Big Data on the Fly: UAV-Mounted Mobile Edge Computing for Disaster Management

• Published in: IEEE transactions on network science and engineering Vol. 7; no. 4; pp. 2620 - 2630
• Main Authors: Xu, Jianwen, Ota, Kaoru, Dong, Mianxiong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Atmospheric modeling; Channel capacity; Communication; Computational modeling; Disaster management; Disasters; Edge computing; markov chain; Mobile computing; mobile edge computing; Performance measurement; Signal to noise ratio; Task analysis; UAV technology; Unified modeling language; Wireless communication
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2020.3016569

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.