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.

翻译：长距离（LoRa）无线网络已被广泛提出作为电池受限的物联网（IoT）设备的高效无线接入网络。在许多实际的搜索与救援（SAR）行动中，一个具有挑战性的问题是定位失踪人员所携带设备的位置。然而，基于LoRa的IoT网络用于SAR行动时，由于地面障碍物（尤其在偏远地区）造成的高信号衰减，其覆盖范围将受到限制。为克服这一挑战，使用无人机（UAV）作为飞行LoRa网关，将地面LoRa节点的消息传输至地面救援站，是一种有前景的解决方案。本文中，无人机辅助LoRa网络的搜救系统中，飞行LoRa（FL）网关的控制问题被建模为部分可观测马尔可夫决策过程。随后，提出了一种基于深度元强化学习（deep meta-RL）的策略，以在SAR行动中控制FL网关的轨迹。为初始化所提出的深度元强化学习策略，首先设计了一种基于深度强化学习（deep RL）的策略，在包含固定无线电几何结构的固定搜索环境中确定自适应FL网关轨迹。然后，作为通用解决方案，采用深度元强化学习框架，用于任何新未知环境中的SAR任务，该框架整合先前的FL网关经验与其他搜索环境收集的信息，并快速适应新环境中的SAR策略模型。随后，对所提出的无人机辅助LoRa网络进行了实验设计和实施。实际评估结果表明，若采用基于深度元强化学习的控制策略替代深度强化学习策略，SAR时隙数从141减少至50。