A Two-Stage Wireless Sensor Grey Wolf Optimization Node Location Algorithm Based on K-Value Collinearity

Yinghui Meng,Qianying Zhi,Erlin Lin,Qiuwen Zhang

doi:10.1155/2020/7217595

Yinghui Meng, Qianying Zhi + Show 2 more

Open Access

https://doi.org/10.1155/2020/7217595

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Abstract

In the practical application of WSN (wireless sensor network), location information of the sensor nodes has become one of the essential information pieces in the whole network. At present, some localization algorithms use intelligent optimization algorithm to optimize the node group directly. Although the overall localization error is reduced, the location deviation of individual unknown nodes will be larger, and the large number of iterations will cause a large energy consumption of nodes. Aiming at the above problems, this paper comes up with a two-stage WSN localization algorithm based on the degree of K-value collinearity (DC-K) and improved grey wolf optimization. The first stage is aiming at the defects of the existing collinearity algorithm, putting forward the concept of DC-K, according to the K-value to carry out the initial location in the first stage. The second stage is using the improved grey wolf optimization algorithm to optimize the location results which were obtained in the first stage, so as to get more accurate location results. The experimental results display that this localization algorithm with a better localization accuracy has high robustness and has fewer iterations in the optimization process, which greatly reduces the energy consumption of nodes.

Highlights

Localization is to identify the coordinates of something in a geographical environment. e significance of node localization technology in WSN is to deduce the coordinates about the unknown nodes which are located in the network
In the first stage, according to the defects of collinearity [6,7,8], in this paper, we propose the new collinearity based on K-value and infer to the initial location of the node from the collinearity of K-value; in the second stage, the improved localization algorithm of Grey Wolf Optimizer (GWO) is used to optimize the node location
Deploy 100 sensor nodes in a 100 × 100 square area, compare and analyze the performance of the wireless sensor node localization algorithm from different communication radius and beacon node ratio, and compare with the Degree of Collinearity Based on Minimum Height (DC-H), DC-A, GWO, and DVhop

Summary

Introduction

Localization is to identify the coordinates of something in a geographical environment. e significance of node localization technology in WSN is to deduce the coordinates about the unknown nodes which are located in the network. The cost of adding additional distance measurement equipment to the node is very high; the lifetime of the sensor node is greatly shortened by frequent receiving of ranging information This method fails in extremely complex geographical environments. More and more scholars apply the intelligent optimization method to location estimation of wireless sensor node; in [4], PSO (particle swarm optimization algorithm) is introduced, through hundreds of iterations of the particle swarm optimization model; the coordinates of the unknown nodes that are consistent with the fitness function are obtained; implementing localization algorithm [5] improved the particle swarm optimization localization method and came up with the concept of quantum particle swarm, choosing the wave function to describe the state of the particle and increasing the location accuracy, but the computational cost is higher. Only 30 iterations are needed to obtain a better location result, which greatly reduces the energy consumption of nodes

Selection of Location Unit Based on Collinearity

Defects of DC-H and DC-A

Preliminary localization of unknown node u:

Conclusion