Research On Coverage Control Based On Particle Swarmintelligence In Wireless Sensor Network

A Wireless Sensor Network(WSN),which consists of large amount of sensors that have the ability to sense the environment,is applied to lots of fields such as environmental monitoring,target tracking,etc.The coverage problem is a key performance metric none of these applications can neglect.The sensors usually have some physical constrains such as batteries with limited power,poor wireless communication capabilities and limited computing resources.These constrains,however,yield coverage problems in WSN.The coverage control strategy is such a method that can improve coverage ratio and lifetime in the target area by deploying location of nodes in wireless sensor networks or by decreasing numbers of sensor nodes in order to increasing lifetime of wirelss sensor networks.In this paper,we study two problems:one is focusing on the coverage enhancing problem based on particle swarm intelligence in WSN;another one is multi-objective coverage control problem in WSN,which concerns not only about improving coverage ratio in the network,but also trying to decrase the number of working or active sensor nodes in WSN.For solving such problems,first of all,we study particle swarm optimization(PSO)algorithm,and based on standard particle swarm optimization,we propose an improved particle algorithm,called Nonlinear Inertial Weight Particle Swarm Optimization(NIW-PSO)algorithm for mobile WSN coverage optimization.Secondly,we study the Pareto Optimality strategy and consider improving coverage and decreasing number of active sensors as a multi-objective coverage optimization problem(MCOP),then propose a Multi-objective Coverage Control Algorithm(MCCA)based on multi-objective particle swarm optimization and Pareto optimality.In the end,we give the conclusion and the future work in this filed.