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

2026-02-23

AI Summary

This study addresses the joint optimization of UAV trajectories, fleet size, and edge service deployment for wildfire monitoring by proposing a risk-aware UAV-edge协同 framework. The framework uniquely integrates historical fire risk data, edge server load, and path planning into a unified model, identifying high-risk zones through fire-history-weighted clustering. It minimizes end-to-end response time via QoS-aware task allocation, 2-opt path optimization, adaptive fleet scheduling, and a dynamic emergency rerouting mechanism. Experimental results demonstrate that, compared to genetic algorithm (GA), particle swarm optimization (PSO), and greedy baselines, the proposed approach reduces average response time by 70.6–84.2%, decreases energy consumption by 73.8–88.4%, and shrinks required fleet size by 26.7–42.1%. Furthermore, emergency responses are completed within 233 seconds—well under the 300-second deadline—without compromising routine operations.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Algorithm Configuration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• wildfire monitoring
• UAV-edge computing
• response time minimization
• resource constraints
• emergency response

Innovation

• risk-aware UAV-edge framework
• fire-history-weighted clustering
• QoS-aware edge assignment
• adaptive fleet sizing
• dynamic emergency rerouting