LoRaCompass: Robust Reinforcement Learning to Efficiently Search for a LoRa Tag

TL;DR

LoRaCompass is a robust reinforcement learning approach that efficiently locates LoRa tags in unknown environments, overcoming domain shift and signal fluctuation issues to achieve high success rates and reduced search paths.

Contribution

It introduces a novel RL model with a spatially-aware feature extractor and UCB-inspired exploration for robust LoRa tag localization under challenging conditions.

Findings

Achieved over 90% success rate in diverse environments.

Reduced search path length by 40% compared to existing methods.

Demonstrated effectiveness in ground and drone-assisted scenarios.

Abstract

The Long-Range (LoRa) protocol, known for its extensive range and low power, has increasingly been adopted in tags worn by mentally incapacitated persons (MIPs) and others at risk of going missing. We study the sequential decision-making process for a mobile sensor to locate a periodically broadcasting LoRa tag with the fewest moves (hops) in general, unknown environments, guided by the received signal strength indicator (RSSI). While existing methods leverage reinforcement learning for search, they remain vulnerable to domain shift and signal fluctuation, resulting in cascading decision errors that culminate in substantial localization inaccuracies. To bridge this gap, we propose LoRaCompass, a reinforcement learning model designed to achieve robust and efficient search for a LoRa tag. For exploitation under domain shift and signal fluctuation, LoRaCompass learns a robust spatial representation from RSSI to maximize the probability of moving closer to a tag, via a spatially-aware feature extractor and a policy distillation loss function. It further introduces an exploration function inspired by the upper confidence bound (UCB) that guides the sensor toward the tag with increasing confidence. We have validated LoRaCompass in ground-based and drone-assisted scenarios within diverse unseen environments covering an area of over 80km^2. It has demonstrated high success rate (>90%) in locating the tag within 100m proximity (a 40% improvement over existing methods) and high efficiency with a search path length (in hops) that scales linearly with the initial distance.