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Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices

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Titel: Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices
Autoren: Talha Azfar, Kaicong Huang, Ruimin Ke
Quelle: ACM Journal on Autonomous Transportation Systems. 3:1-21
Publication Status: Preprint
Verlagsinformationen: Association for Computing Machinery (ACM), 2025.
Publikationsjahr: 2025
Schlagwörter: FOS: Computer and information sciences, Emerging Technologies (cs.ET), Artificial Intelligence (cs.AI), Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence, Computer Science - Emerging Technologies, Distributed, Parallel, and Cluster Computing (cs.DC)
Beschreibung: Edge sensing and computing are rapidly becoming part of intelligent infrastructure architecture, leading to operational reliance on such systems in disaster or emergency situations. In such scenarios, there is a high chance of power supply failure due to power grid issues, and communication system issues due to base stations losing power or being damaged by the elements, e.g., flooding, wildfires, and so on. Mobile edge computing in the form of unmanned aerial vehicles (UAVs) has been proposed to provide computation offloading from these devices to conserve their battery, while the use of UAVs as relay network nodes has also been investigated previously. This article considers the use of UAVs with further constraints on power and connectivity to prolong the life of the network while also ensuring that the data is received from the edge devices in a timely manner. Reinforcement learning is used to investigate numerous scenarios of various levels of power and communication failure. This approach is able to identify the device most likely to fail in a given scenario, thus providing priority guidance for maintenance personnel. The evacuations of a rural town and urban downtown area are also simulated to demonstrate the effectiveness of the approach at extending the life of the most critical edge devices.
Publikationsart: Article
Sprache: English
ISSN: 2833-0528
DOI: 10.1145/3736643
DOI: 10.48550/arxiv.2501.15305
Zugangs-URL: http://arxiv.org/abs/2501.15305
Rights: CC BY SA
Dokumentencode: edsair.doi.dedup.....e190bacfa8e127db7e2d86dd43ebb8f9
Datenbank: OpenAIRE
Beschreibung
Abstract:Edge sensing and computing are rapidly becoming part of intelligent infrastructure architecture, leading to operational reliance on such systems in disaster or emergency situations. In such scenarios, there is a high chance of power supply failure due to power grid issues, and communication system issues due to base stations losing power or being damaged by the elements, e.g., flooding, wildfires, and so on. Mobile edge computing in the form of unmanned aerial vehicles (UAVs) has been proposed to provide computation offloading from these devices to conserve their battery, while the use of UAVs as relay network nodes has also been investigated previously. This article considers the use of UAVs with further constraints on power and connectivity to prolong the life of the network while also ensuring that the data is received from the edge devices in a timely manner. Reinforcement learning is used to investigate numerous scenarios of various levels of power and communication failure. This approach is able to identify the device most likely to fail in a given scenario, thus providing priority guidance for maintenance personnel. The evacuations of a rural town and urban downtown area are also simulated to demonstrate the effectiveness of the approach at extending the life of the most critical edge devices.
ISSN:28330528
DOI:10.1145/3736643