Research On Target Tracking And Energy Saving Coverage Optimization Of Wireless Sensor Networks

Wireless sensor network(WSN)is a wireless network composed of sensor nodes with sensing,communication and computing capabilities.Wireless sensor network has the advantages of low cost,good scalability,high reliability,accuracy,flexibility and easy deployment,making the network has become one of the most promising technologies in the future.At present,wireless sensor networks have been widely used in various fields,such as industry,agriculture,medical treatment,environment and national defense and other fields.Target tracking technology and coverage optimization technology are the main technologies in wireless sensor networks.Target tracking technology is the precondition of many applications of wireless sensor networks.Network coverage determines the scope of service and quality of service provided by the network,which affects the performance of the network and the cost of the network to a great extent.In the thesis,the problem of target tracking and coverage optimization based on wireless sensor networks is studied.The main work of the thesis is as follows:1.Target tracking in wireless sensor networks based on data driven extended Kalman filtering algorithm.As an traditional target tracking algorithm,the extended Kalman filter algorithm only uses the target state information of the current time when the target state is predicted.Due to the interference of noise in the process of the target's movement,the state of the next moment will be offset.Aiming at this problem,this thesis improves the extended Kalman filtering algorithm,and proposes an extended Kalman filter algorithm based on data driven.The improved algorithm takes advantage of the target motion precursor data combined with the current state of information can be used to predict the state of the next moment of the target,and moderately increases the "trust degree" of the measured value in the extended Kalman filter algorithm.The simulation results show that the improved algorithm can improve the accuracy of target tracking to a certain extent.The improved data driven extended Kalman filter tracking algorithm can track the target more accurately when the time interval of sensor measurement increases slowly.The tracking accuracy of the two algorithms is almost the same when the time interval is small.The energy of the sensor is limited,and the time interval of the sensor measurement increases,which can save the energy of the sensor.2.Coverage optimization strategy for wireless sensor networks based on bird swarm algorithm.This thesis studies the coverage optimization strategy of wireless sensor networks based on bird swarm algorithm.The traditional wireless sensor network coverage optimization strategy based on particle swarm algorithm has the problem of slow convergence rate,low node coverage and uneven distribution of nodes.This thesis presents a wireless sensor network coverage optimization strategy based on bird swarm algorithm.Simulation results show that when the number of mobile nodes is limited,the bird swarm algorithm can converge quickly,achieve higher coverage,more uniform node distribution,achieve more reasonable node deployment,extend the service life of the sensor nodes and the lifetime of the network.3.Energy saving coverage optimization strategy for wireless sensor networks based on PSO and BSA combining algorithm.On the basis of the study only consider the problem of coverage,this thesis further studies the network energy saving coverage optimization problem considering the network coverage and energy consumption of information transmitted by nodes in the network.The energy saving coverage optimization problem of the wireless sensor network is studied by constructing a distributed wireless sensor network composed of static sensor nodes and dynamic sensor nodes.This thesis presents PSO and BSA combining algorithm.Through the simulation experiment,the optimization results of based on PSO and BSA combining algorithm are compared with the results of using the bird swarm algorithm and particle swarm algorithm alone.The simulation results show that when the number of sensor nodes in the network is limited,and PSO and BSA combining algorithm optimization method can make the network coverage rate higher and a lower energy measurement.Therefore,the energy saving coverage optimization strategy of wireless sensor networks based on PSO and BSA combining algorithm can not only improve the service quality of wireless sensor networks,but also prolong the lifetime of wireless sensor networks.