Collaborative Optimization Framework for AAV Clusters: Enhancing Energy Efficiency, Reliability, and Stability

Existing Unmanned Aerial Vehicle (UAV) clusters lack a formalized model, fail to consider external interference factors, and overlook the need for dynamic cluster maintenance to ensure stability and coordination in open scenarios. To address these issues, we propose an UAV collaborative cluster formation method suitable for open scenarios, which can form an energy-efficient, reliable, and stable UAV cluster even in external interferences. First, we present a UAV node promotion method based on mobility similarity and connectivity. Then, we formalize a collaborative UAV cluster model based on the energy efficiency, reliability, and stability among UAV nodes. Next, we propose a formation method for UAV clusters based on Pareto optimality and provide a maintenance method for clusters. Extensive simulation results demonstrate that the proposed method significantly outperforms state-of-the-art (SOTA) approaches. Specifically, in open scenarios, our method improves average cluster efficiency by up to 6.9%, enhances average cluster reliability by 12.4%, enhances average cluster stability by 14.2%, and extends the average cluster survival time and the average node survival time by 18.1% and 17.2% compared to the best-performing baseline, verifying the superior effectiveness and robustness of the proposed framework.