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

Source: arXiv - 2602.19742v1

Overview

The paper presents a risk‑aware UAV‑edge service framework that lets drones and edge servers work together to monitor wildfires in real time. By jointly optimizing flight routes, fleet size, and edge‑computing assignments, the authors achieve dramatically faster detection and response while keeping drones’ energy use and communication loads in check.

Key Contributions

• Fire‑history‑weighted clustering that prioritizes high‑risk zones, guiding both patrol routes and edge‑server placement.
• QoS‑aware edge assignment that balances proximity to UAVs with the computational load of image‑analysis tasks.
• Integrated route‑planning + adaptive fleet sizing using a 2‑opt heuristic, allowing the system to scale the number of drones up or down on the fly.
• Dynamic emergency rerouting that instantly reshapes missions when a fire is detected, meeting a 300‑second response deadline.
• Comprehensive evaluation showing 70‑84 % lower response times, 74‑88 % less energy consumption, and up to 42 % fewer drones versus GA, PSO, and greedy baselines.

Methodology

1. Risk‑driven clustering – Historical fire data is fed into a clustering algorithm that tags cells with a “risk weight.” High‑risk clusters become priority waypoints for UAV patrols.
2. Edge‑service provisioning – Each UAV streams sensor data (e.g., infrared images) to the nearest edge node that still has enough CPU/GPU capacity. The assignment problem is solved with a lightweight QoS metric that mixes latency and load.
3. Route optimization & fleet sizing – Starting from the clustered waypoints, a classic 2‑opt local‑search refines each drone’s tour. Simultaneously, a simple linear model checks whether the current fleet can cover all clusters within the required revisit time; if not, the fleet size is increased (or decreased when possible).
4. Emergency handling – When a fire is detected, the system triggers a fast “reroute” sub‑routine: the affected UAV heads straight to the hotspot, while neighboring drones are reassigned to fill any coverage gaps. The whole process completes in under 233 seconds.

All components are modular, so they can be swapped for alternative clustering, routing, or edge‑allocation algorithms without breaking the overall pipeline.

Results & Findings

The numbers indicate that the integrated approach not only speeds up detection but also cuts operational costs (fewer drones, lower battery drain). Importantly, the emergency rerouting adds only a negligible overhead to the normal patrol schedule.

Practical Implications

• Cost‑effective wildfire surveillance – Agencies can deploy fewer drones while still meeting strict monitoring intervals, translating to lower capital and maintenance expenses.
• Edge‑first analytics – By offloading heavy image‑processing to nearby edge nodes, latency stays low and drones conserve battery for flight, a pattern that can be reused for other IoT‑heavy scenarios (e.g., disaster inspection, agricultural scouting).
• Scalable, plug‑and‑play architecture – The modular design lets municipalities integrate existing UAV fleets and edge infrastructure without a full system redesign.
• Rapid emergency response – The sub‑second rerouting logic can be embedded into real‑time command‑and‑control dashboards, giving first responders actionable intel within minutes of fire ignition.
• Generalizable risk‑aware routing – The clustering‑by‑risk concept can be applied to any domain where historical incident data informs patrol priorities (e.g., security drones, infrastructure inspection).

Limitations & Future Work

• Simulation‑centric validation – Experiments were performed in a simulated environment; real‑world field trials are needed to confirm robustness against wind, GPS drift, and communication loss.
• Static edge topology – The current model assumes fixed edge server locations; future work could explore mobile edge nodes (e.g., edge‑enabled trucks) that relocate based on evolving fire fronts.
• Single‑objective focus – While the framework balances several constraints, it does not explicitly optimize for multi‑objective trade‑offs (e.g., cost vs. detection probability) that stakeholders might prioritize differently.
• Security & privacy – The paper does not address secure data transmission or privacy of the collected imagery, which could be critical for public‑sector deployments.

By addressing these gaps, the approach could evolve into a production‑ready platform for large‑scale, risk‑aware UAV‑edge services beyond wildfire monitoring.

Authors

• Yulun Huang
• Zhiyu Wang
• Rajkumar Buyya

Paper Information

• arXiv ID: 2602.19742v1
• Categories: cs.DC
• Published: February 23, 2026
• PDF: Download PDF