## 5 Performance Evaluation
### Numerical Results
Additionally, RPGM also captures the correlation of movement between nodes in a group, while in the RWP model each node moves independently, leading to more frequent changes in network topology and causing more cluster head changes. Therefore, in the proposed clustering protocol in a scenario where the number of nodes (30, 50, 100) and their speed increases, the average number of cluster head changes increases slightly when the network topology changes rapidly. Nevertheless, the number of cluster head changes in each session is less than one. This means the clustering protocol has a very high level of stability. Figure 8: Convergence of Algorithm 1 for maximize cost problem as a function of the number of iterations. In Figure 10, we analyze the impact of the number of hidden neurons on the DNN model with the different numbers of hidden layers. As we can see in Figure 10, the RMSE with one hidden layer will decrease from 0.3164 to 0.0138, with two hidden layers will decrease from 0.0583 to 0.006, and with five hidden layers will decrease from 0.036 to 0.000124 when the number of hidden neurons increases from 5 to 250. It can be explained that the DNN model with more neurons performs better than the DNN model with fewer neurons. Furthermore, the more hidden layer of the DNN model performs better than the less hidden layer. Figure 11 illustrates the PDR versus the variation in node speed. As can be observed in Figure 11, when the speed of the node increases, the PDR is decreased. The reason is that the entire network becomes more unstable as node speed increases. This instability leads to more frequent disruptions of the multicast tree structure and potential packet losses, decreasing the PDR. Again, when comparing the two mobility models, it is evident that the RPGM protocol results in superior performance compared to the RWP model, which exhibits a high PDR. However, it should be noted that the reduction in PDR is significantly less in the case of the DLSMR+TD-PSO protocol compared to other schemes. Therefore, the DLSMR+TD-PSO protocol with the RPGM model shows the highest level of reliability in maintaining a high PDR. Figure 10: Impact of the number of hidden neurons on the DNN model with different numbers of hidden layers. Figure 9: Average number of cluster heads changing as a function of node speed. Figure 12 reveals the impact of node speed on routing delays, including the latency time for cluster construction per session as a function of node speed. As can be observed in Figure 12, the routing delay increases when the node speed increases. The reason is that nodes will move more dynamically when the node's speed increases, consequently increasing the time to establish the route. Once again, the protocols under the RPGM model demonstrate higher stability than protocols under the RWP models because the coordinated group movement in RPGM reduces sudden changes in network topology, thereby reducing route establishment delays. On the other hand, the proposed DLSMR+TD-PSO protocol only involves UAVs as CHs and GW nodes to determine the route to be followed by packets. Thus, the proposed DLSMR+TD-PSO protocol with the RPGM model can send packets with a minimum delay compared to other schemes. Figure 13 presents a comparison of the control overhead as a function of node speed, including the control overhead for cluster formation per node per session. As can be seen, the control overhead increases a little bit when the speed of the node increases. Essentially, this is due to the fact that, when the node's speed increases, more packets will be needed for route establishment and thus the overhead will rise. Again, comparing the different mobility models, the RPGM model demonstrates better control overhead due to high stability than the RWP model. In addition to that, the proposed TD-PSO clustering protocol can reduce the control overhead in all schemes.