Wireless Sensor Network Coverage Strategy Based On Grey Wolf Optimization Algorithm

Wireless sensor networks are made up of many sensor nodes.Its nodes have communication and computing power,which can be widely used in some modern military and urban construction,such as: environmental monitoring,target tracking,battlefield monitoring,smart home,etc.However,the arrangement of the sensor nodes is generally a method of randomly throwing in the air,which results in randomness when deploying nodes,and it is difficult to satisfy the monitoring of the entire area.Therefore,it is very important to research the coverage problem of sensor networks.This paper introduces the basic concept,composition structure,network characteristics and coverage related issues of wireless sensor networks.This paper systematically expounds the relevant knowledge points about wireless sensor networks,including coverage types of wireless sensor networks,sensor node perception models and network coverage performance indicator.This paper deeply studies the wireless sensor network coverage algorithm.when optimizing network coverage,particle swarm optimization has some problems,such as poor optimization accuracy,easy to fall into local optimum and slow convergence rate in the later stage.Therefore,a particle swarm optimization algorithm based on dynamic acceleration factor is proposed on the basis of particle swarm optimization algorithm.It uses the decreasing inertia weight coefficient to enhance the global search ability in the initial stage of optimization and the local search ability in the later stage of optimization.At the same time,the dynamic acceleration factor is used to improve the influence of the particle's own experience and the remaining particle experience on the convergence speed to improve the accuracy and speed of the algorithm.Aiming at the problem of grey wolf algorithm in optimizing network coverage without considering individual experience and population diversity,a grey wolf optimization algorithm based on particle swarm optimization is proposed.It utilizes the individual memory function of the improved PSO algorithm,so that it can memorize the optimal solution in its own evolution process,and improve the convergence precision and convergence speed of the algorithm.At the same time,Tent chaotic sequence is introduced into the improved grey wolf algorithm,which enriches the behavior of grey wolf and maps the grey wolf population evenly into the definition space,which is conducive to the grey wolf jumping out of the local extreme point.And,it introduces nonlinear control parameters to balance the algorithm's global search and local search capabilities.In order to solve the coverage problem of wireless sensor networks,this paper uses probabilistic perception model to calculate network coverage,and uses improved grey wolf optimization algorithm to optimize network coverage deployment.The effectiveness and superiority of PSO_GWO algorithm in the coverage optimization of wireless sensor networks is verified by simulation experiments.Therefore,the algorithm can reasonably allocate resources of the entire wireless sensor network,reduce the redundancy of nodes,increase the coverage rate,and improve the monitoring quality and service quality of the network.At the same time,it can reduce the network energy consumption of the node and extend the life cycle of the network.