## 7 Simulation Results
As shown in Figure 7b, the system available probability \(P_{av}\) increases along with the growth of \(\mu_{2}\), which is because the increase of \(\mu_{2}\) will prompt the bCH to transfer from the S state to the F state faster, and then recover to available faster. This indicates that increasing the transition rate of the bCH from the S state to the F state can make the dual cluster head system recover to available faster. In comparison, there is no \(\mu_{2}\) in the single cluster head algorithm, thus the blue line barely changes. \(\xi\) represents the transition probability of the UAV from the C state to the F state, whose value is relatively low. Therefore, the variation range is set as 0.01-0.09 during simulation. In Figure 7c, it shows that the available probability of both the dual cluster head algorithm and the single cluster head algorithm decreases as \(\xi\) increases, but the available probability of the dual cluster head algorithm is greater than that of the single cluster head algorithm, which fully indicates that the introduction of the dual cluster head algorithm increases the availability of the system. Figure 8a-c shows the variation trend of system weak probability with different parameters. The system weak probability of the dual cluster head algorithm is lower than that of the single cluster head algorithm. For DWCA-DCH, \(P_{avk}\) decreases as \(\gamma\) increases, showing that increasing the charging and maintaining rate will increase the rate at which the system exits the weak state (this is because the bCH will enter the F state as soon as possible when both the pCH and the bCH have low energy, and increasing \(\gamma\) will increase the rate that the bCH returns to C state from F state), thus improving performance. As for the single cluster head algorithm, increasing \(\gamma\) can make the nodes in S state return to the  
Figure 7: The variation trend of system available probability with different parameters. C state faster, which indirectly increases the probability of the node transferring to the S state. For the dual cluster head algorithm, \(P_{mk}\) decreases as \(\mu_{2}\) increases, indicating that increasing the transition rate of bCH from the S state to the F state can make the dual cluster head system recover to available faster. Since the single cluster head algorithm has no bCH, changing \(\mu_{2}\) has no effect on \(P_{mk}\). In the dual cluster head algorithm, as \(\xi\) increases, \(P_{mk}\) increases, because it increases the probability that the system will move from SC to SF. For the single cluster head algorithm, increasing the probability that the node will move from C to F indirectly reduces the probability of node entering the S state, therefore with the increase in \(\xi\), \(P_{mk}\) slightly decreases. Figure 9a-c shows the variation trend of system unavailable probability with different parameters. It can be seen that the unavailable probability of the dual cluster head algorithm is much lower than that of the single cluster head algorithm. With the increase in the number of UAV nodes, \(P_{unav}\) gradually decreases in DWCA-DCH. \(P_{unav}\) decreases as \(\gamma\) increases. When \(\mu_{2}\) is increased, \(P_{unav}\) basically keeps steady. When \(\xi\) increases, \(P_{unav}\) increases significantly, while the curve of the dual cluster head is flatter, indicating that the proposed DWCA-DCH has better performance in improving system stability. Therefore, in the actual deployment, in order to reduce the probability of system unavailable, the fault maintaining and charging rate should be increased, and reasonable measures should be taken to reduce the fault rate of UAV. Figure 10 shows the trend of system available probability of the proposed DWCA-DCH over time, and compares it with MLSC algorithm [14] and the single cluster head algorithm. It can be seen that as time goes on, in the proposed DWCA-DCH, the available probability of the system gradually declines and then rebounds, maintaining a stable trend on the whole.