# Q-Learning Empowered Highly Dynamic and Latency-Aware Routing Algorithm for Flying Ad-Hoc Networks
## 5 Simulation Results
### Simulation Result and Analysis
The results indicate that the protocol under consideration exhibits a more stable delay than PARRoT. The diagram reveals that the delay performance of this protocol is optimal, with the exception of OLSR. Readers should note that OLSR has a relatively  
Figure 11: Impact of the number of nodes on the packet delivery ratio. Figure 12: Comparison of QEHLR to several protocols in end-to-end delay. low packet transmission rate and throughput, whereas QEHLR demonstrates the best performance in terms of delay stability. Figure 14 presents a comparison of the throughput of several protocols, which reflects the transmission efficiency of the protocol. A higher value of this indicator implies that the node can transmit more data per unit of time. The results indicate that the protocol under consideration is comparable to PARRoT in terms of throughput, with a value of approximately 2.2 kbps. Furthermore, the throughput rate of this protocol is higher than that of other protocols. However, it is worth noting that PARRoT suffers from the issue of large computational complexity and a long runtime for the program to execute once. To evaluate the efficiency of the routing protocol in highly dynamic scenarios, this section examines its performance in scenarios involving node speed variation and multi-mobility models. The discount factor COH*Delay is calculated using node aggregation and delay, while COH*LET represents the discount factor calculated using node aggregation and link failure time. Additionally, TCD*Delay is the discount factor calculated using node topology change degree and delay, as discussed before. Figure 15 depicts a comparative analysis of the packet delivery rate under the Gauss Markov mobility model for the three routing factors (utilized to compute the discount factor in Q-value) mentioned in this paper. The protocols employed in both figures exhibit similar trends, with only variations in the discount factor settings in the routing mechanism. The routing factor that integrates the node topology change degree (TCD) demonstrates the best overall performance in  
Figure 14: Comparison of QEHLR to several protocols in terms of throughput. Figure 13: Comparison of QEHLR to several protocols in jitter delay. terms of packet delivery rate under this mobility model. The results indicate that the TCD-based protocol can effectively learn the network topology characteristics by considering the degree of node topology variation. The simulation scenario was conducted under two node counts, and the protocol exhibited superior performance when the node count was set to 15. Figure 15c,d present the average end-to-end delay comparison of nodes under the Gauss Markov mobility model, while Figure 15e,f show the jitter delay comparison. It is observed that both the end-to-end delay and jitter with TCD exhibit superior performance when the number of nodes is 15. However, the performance deteriorates when the number of nodes increases to 20, and the delay can be reduced by utilizing the calculation methods of node aggregation and link failure time. Based on the performance of the three routing coefficients, the calculation method with the node topology degree exhibits the best packet transmission performance for the Gauss Markov mobility model, and the delay performance is better when the number of nodes is 15. Figure 16a,b depict the comparison of packet delivery rates for three routing coefficients under a random waypoint mobility model. It can be observed that the protocol incorporating TCD for routing calculation gradually improves packet transmission rates, starting from a speed of 20 m/s for 15 nodes  
Figure 15: Contrastive illustration under the Gauss Markov model. in this mobility model. However, for 20 nodes, the best packet transmission performance among the three is achieved at a speed of 25 m/s. In terms of delay and jitter metrics, speeds of 15 m/s, 20 m/s, and 35 m/s can ensure and maintain low delays for 15 nodes. For 20 nodes, speeds of 10 m/s, 15 m/s, 25 m/s, 35 m/s, and 40 m/s can ensure and maintain low delays.