Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction

Flying ad hoc networks (FANETs) have highly dynamic and energy-limited characteristics. Compared with traditional mobile ad hoc networks, their nodes move faster and their topology changes more frequently. Therefore, the design of routing protocols faces greater challenges. The existing routing schemes rely on frequent and fixed-interval Hello transmissions, which exacerbates network load and leads to high communication energy consumption and outdated location information. MP-QGRD combined with the extended Kalman filter (EKF) is used for node position prediction, and the Hello packet transmission interval is dynamically adjusted to optimize neighbor discovery. At the same time, reinforcement learning methods are used to comprehensively consider link stability, energy consumption, and communication distance for routing decisions. The simulation results show that compared to QMR, QGeo, and GPSR, MP-QGRD has an increased packet delivery rate, end-to-end latency, and communication energy consumption by 10%, 30%, and 15%, respectively.