## 5 Simulation Results and Discussions
### Impact of Node Density  
First, we analyze the impact of UAV node density on network performance. As shown in Table 1, a fixed simulation area is configured, and the node mobility speed ranges from 15 m/s to 25 m/s in this section. For each parameter setting, 30 independent simulations are conducted, and a 95% confidence interval is adopted. Figure 4 shows the impact of node density on network performance. At any node density, the proposed Traj-Q-GPSR algorithm consistently achieves a significantly lower packet loss rate compared to the traditional GPSR. As node density increases, the packet loss curve reflects a trade-off between improved connectivity and intensified channel contention. Specifically, when the number of nodes increases from 30 to around 70, the connectivity rapidly improves and path breaks are significantly reduced. Leveraging trajectory prediction and two-hop queue optimization, Traj-Q-GPSR reduces the packet loss rate from approximately 33.13% to 25.5%. In contrast, traditional GPSR, relying solely on instant location and single-hop decision-making, struggles with frequent neighbor changes and inefficient recovery, leading to an increased packet loss rate, peaking at 40.66%. As the number of nodes continues to increase from 70 to 100, the network enters a high-density phase. The redundancy among nodes increases, and alternative routes become more available, allowing GPSR to partially compensate for local routing failures and reducing the loss rate to 35.46%. Meanwhile, Traj-Q-GPSR, by proactively filtering invalid packets and applying queue-aware intelligent routing, effectively mitigates congestion and queuing losses under dense scenarios, further reducing the packet loss rate to 22.76%. As can be seen from the end-to-end delay curves in Figure 4b, across all regimes, Traj-Q-GPSR achieves substantially lower delays than the conventional GPSR. Although GPSR's latency decreases modestly as density grows, it remains around 1000 ms. In contrast, Traj-Q-GPSR maintains delays below 500 ms, rapidly dropping to 191.94 ms at moderate densities and stabilizing near 100 ms at high densities. The observed performance gap stems from the fundamental differences in their forwarding strategies. GPSR employs a one-hop greedy forwarding approach, which often triggers perimeter recovery when routing voids occur, resulting in frequent detours and retransmissions. In contrast,Traj-Q-GPSR utilizes trajectory information and two-hop neighbor awareness to enable more forward-looking and informed relay selection. It also integrates queue-aware packet management to improve forwarding efficiency and reduce unnecessary overhead. Together, these innovations dramatically curtail the detour and retransmission overheads that dominate GPSR's delay profile. As shown in Figure 4c, routing efficiency for both Traj-Q-GPSR and GPSR decreases as node count grows from 30 to 100, but Traj-Q-GPSR consistently outperforms GPSR. At low densities, adding nodes rapidly improves connectivity but increases average hop-count and forwarding overhead even faster, causing the ratio \(\frac{\sum totalRx}{\sum totalTx\times HopCount}\) to drop. Once the topology nears saturation, gains in delivered packets and in transmitted-hop product balance each other, stabilizing efficiency. In dense regimes, Traj-Q-GPSR's trajectory prediction and two-hop neighbor awareness fully exploit available paths, yielding low delay and high reliability, so its efficiency remains at a high plateau. Figure 4d plots throughput as node count increases from 30 to 100. Enhanced connectivity and spatial reuse drive overall growth: GPSR rises from approximately \(1.03\times 10^{5}\) bps to \(1.40\times 10^{5}\) bps, whereas Traj-Q-GPSR climbs from \(1.32\times 10^{5}\) bps to \(1.92\times 10^{5}\) bps. This trend mirrors reductions in packet loss and latency, reflecting more multi-hop paths, fewer link failures and retransmissions, and lower MAC-layer contention at high density.