Decentralized Source Localization without Sensor Parameters in Wireless Sensor Networks

TL;DR

This paper introduces two novel methods for source localization in decentralized wireless sensor networks that do not require prior knowledge of sensor parameters, demonstrating effective localization through simulations.

Contribution

It proposes hitting set and feature selection methods for source localization without sensor parameter knowledge, extending to multiple sources and analyzing sample complexity.

Findings

Effective source localization demonstrated in simulations

Methods outperform centroid and existing estimators

Extended to multiple sources and maximum likelihood

Abstract

This paper studies the source (event) localization problem in decentralized wireless sensor networks (WSNs) under the fault model without knowing the sensor parameters. Event localizations have many applications such as localizing intruders, Wifi hotspots and users, and faults in power systems. Previous studies assume the true knowledge (or good estimates) of sensor parameters (e.g., fault model probability or Region of Influence (ROI) of the source) for source localization. However, we propose two methods to estimate the source location in this paper under the fault model: hitting set approach and feature selection method, which only utilize the noisy data set at the fusion center for estimation of the source location without knowing the sensor parameters. The proposed methods have been shown to localize the source effectively. We also study the lower bound on the sample complexity requirement for hitting set method. These methods have also been extended for multiple sources localizations. In addition, we modify the proposed feature selection approach to use maximum likelihood. Finally, extensive simulations are carried out for different settings (i.e., the number of sensor nodes and sample complexity) to validate our proposed methods in comparison to centroid, maximum likelihood, FTML, SNAP estimators.