Target Localization in Wireless Sensor Networks using Error Correcting Codes

TL;DR

This paper introduces an energy-efficient, coding-based iterative target localization scheme for wireless sensor networks that is robust against malicious sensors and channel imperfections, achieving high detection accuracy with lower computational cost.

Contribution

The work presents a novel coding-based iterative localization method that is resilient to Byzantine faults and fading channels, with proven asymptotic perfect detection capabilities.

Findings

Achieves high target detection probability even with Byzantine sensors.

Provides performance bounds and asymptotic analysis for the proposed schemes.

Offers computationally efficient localization comparable to MLE, robust to errors.

Abstract

In this work, we consider the task of target localization using quantized data in Wireless Sensor Networks (WSNs). We propose an energy efficient localization scheme by modeling it as an iterative classification problem. We design coding based iterative approaches for target localization where at every iteration, the Fusion Center (FC) solves an M-ary hypothesis testing problem and decides the Region of Interest (ROI) for the next iteration. The coding based iterative approach works well even in the presence of Byzantine (malicious) sensors in the network. We further consider the effect of non-ideal channels. We suggest the use of soft-decision decoding to compensate for the loss due to the presence of fading channels between the local sensors and the FC. We evaluate the performance of the proposed schemes in terms of the Byzantine fault tolerance capability and probability of detection of the target region. We also present performance bounds which help us in designing the system. We provide asymptotic analysis of the proposed schemes and show that the schemes achieve perfect region detection irrespective of the noise variance when the number of sensors tends to infinity. Our numerical results show that the proposed schemes provide a similar performance in terms of Mean Square Error (MSE) as compared to the traditional Maximum Likelihood Estimation (MLE) but are computationally much more efficient and are resilient to errors due to Byzantines and non-ideal channels.