Abstract
It is a critical but difficult task to provide information transmission and computation services to first responders or rescue teams in disaster-hit areas as catastrophes may cause casualties and massive damage to human-made facilities. Unmanned aerial vehicles (UAVs) are a great choice to provide these services in such areas due to their inherent mobility and easy-to-deploy properties. This paper proposes a four-layer data-driven disaster response architecture, which can leverage UAV-carried Fog (UAVFog) nodes’ communication and computation capabilities as well as deep learning’s ability to extract mission-critical information from sensory data. In our proposal, UAVs are in charge of sensing and interconnecting disaster-hit areas. On the other hand, UAVFog nodes handle the data processing and decision-making tasks. We identify the functions and entities in each layer, and four key advantages of the proposed architecture are presented. The structure of the UAVFog node is detailed, and its technical requirements are summarized. Then, an injury diagnosis scenario is presented as a motivating use case with performance analysis. Finally, several open issues for future research are highlighted.
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Wei, X., Li, L., Tang, C., Subramaniam, S. (2020). UAVFog-Assisted Data-Driven Disaster Response: Architecture, Use Case, and Challenges. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12343. Springer, Cham. https://doi.org/10.1007/978-3-030-62008-0_41
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DOI: https://doi.org/10.1007/978-3-030-62008-0_41
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