Research And Improvement Of DV-hop Localization Algorithm In Wireless Sensor Networks

Wireless sensor network(WSN)has been widely used in various fields and is an indispensable member of modern society.For WSN,location technology is the starting point of building network,so it has also become the research hotspot of many scholars.The main work of this article is to analyze and improve the DV-Hop algorithm.The specific work is as follows:1.This article studies the architecture,characteristics and key technologies of WSN,and focuses on the location technology,including two kinds of typical location algorithms and three node estimation methods.The principle of DV-Hop algorithm is studied in detail,the algorithm error is analyzed,and the influence of algorithm defects is summarized through simulation.2.Aiming at the problem that the hop setting is too rough and the estimated distance deviates from the true value,a DV-Hop algorithm based on weighting and RSSI correction is proposed in this article.Firstly,through the multi communication radius method,the fixed communication radius is evenly divided into five levels to make the discrete hops closer to the actual value;Then,according to the number of hops between anchor nodes and unknown nodes,the average hop distance is weighted;Finally,according to the RSSI ranging model,the average hop error is defined and the estimated distance is corrected twice.The simulation results show that the positioning optimization effect of the algorithm is obvious.3.Aiming at the problem that the DV-Hop algorithm can not locate the singular matrix when estimating the coordinates,this article uses the grey wolf intelligent optimization algorithm instead of the least square method to transform the node matrix operation problem into an optimization problem.Because the standard grey wolf algorithm has the disadvantage of "premature phenomenon",and has no communication and memory ability,it often falls into the trap of local optimization.Therefore,this article proposes a DV-Hop algorithm based on the improved gray wolf algorithm,which mainly has four improvements,including population initialization method,convergence factor,location update process and fitness function.The simulation results show that the improved algorithm reduces the requirements for the number of iterations and population,improves the search ability and convergence speed of Gray Wolf algorithm,and further reduces the positioning error.