Target Localization in Wireless Sensor Networks Using Error Correcting Codes

Abstract

Abstract-In this work, we consider the task of target localization using quantized data in Wireless Sensor Networks (WSNs). We propose a computationally efficient localization scheme by modeling it as an iterative classification problem. We design coding theory 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 theory 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.

A fast energy-aware multi-target detection technique using binary wireless sensors

“…Sensor networks for surveillance applications, and in particular, for detection, are investigated in many papers covering a plethora of system designs with a wide class of objectives and constraints for specific applications. In [1], a distributed vector estimation for power-and bandwidthconstrained WSNs is considered, where the fusion center reconstructs the unknown vector by a linear estimation, and in [2], a computationally efficient localization scheme was…”

Study on Multi-Source Localization of Wireless Sensor Network Based on Quantitative Information

“…Masazade [7] put forward cyclic source localization algorithm against heterogeneous sensor networks, which firstly obtains the posterior probability density function through the Monte-Carlo method, then proposes two node selection methods, and finally estimates the location of signal sources according to the selected nodes and objective function. Vempaty [8] put forward a cyclic localization method based on coding theory. In each cycle, the base stations estimate the location of signal sources though solving the problem of M-nary hypothesis testing and meanwhile determine the areas of interest of the next cycle.…”

A Performance Limit of TOA-Based Location-Aware Wireless Networks With Ranging Outliers

“…In [6], it has been analyzed that these ranging outliers lead to a large increase in localization error when the least-square method is applied. To address the problem caused by ranging outliers, various algorithms are proposed, such as linear programming [7], error correcting codes [8] and sum-product algorithm [9]. Nevertheless, to the best knowledge of the authors, performance limits of these algorithms have not been analyzed.…”