Research On Location Algorithm Of Wireless Sensor Network Based On Mobile Anchor Node

Wireless sensor network is a kind of self-organizing network,which deploys a large number of sensor nodes in the monitoring area,real-time monitoring of the monitoring area,real-time transmission of effective information to users.Node positioning technology is one of the key basic technologies of WSN.It is widely used in military and national defense,fire monitoring,medical and health care,and other aspects.More and more applications have put forward very high requirements for positioning accuracy.When the location information of the sensor nodes is unknown,the data measured by the sensor is difficult to be applied and has no practical significance.Therefore,location information is the premise of pre-warning,decision-making and post-processing of sudden events in the network.Node location technology is the key problem in the practical application of WSN.This paper mainly studies the location of static anchor nodes and static unknown nodes and the location of mobile anchor nodes and static unknown nodes.In addition,in order to improve the positioning accuracy,researchers apply the intelligent algorithm to the node localization algorithm.In this paper,the gray wolf optimization algorithm is studied,and it is improved and applied to the location algorithm.The improved gray wolf optimization algorithm designed in this paper,on the one hand,improves the only control factor in the gray wolf optimization algorithm,and the control factor of the gray wolf optimization algorithm is linear,but the actual iterative process is nonlinear.Therefore,the nonlinear dynamic change control factor is designed in this paper.On the other hand,the gray wolf individuals update their individual positions according to the positions of ?,? and ? in the basic algorithm,and regard ?,? and ? as equal importance,ignoring their respective characteristics.Therefore,the weight factor is introduced in the position updating in this paper.Secondly,inspired by particle "cognition" behavior and particle "society" behavior in particle velocity update in particle swarm optimization(PSO),the disturbance term is introduced to enhance the disturbance and improve the ability of jumping out of local optimization.Compared with the basic gray wolf optimization algorithm and other improved grey wolf optimization algorithms,the simulation results of other intelligent algorithms show that the improved strategy is effective and has higher convergence accuracy and convergence speed.The location algorithm based on static node can be divided into two kinds: one is static anchor nodes assisted positioning,the other is mobile anchor nodes assisted positioning.In the part of auxiliary positioning algorithm for static anchor nodes,this paper mainly combines the common-edge proportion theorem in geometry knowledge,and establishes a mathematical positioning model based on three anchor nodes,which can estimate the location of unknown nodes.In this paper,the initial position estimation is regarded as part of the initial value of the improved gray wolf optimization algorithm,and the coordinates of unknown nodes are optimized by intelligent algorithm to achieve the purpose of higher precision positioning.In the part of mobile anchor node assistant location algorithm,this paper mainly considers the high cost and high energy consumption of mobile anchor node,and hopes to use a small number of mobile anchor nodes to locate unknown nodes in the network.Therefore,a path planning method based on single mobile anchor node is designed.The main work is to select the virtual anchor node first,and then use the TSP algorithm based on the improved gray wolf optimization to carry out the path planning.In the stage of selecting virtual anchor nodes,three virtual anchor nodes are selected as a group,and the desired virtual anchor nodes are selected by eliminating redundancy.The simulation results show that the location algorithm based on improved gray wolf optimization is more accurate and robust to ranging error than DPSO localization algorithm and the trilateral location algorithm based on ranging.Compared with the classical path planning and other existing path planning algorithms,the proposed path planning algorithm based on mobile anchor node and improved grey wolf optimization has higher network location coverage and saves the network cost.It can achieve ideal positioning accuracy.