Large Scale WSN Deployment Based on an Improved Cooperative Co-evolution PSO with Global Differential Grouping

摘要

With the development of wireless sensor networks (WSNs), the applications of WSNs are becoming more and more, especially in military monitoring, target tracking and traffic control. Coverage is one of the key metrics for WSNs performance. As a number of typical WSN applications such as forest fire or hostile environments monitoring are likely to expand their service coverage, they have to deploy a large number of sensor nodes in the interest area. However, with the number of sensor nodes increase the dimensions of the problem are also getting higher, traditional WSNs deployment algorithms can not achieve the desired results. In this paper, we propose an improved cooperative co-evolution global differential grouping particle swarm optimization (ICC-GDG-PSO) algorithm to solve the problem of large scale deployment. Global Differential Grouping (GDG) algorithm has good performance in large scale problem optimization, especially when the WSN contains thousands of sensors. We using the GDG decomposition strategy on a cooperative coevolution (CC) framework. But GDG algorithm won't update the grouping since the grouping information confirmed, we integrate the random grouping mechanism after the GDG algorithm to get more accurate grouping of variables. Experimental results show that our proposed ICC-GDG-PSO algorithm is superior to other algorithms in the large scale deployment problem.