MMJP: A Mobility and Congestion-Aware Routing Protocol for Flying Ad Hoc Networks

Unmanned Aerial Vehicle networks, also known as Flying Ad Hoc Networks (FANETs), offer significant advantages in applications such as disaster response and emergency communications, owing to their rapid deployment, wide-area coverage, and strong survivability. Routing protocols are critical to FANETs performance, ensuring reliable communication through dynamic path maintenance and three-dimensional (3D) topology optimization. However, most existing protocols are designed primarily based on node mobility or network topology, often overlooking traffic distribution. This can lead to severe congestion in high-traffic areas, resulting in increased end-to-end delays, delayed route updates, and packet loss. While some existing methods address congestion through flow prediction, they frequently disregard topological relationships among nodes, reducing the accuracy of predictions. To address these challenges, this paper proposes a Multi-Metric Joint Prediction (MMJP) routing protocol based on a Spatio-Temporal Graph Convolutional Network (STGCN). MMJP jointly models traffic dynamics and topological structure to enhance prediction accuracy and congestion control. The proposed STGCN model exchanges information with neighboring nodes, aggregates their features, and learns spatio-temporal representations to forecast future node resource availability. The resulting predicted network state is used to select routes that avoid congestion-prone areas. Simulation results demonstrate that MMJP outperforms benchmark protocols, including Destination-Sequenced Distance Vector (DSDV), 3D Geographic Greedy Routing (GREEDY), and the predictive protocol OPAR. Specifically, MMJP achieves over 30$\%$higher Packet Delivery Ratio and a 35$\%$improvement in throughput compared to DSDV and GREEDY, and outperforms OPAR by approximately 10$\%$in both metrics.