Research On Wireless Sensor Network Coverage Based On Particle Swarm Optimization Algorithm

Wireless sensor network(WSN)is a self organized network formed by distributing huge number of sensor nodes in the monitoring area and transmitting information among the nodes.Because of the complex working environment of the wireless sensor network and the inconvenience of replacing the power of the sensor nodes,the coverage control problem of the network has become the core of the research.It determines the monitoring area coverage rate of the wireless sensor network and the overall life of the network,which directly reflects the monitoring quality of the wireless sensor network.So applying a reasonable coverage strategy can optimize the deployment of nodes and achieve the effective use of wireless sensor network nodes.This thesis focuses on the problem of nodes arrange and coverage in wireless sensor networks.And set up the coverage model of wireless sensor network based on standard particle swarm optimization and social particle swarm optimization.And put up the method of dynamically adjusting nodes' sensing radius and adaptive inertia weight coefficient to improve the network coverage rate,reduce energy consumption and ensure coverage quality.The main contents of the thesis are as follows:(1)This thesis analyzes the influence of sensor nodes' sensing radius on the overall network coverage optimization performance.As the energy of the node is limited,the greater the sensing radius is,the greater the energy consumption is.Therefore,a coverage strategy which can dynamically adjust the node perception radius is proposed.The main idea is to introduce the energy consumption coefficient to determine how much the nodes' energy consumption is,so as to change the sensing radius of nodes dynamically,and reach the high quality coverage of monitoring area.The standard particle swarm optimization(PSO)and social particle swarm optimization(PSO)are used to optimize the sensor network coverage model,and analyzing the simulation results by MATLAB.The results show that: firstly,the effects of social particle swarm optimization in wireless sensor network coverage optimization model is better than standard particle swarm optimization(PSO);secondly,it is proved that we can improve the coverage of the entire network by adjusting the nodes' sensing radius.From the results of MATLAB simulation,we can see that this strategy can improve the network coverage rate while reducing network energy consumption and achieve the purpose of optimization.(2)In the iterative process of the particle swarm optimization(PSO),we analyze the inertia weight coefficient in the speed formula,which decreases linearly with the increase of the iterations.In the whole process,the degree of migration and evolution of the particles are not considered too much.Therefore,a self-adaptive particle swarm optimization algorithm is proposed.From the simulation results of MATLAB,we can see that the improvement of this adaptive strategy is effective.