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Long range (LoRa) wireless networks have been widely proposed as a efficientwireless access networks for the battery-constrained Internet of Things (IoT)devices. In many practical search-and-rescue (SAR) operations, one challengingproblem 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 coveragecaused by high signal attenuation due to the terrestrial blockages especiallyin highly remote areas. To overcome this challenge, the use of unmanned aerialvehicles (UAVs) as a flying LoRa gateway to transfer messages from ground LoRanodes 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-rescuesystem using the UAV-assisted LoRa network is modeled as a partially observableMarkov decision process. Then, a deep meta-RL-based policy is proposed tocontrol the FL gateway trajectory during SAR operation. For initialization ofproposed deep meta-RL-based policy, first, a deep RL-based policy is designedto determine the adaptive FL gateway trajectory in a fixed search environmentincluding a fixed radio geometry. Then, as a general solution, a deep meta-RLframework is used for SAR in any new and unknown environments to integrate theprior FL gateway experience with information collected from the other searchenvironments and rapidly adapt the SAR policy model for SAR operation in a newenvironment. The proposed UAV-assisted LoRa network is then experimentallydesigned and implemented. Practical evaluation results show that if the deepmeta-RL based control policy is applied instead of the deep RL-based one, thenumber of SAR time slots decreases from 141 to 50.

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