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

2025-01-25

AI Summary

To address power outages, communication disruptions, and insufficient battery lifetime of critical devices in post-disaster edge sensing and computing systems, this paper proposes a UAV-assisted dynamic edge relay network architecture. Methodologically, it introduces proximal policy optimization (PPO)-based reinforcement learning for the first time to jointly optimize UAV energy constraints, wireless link reliability, and priority-aware task scheduling across edge devices, integrated with device state modeling and real-time risk assessment. The key innovation lies in unifying fault prediction, adaptive resource orchestration, and operational decision support within a single framework. Experimental evaluation in urban–rural evacuation simulations demonstrates a 47% extension in battery lifetime for critical edge devices, an F1-score of 0.92 for high-risk device identification, and a 32% improvement in emergency response timeliness.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Edge sensing and computing is 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 etc. 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 paper 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 nodes 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.

Problem

• Disaster Response
• Unmanned Aerial Vehicle (UAV) Technology
• Network Lifespan Extension

Innovation

• Edge Computing Optimization
• Unmanned Aerial Vehicles (UAVs)
• Machine Learning Prediction