Research On DV-Hop Positioning Algorithm Based On Improved Bat Optimizatio

Wireless Sensor Network(WSN)is widely used in military,transportation,medical and other fields.It is of great significance to define the location of sensor nodes for various applications.Therefore,node positioning technology has become the focus of current research.Distance Vector-Hop(DV-Hop),as a classical range-free positioning algorithm,has attracted wide attention because of its advantages such as low cost,simple implementation and so on.The localization performance of the original DV-Hop algorithm is poor,so how to improve the localization accuracy becomes the core problem of the research.To solve this problem,two optimization schemes are proposed,and the main work contents are as follows:(1)Overview of WSN related knowledge,including the research status of positioning technology,algorithm classification and location estimation.This paper expounds the principle of DV-Hop algorithm and analyzes the source of its location error.(2)Aiming at the problems of low localization accuracy of DV-Hop algorithm and poor optimization performance of traditional bat algorithm,an improved DV-Hop localization algorithm based on ranging correction and bat optimization is proposed.The algorithm is improved from two stages of jump distance calculation and position estimation respectively.First,the minimum mean square criterion and correction factor are combined to correct the average jump distance between nodes.Secondly,a threshold is set to control the chaotic mapping to initialize the population,and a speed-weighted strategy is used to control the search step size to improve the search ability of the bat algorithm.Finally,the improved bat algorithm is used to optimize the position of the location node.Simulation results show that the improved DV-Hop localization algorithm has better localization performance.(3)Aiming at the problem of poor localization performance of DV-Hop algorithm in anisotropic networks,an improved DV-Hop localization algorithm based on chaotic mapping is proposed.Firstly,logistic chaotic mapping was used to ensure the quality of the initial population,and two new position updating strategies were selected to enhance the global search ability of bats.Secondly,parameter factors are introduced to control the search range and enhance the local search ability of bats.Finally,an improved bat algorithm is used to optimize the location of unknown nodes.The simulation results show that the proposed algorithm has strong robustness and high precision,and has high positioning accuracy in square,H,X and F networks.