Towards Effective Forest Fire Response: A Cloud–Edge Collaborative UAV Deployment Strategy for Rapid Situational Awareness

Rapid and balanced situational awareness of fire fronts is critical for effective initial response to forest fires, yet suboptimal task planning for Unmanned Aerial Vehicle (UAV) swarms can delay intelligence delivery. This paper presents a cloud–edge collaborative approach that integrates edge-driven rapid task partitioning with cloud-based global workload balancing, explicitly addressing the NP-hard multiple traveling salesman problem underlying multi-UAV reconnaissance. At the edge, a fire-spread-informed line clustering algorithm quickly assigns monitoring points to UAVs, exploiting low-latency processing for initial sectorization. The cloud then refines this allocation through a novel cooperative–competitive task transfer mechanism that minimizes the makespan. Extensive simulations and a real-world case study based on the 2020 Liangshan wildfire show that the proposed method reduces makespan by up to 24.5% compared to conventional centralized and distributed baselines, while remaining robust under severe communication constraints.