A Risk-Aware UAV-Edge Service Framework for Wildfire Monitoring and Emergency Response

TL;DR

This paper presents an integrated UAV-edge framework for wildfire monitoring that optimizes routing, fleet size, and edge services, significantly reducing response time and energy use while ensuring rapid emergency rerouting.

Contribution

It introduces a novel co-optimization framework that jointly addresses UAV routing, fleet sizing, and edge service provisioning for wildfire response, considering interdependent subproblems.

Findings

Response time reduced by up to 84.2%

Energy consumption decreased by up to 88.4%

Emergency rerouting responds within 233 seconds

Abstract

Wildfire monitoring demands timely data collection and processing for early detection and rapid response. UAV-assisted edge computing is a promising approach, but jointly minimizing end-to-end service response time while satisfying energy, revisit time, and capacity constraints remains challenging. We propose an integrated framework that co-optimizes UAV route planning, fleet sizing, and edge service provisioning for wildfire monitoring. The framework combines fire-history-weighted clustering to prioritize high-risk areas, Quality of Service (QoS)-aware edge assignment balancing proximity and computational load, 2-opt route optimization with adaptive fleet sizing, and a dynamic emergency rerouting mechanism. The key insight is that these subproblems are interdependent: clustering decisions simultaneously shape patrol efficiency and edge workloads, while capacity constraints feed back into feasible configurations. Experiments show that the proposed framework reduces average response time by 70.6--84.2%, energy consumption by 73.8--88.4%, and fleet size by 26.7--42.1% compared to GA, PSO, and greedy baselines. The emergency mechanism responds within 233 seconds, well under the 300-second deadline, with negligible impact on normal operations.