## 5 Simulation Results and Discussions
### Impact of Node Mobility Speed  
Node mobility speed is a key factor affecting the stability and performance of FANETs. Higher mobility speeds result in more frequent topology changes and increased link-break probability, imposing greater demands on the robustness and adaptability of routing protocols. In this subsection, we investigate the performance of each protocol across four core metrics under various mobility speeds to assess their adaptability in highly dynamic environments. The network is fixed at 70 UAV nodes with 20 CBR pairs. Each speed scenario is simulated 30 times, and all results are reported with a 95% confidence interval. As illustrated in Figure 5a, the packet loss rate of Traj-Q-GPSR remains nearly constant as the UAV average speed increases, while GPSR's loss rate rises from 21.8% up to 26.8%, with a pronounced jump around 20 m/s and a further increase nearing 27%. This divergence stems from their differing robustness to high-mobility links: GPSR relies solely on one-hop greedy forwarding, so when nodes move rapidly links break more frequently and retransmissions surge. In contrast, Traj-Q-GPSR uses two-hop neighbor tables and trajectory information to select more stable relays. It also applies queue-based rules to avoid sending packets over links that are likely to break, reducing both retransmission overhead and packet loss. At 30 m/s, Traj-Q-GPSR's loss rate is 17% lower than GPSR's, clearly demonstrating its superior link reliability in highly dynamic environments. As shown in Figure 5b, when the UAV speed increases from 10 m/s to 30 m/s, the end-to-end delay of GPSR first rises from 878.7 ms to 991.4 ms, and then drops to 827.5 ms. This trend is mainly caused by prolonged fallback to recovery mode under low-to-moderate mobility, where broken links result in delayed forwarding. At higher speeds, however, faster topology updates enable quicker discovery of alternative relay paths and reduce average waiting time. In contrast, Traj-Q-GPSR maintains a stable delay range of 134-170 ms across all speeds, benefiting from trajectory prediction for preemptive two-hop neighbor construction and Q-learning-based robust relay selection. This allows Traj-Q-GPSR to sustain higher routing connectivity and forwarding efficiency under dynamic conditions, achieving an approximate 80% reduction in overall delay. Figure 5c shows that Traj-Q-GPSR consistently outperforms GPSR in routing efficiency, and both exhibit a synchronized \"rise-then-fall\" trend as speed increases. This behavior closely mirrors the combined characteristics of packet loss rate and end-to-end delay discussed earlier. At moderate speeds, topology updates occur at an optimal rate, yielding good inter-node connectivity and enhanced path validity, which in turn boosts successful packet delivery and reduces average hop count, driving up routing efficiency. As speed increases, frequent link breakages reduce route stability. Although delay may slightly decrease, rising packet loss lowers throughput, leading to reduced efficiency. By contrast, Traj-Q-GPSR leverages trajectory awareness and Q-learning to mitigate high-mobility link fluctuations, maintaining superior efficiency across all speed regimes--achieving a peak improvement of over 52% compared to GPSR. As shown in Figure 5d, Traj-Q-GPSR consistently maintains a high and stable throughput performance, with values ranging from \(1.8\times 10^{5}\) bps to \(2.0\times 10^{5}\) bps. In contrast, GPSR achieves significantly lower throughput, fluctuating around \(1.0\times 10^{5}\) to \(1.2\times 10^{5}\) bps. This trend closely aligns with the packet loss rate and routing efficiency observed earlier, particularly at the speed of 15 m/s, where the lowest packet loss and highest efficiency result in elevated throughput, indicating a synergistic optimization of overall transmission quality. Even under high-speed mobility, Traj-Q-GPSR leverages trajectory awareness and reinforcement learning strategies to adapt swiftly, thereby preserving throughput stability.