage optimization model is formulated to maximize the coverage rate of the target area while reducing the moving distance of nodes. In each iteration of IPSO, the population fitness value is calculated and compared with the historical optimal value, when the arbitrary dimensional location information of each node is updated, which can avoid the standard PSO algorithm loses the optimal solution, and IPSO will determine the candidate deployment location of nodes. Based on which, IRCD node coverage scheduling optimization algorithm is proposed, so that the final deployment location can be determined iteratively by calculating IRCD of nodes. Simulation results indicate that, for the nodes initial coverage state follows random distribution and Gaussian distribution, IPSO-IRCD can, respectively, improve 4.6% and 7.4% coverage ratio compared with the suboptimal algorithm in other five similar algorithms and reduce 809.59 m and 626.63 m nodes moving distance.