Catch Me If You Can: Deep Meta-RL for Search-and-Rescue using LoRa UAV Networks

TL;DR

This paper introduces a deep meta-reinforcement learning approach to optimize UAV-controlled LoRa gateways for search-and-rescue operations, significantly reducing the time needed to locate devices in complex environments.

Contribution

It proposes a novel deep meta-RL framework for UAV gateway control in LoRa-based SAR, enabling rapid adaptation to new environments and improving efficiency over traditional deep RL methods.

Findings

Meta-RL reduces SAR time slots from 141 to 50.

Proposed method adapts quickly to new environments.

Experimental implementation validates the approach.

Abstract

Long range (LoRa) wireless networks have been widely proposed as a efficient wireless access networks for the battery-constrained Internet of Things (IoT) devices. In many practical search-and-rescue (SAR) operations, one challenging problem is finding the location of devices carried by a lost person. However, using a LoRa-based IoT network for SAR operations will have a limited coverage caused by high signal attenuation due to the terrestrial blockages especially in highly remote areas. To overcome this challenge, the use of unmanned aerial vehicles (UAVs) as a flying LoRa gateway to transfer messages from ground LoRa nodes to the ground rescue station can be a promising solution. In this paper, the problem of the flying LoRa (FL) gateway control in the search-and-rescue system using the UAV-assisted LoRa network is modeled as a partially observable Markov decision process. Then, a deep meta-RL-based policy is proposed to control the FL gateway trajectory during SAR operation. For initialization of proposed deep meta-RL-based policy, first, a deep RL-based policy is designed to determine the adaptive FL gateway trajectory in a fixed search environment including a fixed radio geometry. Then, as a general solution, a deep meta-RL framework is used for SAR in any new and unknown environments to integrate the prior FL gateway experience with information collected from the other search environments and rapidly adapt the SAR policy model for SAR operation in a new environment. The proposed UAV-assisted LoRa network is then experimentally designed and implemented. Practical evaluation results show that if the deep meta-RL based control policy is applied instead of the deep RL-based one, the number of SAR time slots decreases from 141 to 50.