Enhancing Received Signal Strength-Based Localization through Coverage Hole Detection and Recovery.

Shuangjiao Zhai,Zheng Wang,Dingyi Fang,Xiaojiang Chen,Zhanyong Tang,Zhanglei Li,Dajin Wang,Feng Chen,Qingpei Li

doi:10.3390/s18072075

Abstract

In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In this paper, we focus on detecting and recovering coverage holes of WSNs to enhance RSSI-based localization techniques whose coverage model is an ellipse. We propose an algorithm inspired by Voronoi tessellation and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes and can reach any set coverage rate, up to 100% coverage.

Highlights

In wireless sensor networks (WSNs), a thorough coverage of the target regions is of vital importance to the performance of its applications
We propose an algorithm to detect and recover coverage holes of WSNs based on Radio Signal Strength Indicator (RSSI)-based localization techniques whose coverage model is an ellipse [25]
We systematically investigate the coverage model of RSSI-based localization techniques, and ellipse coverage model is derived from theoretical analysis and experimental verification

Summary

Introduction

In wireless sensor networks (WSNs), a thorough coverage of the target regions is of vital importance to the performance of its applications. This assumption does not hold in many localization scenarios This assumption does not hold for a Radio Signal Strength Indicator (RSSI)-based localization application [23,24,25,26,27,28], whose task is to locate objects according to the disturbance of the objects to several communication links. In this scenario, the present of a giant pandas will change the RSSI signal pattern between sensors, which is matched against training samples to identify if a panda was appearing in the target area. The evaluation results show that the proposed method is robust to noisy environment and environmental change

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