Improvement And Research On DV Hop Localization Algorithm In WSN

With the development of communication and computer technology,wireless sensor network(WSN)technology has been widely used in military security,intelligent transportation,environmental monitoring and other fields.The positioning accuracy of node positions in WSN has a significant impact on the real-time,reliability,and effectiveness of these applications,for a long time,researchers have continuously proposed algorithms to improve positioning accuracy,taking into account factors such as system cost and algorithm complexity.The DV-Hop algorithm has attracted widespread attention due to its advantages of long service life,low configuration cost,and relatively high positioning accuracy.Therefore,this article chooses the DV-Hop algorithm as the research and improvement object.The main work of this article is as follows:(1)Introduced the architecture,characteristics,and related technologies of wireless sensor networks,summarized the implementation principles,classification,and evaluation standards of WSN positioning algorithms;Detailed analysis of the principle,specific steps,and implementation method of the classic DV-Hop algorithm;Analyzed the sources of errors generated during the algorithm implementation process;Finally,the node network topology distribution map of the DV-Hop algorithm and the node positioning error generated by the experiment were simulated using Matlab tools.(2)This article optimizes and improves the traditional DV-Hop algorithm to address the errors that may occur in the selection of inter node hops and the calculation of average hop distance.Firstly,the communication radius was refined and classified into multiple layers,and the impact of different levels of classification on positioning error was studied through experiments;Then,the average hop distance of the nearest anchor nodes around the unknown node is selected for weighted normalization as the new average hop distance value of the unknown node.The results of simulation experiments have proven that the improved algorithm can effectively reduce positioning errors.(3)Introduce the sparrow algorithm into the DV-Hop algorithm for calculating the coordinates of unknown nodes;Simultaneously using the Circle chaos initialization strategy to improve the diversity of initial particles in the Sparrow algorithm;Introducing adaptive weight factors into the discoverer formula of the Sparrow algorithm improves the algorithm’s global search ability in the early stage while enhancing its local optimization ability in the later stage;Finally,the ability of the algorithm to jump out of the local optimum is improved through Gaussian mutation and Reverse learning.Replace the calculation method of unknown node coordinates in the original DV-Hop algorithm with the improved Sparrow algorithm.The simulation experiment results show that the improved DV-Hop algorithm has lower positioning error compared to the original algorithm and has good positioning performance.