## 7 Simulation Results  
In this section, we analyze the proposed algorithm and verify its performance according to the proposed survivability analysis method. We assume that the UAV nodes are randomly distributed in a square of 1 km*1 km, with a constant flying height of 50 m, while the gBS is located at the center of the area. The maximum velocity is 15 m/s. The flight path of the UAV is a circle centered on the gBS, and the \(\eta_{Los}\) and \(\eta_{NLoS}\) are 1.6 dB and 23 dB, respectively. The carrier frequency of the UAV is 2 GHz [31]. In this paper, the selection criterion for the pCH is higher service efficiency, and the selection criterion for the bCH is higher stability, therefore the values of \(w_{1}^{p}\), \(w_{2}^{p}\), \(w_{3}^{p}\), \(w_{4}^{p}\), \(w_{5}^{p}\) are set as 0.4, 0.2, 0.2, 0.1, 0.1, and the values of \(w_{1}^{b}\), \(w_{2}^{b}\), \(w_{3}^{b}\), \(w_{4}^{b}\), and \(w_{5}^{b}\) are 0.1, 0.1, 0.1, 0.5, and 0.2, respectively. In other scenarios, the value of different weight coefficients can be adjusted according to actual network requirements. The rest of the parameters not mentioned are listed in Table 2.  
Figure 4 shows the distribution and clustering results of the UAVs. The pCH nodes of the UAVs are represented by rhombuses, the bCH nodes are represented by hollow circles, and the nodes in the same cluster are shown in the same color. \begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Value** \\ \hline The air fluid density \(\rho/\text{kg}/\text{m}^{3}\) & 1.2 \\ UAV drag coefficient \(C_{\text{D}_{0}}\) & 0.025 \\ Reference area of the UAV \(\text{S}/\text{m}^{3}\) & 0.192 \\ UAV rotor number \(M\) & 4 \\ UAV blade chord \(c_{b}/\text{m}\) & 0.022 \\ The radius of the rotor spinning disk \(\beta/\text{m}\) & 0.25 \\ The frame weight of UAV \(W/\text{kg}\) & 2 \\ The battery and payload weight of UAV \(w/\text{kg}\) & 2 \\ The gravity \(g/\text{m}/\text{s}^{2}\) & 9.8 \\ Ideal cluster size \(\delta\) & 5 \\ The scale parameter \(\Lambda\) & 0.5 \\ The shape parameter \(k\) & 1 \\ Signal receiving distance of UAV \(range/\text{m}\) & 300 \\ \hline \end{tabular}
\end{table} 
Table 2: Parameter settings. In Figures 5-9, the proposed DWCA-DCH algorithm is compared with the traditional single cluster head WCA clustering algorithm [32]. Figure 5 shows the change of the probability of each state of the system with the increase of the number of UAV nodes. As can be seen from the simulation results, when the number of UAV nodes increases gradually, the system available probability in the DWCA-DCH algorithm increases significantly at first, reaches the maximum when the number of nodes is 200, and then presents an overall stable and slightly decreasing trend. When the number of nodes in the UAV network is low, the robustness of the network is poor. As shown in Figure 5, the performance of the p/bCHs selected by the DWCA-DCH algorithm proposed in this paper is better, which can effectively improve the network survivability performance. When the number of nodes in the network gradually increases to 200, the clustering basically reaches stability, and the system availability probability also reaches the peak. After that, as the number of nodes increases, the network gradually expands, and the system efficiency decreases slightly, but it is basically stable. As a result, optimal performance can be achieved when 200 UAVs are deployed. Therefore, in the following simulation, the number of nodes is selected as 50, 100, and 200 for comparison. In the compared single cluster head algorithm, the available probability of the system decreases as the number of UAVs increases. In the DCWA-DCH algorithm, the performance keeps increasing until the number of nodes reaches 200. As shown in Table 3, the system availability probability in DWCA-DCH is about 35% higher than that in the single cluster head algorithm in average. Therefore, the proposed DCWA-DCH algorithm can effectively improve the system performance and is more suitable for the deployment of a large number of nodes.