## 7 Simulation Results
Figure 6 illustrates the trend of survivability of the system as a function of the velocity of UAV nodes, and highlights the correlation between state probabilities and mobility and energy consumption parameters. As shown in Figure 6a, the system available probability decreases along with the increase in velocity in both the DCWA-DCH algorithm and the single cluster head algorithm. The increase in velocity caused the corresponding energy consumption enhancement of the UAV, which further caused the increase in \(\lambda\). By comparison, the system available probability in DCWA-DCH algorithm outperforms that of the single cluster head algorithm, which demonstrates that the introduction of bCH enhances the resilience of a UAV network against the instability caused by mobility and energy consumption. Figure 4: The distribution and clustering results of the UAVs. The nodes in the same cluster are shown in the same color. The label takes one of the clusters as example. Moreover, along with the increase in \(\lambda\), the probability of UAV at S state increases. Hence, the system weak probability also increases along with the increase in velocity, which is shown in Figure 5(b). Unlike the single cluster head algorithm, the system will transfer into the weak state only when both the pCH and the bCH are in S state or one is in S state and the other is in F state. Consequently, the system weak probability in DCWA-DCH is  
Figure 5: The probability of each state of the system with the increase in the number of UAVs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**UAV Number** & **DWCA-DCH** & **Single Cluster Head** & **Improvement** \\ \hline
20 & 0.8137 & 0.6797 & 19.7\% \\ \hline
50 & 0.8761 & 0.6751 & 29.8\% \\ \hline
80 & 0.8993 & 0.6728 & 33.7\% \\ \hline
110 & 0.9069 & 0.6702 & 35.3\% \\ \hline
140 & 0.9106 & 0.67 & 35.9\% \\ \hline
170 & 0.9107 & 0.670 & 36.0\% \\ \hline
200 & 0.9132 & 0.665 & 37.3\% \\ \hline
300 & 0.9129 & 0.6614 & 38.0\% \\ \hline
400 & 0.9109 & 0.6604 & 37.9\% \\ \hline
500 & 0.9106 & 0.6578 & 38.4\% \\ \hline
600 & 0.911 & 0.6596 & 38.1\% \\ \hline
700 & 0.9062 & 0.6555 & 38.3\% \\ \hline
800 & 0.903 & 0.6533 & 38.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of system available probability between DWCA-DCH and single cluster head algorithm. Figure 6: The probability of each state of the system with the increase in the velocity of UAVs. lower than that in the single cluster head algorithm, which demonstrates the robustness of the proposed DCWA-DCH. As system weak probability increases, system unavailable probability also increases under the same \(\mu\), which is shown in Figure 6c. The system unavailable probability in DCWA-DCH is also lower since the system will transfer into the unavailable state only when both the pCH and the bCH are in F state. The variation trend of system available probability with different parameters is shown in Figure 7. As shown in Figure 7a-c, no matter how other parameters change, the DWCA-DCH algorithm outperforms the single cluster head algorithm in terms of the system available probability. As shown in Figure 7a, the system available probability of the single cluster head algorithm and the dual cluster head algorithm increases as \(\gamma\) increases (i.e., the probability of the UAV transferring from the F state to the C state increases). The system available probability of the dual cluster head algorithm is greater than that of the single cluster head algorithm. It shows that the dual cluster head algorithm increases the available probability of the system. When both the pCH and the bCH have low energy, the bCH will enter the F state as soon as possible and fly to the charging pile, so as to restore the system to available as soon as possible. Therefore, the impact of \(\mu_{2}\) is considered on the system survivability, i.e., the influence of the probability change of the bCH from the S state to the F state on the system survival probability.