# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 4 Evaluation
### Comparison of Experimental Results
Figure 3b compares the delay performance of the four protocols. As node speed increases, GPSR exhibits more routing errors compared to QGeo, leading to increasing delays. In contrast, QMR and MP-QGRD demonstrate more adaptability. QMR selects nodes with lower delays for routing decisions, but rapid changes in neighbor nodes cause routing changes and increased delays. MP-QGRD accurately predicts node positions and selects nodes closer to the destination as relays. Therefore, MP-QGRD outperforms QMR in delay performance. Figure 3c shows that as nodes move faster, their communication energy consumption continues to increase. GPSR and QGeo routing protocols do not consider energy consumption, resulting in relatively high energy consumption. QMR and MP-QGRD consider energy consumption in their reward functions, but MP-QGRD also considers link retention time and sets an energy threshold, selecting nodes with energy above the threshold for data transmission. Therefore, the energy consumption of the MP-QGRD routing protocol is lower than that of other routing protocols. Figure 3d demonstrates that as node speed increases, the routing overhead of the four protocols continues to rise. GPSR is a stateless protocol, where the rapid movement  
Figure 3: Simulation results of node velocity variation: (**a**) packet delivery ratio; (**b**) end-to-end delay; (**c**) energy consumption in communication; (**d**) routing overhead. of nodes leads to frequent changes in the topology structure and a high number of route retransmissions, resulting in gradually increasing routing overhead. However, QGeo shows lower overhead compared to GPSR by Q-learning technology. Although QMR introduces a neighbor discovery mechanism, it does not dynamically adjust the sending interval of Hello packets to promptly discover dynamic neighbor changes, leading to increased routing overhead. Dynamically adjusting the sending interval of Hello packets during neighbor discovery reduces routing overhead. Therefore, the routing overhead of MP-QGRD is the lowest. The strategy of sending Hello packets at fixed time intervals will result in an average increase of about 15-20% in communication overhead. To further demonstrate the performance of our algorithm, we presented the changes in metrics corresponding to different numbers of drone nodes at an average movement speed of 70 m/s. Figure 3(a) illustrates how the packet delivery ratio of the four routing protocols increases as the number of nodes increases. Among them, MP-QGRD outperforms the others significantly. MP-QGRD enhances relay node availability through mobility prediction and selects the most stable link for forwarding. As the number of nodes increases, network connectivity improves, resulting in higher PDR. GPSR performs the poorest due to its reliance solely on node positions rather than global information. QGeo, by employing Q-learning to select the optimal path, outperforms GPSR. QMR, by evaluating neighbor relationships to determine the best node for forwarding, performs better than QGeo. Figure 3(b) demonstrates the impact of node count changes on end-to-end delay. MP-QGRD exhibits the lowest latency in low-density networks by reducing routing construction time through accurate position prediction. Although delays increase with more nodes, MP-QGRD remains significantly lower than other protocols. MP-QGRD reduces delay by selecting reliable links using Q-learning. GPSR experiences the longest delay due to its perimeter forwarding mechanism. QMR, considering delay metrics, outperforms QGeo. MP-QGRD predicts node positions and selects the nearest node to the destination for forwarding, achieving low latency. Figure 3(c) shows the communication energy consumption of the four routing protocols under different node counts. MP-QGRD and QMR consume less energy than QGeo and GPSR. The former considers node energy consumption when designing the reward function, so it selects the next hop node with lower energy consumption for forwarding.