An improved range-free location algorithm for industrial wireless sensor networks

Abstract

In the traditional DV-Hop location algorithm, the number of hops within the communication radius is counted as one hop. However, in the case of uneven distribution of nodes, this method will bring a large error to the calculation of hop distance. In order to solve the above problems, an improved DV-Hop location algorithm with multiple communication radius is proposed in this paper. Based on the multi-communication radius location algorithm, the method uses the cosine theorem to correct the hop number and estimated the average hop distance, and optimizes the estimated distance of the unknown node. The algorithm uses multiple communication radius to broadcast the position to obtain the minimum number of hops between the unknown node and the beacon node and adjusts the estimated hop distance by using the cosine theorem after estimating the good hop distance, and finally uses the trilateration method, or the maximum likelihood estimation method estimates the position coordinates. The experimental results show that compared with the traditional DV-Hop algorithm and DDV-Hop algorithm, the improved DV-Hop algorithm improves the positioning accuracy and reduces the average positioning error of the unknown nodes.

A multi‐objective distance vector‐hop localization algorithm based on differential evolution quantum particle swarm optimization

“…Han et al 25 proposed to improve the search stagnation and premature convergence of the DE algorithm by injecting random variation operation algorithm and then obtained the optimal global solution of node estimated position by using the improved DE algorithm, thus enhancing the stability of the localization algorithm. The improved DV hop algorithm proposed by Li et al 26 uses multiple communication radius to refine the number of hops to improve the localization accuracy. At present, there have been much research results on the improvement of the DV‐HOP algorithm, but there are still some shortcomings, such as not taking into account the low collinearity of anchor nodes, increasing algorithm complexity, increasing wireless transmission cost, and other problems 27…”

Performance Analysis of Wireless Location and Velocity Tracking of Digital Broadcast Signals Based on Extended Kalman Filter Algorithm

“…It can be obtained by solving the equations. In the case of multiple groups of solutions, solutions that do not conform to the actual position are easily excluded [16].…”

A path planning model based on nested regular hexagons using weighted centroid localization

“…Their cost is cheaper, and they do not require extra hardware 8 . Also, in recent years, existing range‐free schemes have been developed using different approaches to increase the accuracy and coverage performance of range‐free schemes 9–12 …”