After disasters, network communication is highly susceptible to disruption. In this case, we may need solutions without original architectures to meet the requirements of connectivity and communication. As a research hotspot, existing studies and practices in disaster management are often costly and may have to rely on differentiated strategies to deal with actual situations. In this paper, we choose UAVs as edge node carriers and LoRaWAN (Long Range Wide Area Networking) as a communication method in coping with mobile edge computing (MEC) for disaster management. Here we propose UAV-mounted MEC task management strategies to achieve emergency communication enabled by LoRaWAN. The system model includes two parts, air-to-ground and remote-to-air, in which we choose LoS/NLoS path loss model and log-distance to describe the connections. The experiment results show that our strategy can achieve low-cost, long-range MEC service, which can be quickly deployed in the affected area after disasters. We also choose path loss, SNR (signal-noise ratio), and channel capacity as performance metrics and prove that our solutions can increase the channel capacity while maintaining the same level of path loss and SNR.

中文翻译：

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动态大数据：用于灾害管理的无人机安装移动边缘计算

灾难发生后，网络通信极易中断。在这种情况下，我们可能需要没有原始架构的解决方案来满足连接和通信的要求。作为研究热点，现有的灾害管理研究和实践往往成本高昂，可能不得不依赖差异化的策略来应对实际情况。在本文中，我们选择无人机作为边缘节点载体，选择 LoRaWAN（远程广域网）作为通信方法，以应对移动边缘计算（MEC）进行灾害管理。在这里，我们提出了无人机安装的 MEC 任务管理策略，以实现 LoRaWAN 支持的紧急通信。系统模型包括空对地和远程对空两部分，其中我们选择视距/非视距路径损耗模型和对数距离来描述连接。实验结果表明，我们的策略可以实现低成本、远程的MEC服务，可以在灾后快速部署到受灾地区。我们还选择路径损耗、SNR（信噪比）和信道容量作为性能指标，并证明我们的解决方案可以增加信道容量，同时保持相同的路径损耗和 SNR 水平。