Tracking Wildfire Assets with Commodity RFID and Gaussian Process Modeling

TL;DR

This paper introduces a cost-effective RFID-based method combined with Gaussian Process modeling to localize wildfire assets in forests with accuracy comparable to GPS, without needing pre-tagged known locations.

Contribution

The paper presents a novel approach that uses Gaussian Processes and environment modeling to localize RFID tags without prior known locations, enhancing wildfire asset tracking.

Findings

Achieves GPS-level localization accuracy with commodity RFID.

Enables tracking of multiple wildfire assets simultaneously.

Reduces costs compared to GPS-based solutions.

Abstract

This paper presents a novel, cost-effective, and scalable approach to track numerous assets distributed in forested environments using commodity Radio Frequency Identification (RFID) targeting wildfire response applications. Commodity RFID systems suffer from poor tag localization when dispersed in forested environments due to signal attenuation, multi-path effects and environmental variability. Current methods to address this issue via fingerprinting rely on dispersing tags at known locations {\em a priori}. In this paper, we address the case when it is not possible to tag known locations and show that it is possible to localize tags to accuracies comparable to global positioning systems (GPS) without such a constraint. For this, we propose Gaussian Process to model various environments solely based on RF signal response signatures and without the aid of additional sensors such as global positioning GPS or cameras, and match an unknown RF to the closest match in a model dictionary. We utilize a new weighted log-likelihood method to associate an unknown environment with the closest environment in a dictionary of previously modeled environments, which is a crucial step in being able to use our approach. Our results show that it is possible to achieve localization accuracies of the order of GPS, but with passive commodity RFID, which will allow the tracking of dozens of wildfire assets within the vicinity of mobile readers at-a-time simultaneously, does not require known positions to be tagged {\em a priori}, and can achieve localization at a fraction of the cost compared to GPS.