Localization Algorithm In Wireless Sensor Networks Based On DV-HOP

Nowadays,with the development of science and technology,wireless sensor network has become a hot research field.Localization is one of the most critical functions in wireless sensor network applications.This paper mainly studies the popular DV-HOP location algorithm without the need of location algorithm,and analyzes the subjective and objective errors of the algorithm in the positioning process.In order to improve the location accuracy of the algorithm,two improved algorithms are proposed.1.An improved DV-HOP algorithm based on RSSI hops optimization,In the first step of the algorithm,RSSI algorithm is introduced to optimize the minimum hops.The RSSI value received by the node with the distance of the anchor node is taken as the reference value.The calculated hop weight value of each hop will finally get the improved minimum hops,calculate the weighted average hop distance of the unknown node,and take the reciprocal of the distance between the anchor node and the unknown node as the leveling.The weight of the average jump distance avoids the accidental error of the unknown node with the average jump distance from the nearest anchor node as the average jump distance.Finally,the coordinates are corrected.The simulation results show that the algorithm has the best location effect compared with the traditional algorithm and [39] RSSI based on the improved algorithm.2.Improved DV-HOP algorithm based on particle swarm optimization.Using RSSI technology to optimize the minimum hops first.Then,the traditional particle swarm optimization algorithm is optimized from three aspects: inertia weight,learning factor and fitness function.In the last step,using the optimized PSO algorithm to calculate the nodal coordinates solves the shortcomings of the third stage of the traditional algorithm which is sensitive to the initial value.The simulation results show that compared with the traditional algorithm and the improved algorithm based on particle swarm optimization in document [53],the location error of the algorithm is minimal.