Research On Coverage Control Algorithm For Wireless Sensor Networks

With the development of science and technology,wireless sensor network has been widely used in military,medical,environmental and other fields.How to deploy sensor nodes reasonably is the key to make full use of sensor network in the used area.When wireless sensor network monitoring information,the sensor nodes randomly arranged in the test area,researchers have proposed using Particle swarm optimization(PSO)algorithm to guide the deployment of sensors,so as to improve the coverage of the network,but due to the premature convergence of PSO itself exists Particle,update the way is single,easy to fall into local optimum,led to a range of sensors to the overlapping of stacking,has created the problem of network coverage rate is low.In order to increase the diversity of algorithms,this thesis has the following research contents:The Improve particle swarm optimization by adjusting particle spacing is based on omni-directional perception model,which improves the particle position adjustment by different particle spacing and the ability of particle searching for the optimal solution,so that the network can obtain a higher coverage rate in a specific area and reduce the possibility of premature local convergence.The coverage optimization of wireless sensor network with APS-PSO algorithm not only reduces the situation of particle aggregation,but also enhances the ability of particle exploration.The algorithm not only improves the uniformity of sensor node distribution and network coverage,but also has relatively good stability.The coverage rate of APS-PSO reached 99.11%,which was 4.26% higher than the basic PSO,3.61% higher than the genetic algorithm,and 2.52% higher than the enhanced transboundary immunoparticle swarm optimization.Exploring ability Enhanced particle swarm optimization(EEC-PSO)is applied to the directed fan perception model,the algorithm is based on basic PSO,the optimum solution and refined by adding time optimal trajectory two aspects to improve the way of individual updates,to avoid premature local convergence phenomenon,and enhance the global convergence of the algorithm,and improve the exploring ability of the particles so as to achieve better coverage optimization effect.MATLAB experimental simulation results show that the coverage of EEC-PSO can reach 98.6%,which is 5.8% higher than the enhancement algorithm of DSN based on particle swarm optimization,7.43%higher than that of genetic algorithm,and 8.65% higher than PSO.It is concluded that the EEC-PSO deployment scheme is superior to the other three algorithms.