Research On Optimization Algorithm Of WSN Particle Swarm Coverage

Wireless sensor network(WSN)is currently in the domestic and international concern,involving multi-disciplinary and knowledge of high cross,integrated frontier hotspot research field.It provides the efficient connection of the transport network with the information of the objective world,providing the most direct,the most effective and most real information for the next generation of networks.In WSN research,covering problem is a large number of sensor nodes deployed in monitoring area problems first,and how to select efficient coverage algorithm and control strategy for WSN nodes in optimizing deployment,to a large extent affected the effective use of node energy,network perception,service quality and the area covered by the sensor nodes can be improved greatly,so as to prolong the survival time of network.This thesis mainly researches on the coverage problem aiming at the node in WSN deployment.The research on the theory of the particle swarm optimization(PSO)algorithm and bacterial foraging optimization(BFO)algorithm foundation,established the optimization model of WSN coverage related,puts forward the improved BFO algorithm combined with PSO algorithm.The main content and research results are as follows:(1)Firstly,the convergence performance of WSN covering algorithm is studied.It Because the standard particle swarm optimization(PSO)algorithm has the disadvantages of slow convergence rate and easy to fall into the local optimal value in the WSN coverage deployment,which leads to the phenomenon of "premature" and single particle defects.For the above problems,this paper to improve the standard of bacterial foraging optimization(BFO)algorithm: In the trend steps,by adaptively improving the swimming step of the bacteria,making them more accurate and quick search to the target;In the replication operation,a distributed estimation algorithm is added to increase the diversity of the species of bacteria.Furthermore,based on the improved BFO algorithm,the particle swarm optimization-bacterial foraging optimization(PSO-BFO)algorithm is proposed,that is,the PSO algorithm completes the global search of space,memorizes the information of individuals and groups,while the local search of space is completed by the trend and aggregation operation of improved BFO algorithm.The simulation results show that The proposed optimization algorithm is superior to the standard particle swarm and bacterial foraging algorithms in terms of the convergence rate and optimization efficiency of the particles,thus ensuring the effectiveness of the algorithm in WSN deployment.(2)For random high density sensor nodes of the network environment is prone to have the overlapping areas and coverage blind spots in node monitoring area,the papers to maximize WSN network coverage and the least number of nodes deployed to cover the optimization goal,the proposed particle swarm optimization-bacteria foraging coverage optimization algorithm(PSO-BFO)with single PSO algorithm and BFO algorithm for comparative analysis.The simulation results show that,the coverage optimization algorithm proposed in this paper is able to obtain a higher WSN coverage rate with fewer deployment nodes,maintain the stability of the network,and prolong the survival time of WSN network effectively.Finally,summarizing the work done in this paper and puts forward the research and development direction.