Edge-Enhanced Attention Mechanism for Dynamic UAV Formation Fault Detection

Unmanned Aerial Vehicle (UAV) formations play a vital role in modern mission-critical applications, such as disaster response, surveillance, and Search and Rescue (SAR) operations. Ensuring the operational health of Unmanned Aerial Vehicles (UAVs) in the most often dynamic environments in which they are deployed requires a robust, scalable, and realtime fault detection mechanism to ensure mission integrity. In this paper, we propose an Edge-Enhanced Graph Attention Network (EE-GAT) model for fault detection in a dynamic UAV formation. The proposed method integrates node-level features such as altitude, velocity, battery level, with edge-level network proximity measures (RSSI and SINR) into an attention-based graph learning framework to enable accurate detection of UAV position status in a leader-follower topology. Through extensive simulations in NS3 v3.42, we demonstrate that our EE-GAT achieves a high fault detection accuracy of up to $\mathbf{9 6. 1 \%}$. Our approach outperforms baseline models, offering a lightweight, and cost-effective solution for real-time UAV health monitoring in dynamic and large-scale formations.