Research On Coverage Optimization Algorithm For Wireless Sensor Networks

This paper studies the coverage optimization of Wireless Sensor Network(WSN).During coverage optimization,sensor nodes are usually broadcast in a random deployment manner in the target area.In this way,multiple overlapping coverage areas and coverage holes may occur,resulting in reduced network coverage and waste of resources.In order to solve these problems,this paper introduces the node probability perception model and the grid coverage model,the particle swarm algorithm and the fruit fly algorithm have been improved and applied to the model.To a certain extent,our methods effectively reduce the redundancy of the nodes in the coverage and improve the coverage of the network.When the particle swarm optimization algorithm is applied to the coverage optimization of wireless sensor networks,there will be problems such as slow convergence rate,proneness to premature convergence,and particle crossover.Aiming at these problems,an optimized ability enhancement and bounds immune particle swarm optimization is proposed.Two improvements have been made in particle boundary crossing and particle update,which achieve higher coverage and avoid falling into local optimum.Combined with the corresponding model,the algorithm is applied to the coverage optimization of wireless sensor networks,which not only reduces the network redundancy,but also reduces the energy consumption caused by the node movement.Simulation results show that the algorithm can increase the coverage of 11% by an average,and through 50 Monte Carlo experiments,it shows that the algorithm has strong stability.When the fruit fly algorithm is optimized for wireless sensor network coverage,due to the single step change,and the update method is too limited,it is easy to fall into a local optimum.In order to solve this problem,this paper makes an improvement on the algorithm of fruit fly optimization algorithm,and puts forward the Dichotomy Fruit Fly Optimization Algorithm.Through the improvement of the step length,two methods are used to update the individual.It can quickly jump out of the local optimal and avoid the precocious phenomenon effectively,and the coverage optimization of the network sensor network can be better.Simulation shows that the dichotomy fruit fly optimization algorithm can effectively avoid redundant coverage,improve network coverage and save energy consumption.