• Integrated UAV‑edge framework co‑optimizes route, fleet size, and edge service for wildfire monitoring. • Fire‑history‑weighted clustering prioritizes high‑risk zones, improving patrol efficiency. • QoS‑aware edge assignment balances proximity and computational load. • 2‑opt route optimization with adaptive fleet sizing cuts travel time. • Dynamic emergency rerouting reacts within 233 s, under 300‑s deadline. • Experiments show 70‑84 % faster response, 74‑88 % lower energy, 27‑42 % smaller fleet.
Article Summaries:
- The paper introduces a risk‑aware framework that integrates UAV route planning, fleet sizing, and edge‑computing service allocation for wildfire monitoring and emergency response. It prioritizes high‑risk zones using fire‑history‑weighted clustering, assigns edge nodes based on quality‑of‑service metrics, and optimizes patrol routes with a 2‑opt algorithm while dynamically adjusting fleet size. A real‑time emergency rerouting module ensures rapid response to incidents. Experiments show the framework cuts average response time by 70-84 %, energy use by 74-88 %, and fleet size by 27-42 % versus GA, PSO, and greedy baselines, with emergency reactions completing within 233 s-well under the 300‑second deadline.
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