Research On The Coverage Problem Of Wireless Sensor Network Based On Evolutionary Algorithm

Coverage optimization is one of the critical issues in wireless sensor network(WSN).Coverage optimization refers to maximizing the effective coverage of the monitoring object or the monitoring area of the WSN by moving the position of the sensor nodes.In recent years,there have been few studies on the coverage optimization of large-scale WSN,furthermore,the research on the coverage optimization of WSN has remained at a small scale.Although,the research on multi-objective coverage optimization of WSN started earlier,however,the research on each objective in the network is independent,and often dealing with a single objective function or multiple objectives combined to a single objective function.In this paper,the above two problems are studied and the corresponding solutions are proposed.(1)To build a WSN in a large-scale area requires the deployment of a large number of sensor nodes,and accordingly,the search space for the problem will getting higher.However,the performance of the traditional evolutionary algorithm will decrease exponentially with the existence of the ‘dimension disasters'.In this paper,an improved cooperative co-evolution global differential grouping particle swarm optimization(ICC-GDG-PSO)algorithm is introduced for large-scale WSN coverage optimization.The algorithm used the cooperative coevolution(CC)framework,which divides the original problem into multiple sub-problems using a divide-and-conquer strategy,accordingly,the solution to the original problem is obtained by solving each sub-problem.Moreover,in this paper,global differential grouping was integrated with random grouping,and the grouping information is further updated.Finally,each group is solved by using the particle swarm optimization(PSO)algorithm.The comparative experimental results show the superiority of the proposed ICC-GDG-PSO algorithm in large-scale WSN coverage optimization.(2)Most of the studies on WSN multi-objective coverage optimization consider the objectives individually without considering the multiple objectives in the same time.In this way,the multi-objective problems are divided into single-objective problems or the objectives are treated by linear weighing method,which tends to cause the algorithm to lose some good solutions since the multiple objectives of the WSN are in conflict with each other,and by optimizing one of the objective,the other will be constrained.Therefore,in this paper,an improved multi-objective evolutionary algorithm based on coordinate transformation(IMOEA/CT)algorithm is proposed by considering the two objectives of the coverage and the node's mobile energy consumption at the same time.The proposed algorithm can obtain a set of Pareto optimal solutions after a single run,and then the most appropriate solution is selected by the decision maker according to the actual needs.Finally,the comparison experimental results show the superiority of the IMOEA/CT algorithm in the multi-objective coverage optimization of WSN.