A Kernel-Based Node Localization in Anisotropic Wireless Sensor Network

Wenxiu He,Fangfang Lu,Yi Ruan,Tingjuan Lu,Yi Zhang,Jingjing Chen,Qianchuan Zhao

doi:10.1155/2021/9944358

Abstract

Wireless sensors localization is still the main problem concerning wireless sensor networks (WSN). Unfortunately, range-free node localization of WSN results in a fatal weakness–, low accuracy. In this paper, we introduce kernel regression to node localization of anisotropic WSN, which transfers the problem of localization to the problem of kernel regression. Radial basis kernel-based G-LSVR and polynomial-kernel-based P-LSVR proposed are compared with classical DV-Hop in both isotropic WSN and anisotropic WSN under different proportion beacons, network scales, and disturbances of communication range. G-LSVR presents the best localization accuracy and stability from the simulation results.

Highlights

Wireless sensors localization is still the main problem concerning wireless sensor networks (WSN). e localization algorithms of WSN can be classified into the range-based measurement method and the range-free measurement method. e former one can get high accuracy but need range information [1,2,3,4], while the latter one gets low accuracy without any range information
In order to improve the accuracy of range-free node localization, machine learning is introduced to the localization of WSN [5]
In order to improve the accuracy of node localization in anisotropic WSN, we introduce kernel regression approach to node localization firstly. e contribution in this paper can be summarized to (1)

Summary

Introduction

Wireless sensors localization is still the main problem concerning wireless sensor networks (WSN). e localization algorithms of WSN can be classified into the range-based measurement method and the range-free measurement method. e former one can get high accuracy but need range information [1,2,3,4], while the latter one gets low accuracy without any range information. In order to improve the accuracy of range-free node localization, machine learning is introduced to the localization of WSN [5]. A graph-based localization algorithm was presented using conventional neural networks (CNN) and support vector machine (SVM) [15] in the paper. Another localization method using SVM for largescale WSNs was proposed in [16]. Based on SVM, a new algorithm called support vector machine-based range-free localization (RFSVM) has been presented in [17]. In order to improve the accuracy of node localization in anisotropic WSN, we introduce kernel regression approach to node localization firstly.

Materials and Methods

Regression problem

Sensors Anchors

Proportion of beacons