Research On Network Coverage Technology Based On Hybrid Beetle Swarm Optimization Algorithm

Sensor nodes communicate with each other through a hop routing protocol in the monitoring area,forming a wireless sensor network(WSN).WSN has advantages such as low cost,adaptability,and long life cycle,so it is widely used in fields such as power grid detection,military,and medical management.However,the complex and changeable actual working environment of sensor nodes and the limited power supply energy restrict the development of WSN.Therefore,how to optimize network coverage has become a core issue in wireless sensor network research,which directly affects the quality of service of wireless sensor networks.This article focuses on the optimization of network sensing coverage and has done the following work:(1)For the WSN node coverage problem in an ideal environment,this paper proposes a beetle swarm optimization algorithm(BSO)with chaotic initialization and Gaussian mutation.The network coverage model is used as the objective function,and the optimal fitness value is the coverage rate.In the population initialization stage,in view of the uneven distribution of sensor nodes when randomly deploying mobile nodes,this paper compares various chaotic mapping relationships and chooses cubic mapping to initialize the beetle swarm to improve population diversity.(2)In view of the fact that the beetle swarm algorithm is easy to fall into local optimum when solving nonlinear problems,this paper introduces Gaussian mutation to make it jump out of local optimum.The convergence speed is slow in the later stage and the search accuracy is low.The dynamic perception factor and adaptive weight are added to solve the problem of convergence speed and accuracy.The algorithm proposed in this paper is tested using 10 test functions and compared with 4 similar algorithms.It is applied to network node coverage optimization in an ideal environment.The experimental simulation results prove that the performance of the beetle swarm algorithm with chaotic initialization and Gaussian mutation is better than that of comparative algorithms,has better search ability,and improves the coverage rate of WSN nodes.(3)For the WSN node coverage rate problem in a complex environment,this paper proposes a hybrid beetle swarm(DEBSO)algorithm and applies it to WSN node coverage in an ideal environment and a complex environment.In the initial stage of the algorithm,use a good point set learning strategy to initialize the population.By introducing dynamic learning factors and adaptive weights to solve the problem that the search ability of beetle swarm algorithm is poor in early and late stages.Use differential optimization algorithm mutation crossover operation to randomly extract three individual values and guide other individuals to communicate to prevent untimely information feedback between particles.The DEBSO algorithm is proposed and compared with other 4 algorithms.Using the same 10 test functions proves its superiority and then applied to WSN coverage problems in ideal and complex environments.The experimental results show that DEBSO can optimize wireless sensor network coverage.