Research On Coverage Optimization Of Wireless Sensor Network Based On Improved Genetic Algorithm

Wireless sensor Network(Wireless sensor Networks,WSNs)mainly consists of some low-cost,low-power,multi-functional,small size wireless sensor network nodes.Its sensor nodes share a common sense of the environment for simple data processing and wireless communication over a short distance.Overlay control is a basic problem in wireless sensor networks,and it is also a measure of service quality evaluation in wireless sensor networks.How to ensure the maximum coverage of wireless sensor networks under certain quality of service conditions,and to provide reliable monitoring and target tracking services is the topic of wireless sensor network problems today.An effective wireless sensor network coverage control strategy can optimize the resource allocation of the network,improve the energy efficiency of network nodes,perceive the quality of service,and prolong the overall survival time of network nodes.Based on the improved genetic algorithm for wireless sensor network coverage optimization,this paper first introduces genetic algorithm into the coverage control problem,and then analyzes the advantages and disadvantages of intelligent algorithms such as particle swarm algorithm,ant colony algorithm and genetic algorithm.Then an improved roulette optimization method is proposed for the mobile node scheduling problem of wireless sensor networks.This method is based on the choice of fitness ratio,that is,using the selection probability of all individuals to calculate the cumulative probability,produce a complete descendant individual and retain its genes,avoid falling into the local optimal,and then quickly and accurately find the optimal solution of the node scheduling problem.In order to solve the problem of network coverage in the area monitoring of wireless sensor networks,a two-element perceptual model is established,aiming at the radius and coverage of the sensor,and the model is solved by using the immune cloning algorithm.The main research work and achievements of the thesis are as follows:1.A two-element perceptual model Network is constructed,and a wireless sensor network node deployment method is proposed.For areas where nodes are deployed in an undetermined manner,a large number of static sensor nodes are typically deployed in the monitoring area.Aiming at the monitoring area with high density of sensor nodes,a node scheduling algorithm based on improved genetic algorithm is proposed.In order to effectively cover the network,the sensor network is optimized to achieve energy-saving coverage by redeploying and deploying network nodes.2.In order to solve the problem of network coverage in the area monitoring of wireless sensor networks,the principle of immune cloning is introduced into the network coverage,the sensor radius and coverage are used as the goal,the model is solved by the immune cloning algorithm,and the results are compared with the genetic algorithm.Through experimental simulation,the immune cloning algorithm makes the arrangement of sensor position in the same monitoring area more reasonable,avoids the coverage redundancy,saves energy,and improves the coverage of the wireless sensor network.3.The simulation experiment of coverage optimization is designed by using elementary particle swarm optimization algorithm,ant colony algorithm,genetic algorithm and improved genetic algorithm.Through the simulation results,the advantages and disadvantages of several algorithms are compared and analyzed,and the improvement ideas for the deficiency of genetic calculation are put forward.In this paper,the genetic operator is improved in the coverage simulation experiment of traditional genetic algorithm and improved genetic algorithm,including the improvement of selection operator,crossover operator and mutation operator.Through the improvement of genetic operator,it can effectively avoid the local optimal situation of coverage and improve the global search ability of the algorithm.