## 5 Simulation Results
Additionally, when the speed is relatively low, the deviation between the predicted and theoretical values is small. With increased speed, this deviation gradually increases. This is because, in the simulations, nodes are confined within a given space, and the higher the node speed, the more susceptible the link is to disruption. In addition, although a lower level of node activity results in greater accuracy in link estimation, the proposed method can fully reflect the dynamic characteristics of the network topology during rapid changes even when node activity is high. Figure 10 further introduces different interruption probabilities for comparison based on Figure 8. It can be seen in the figure that when communication radii are the same, the channel capacity corresponding to a high interruption probability is always higher than that of a low interruption probability. This indicates that allowing a higher interruption probability can enhance the network performance. This is because under the scenario of interruption probability, the constraint on link reliability can be relaxed in exchange for higher capacity. For the high interruption probability, the channel capacity reaches its peak when the communication radius is around 110, and then decreases; the peak of the interruption probability occurs when the communication radius is around 80. This is because when the interruption probability is higher, the system can accept more link interruptions and thus chooses a longer transmission distance (increased radius). There is a positive correlation between interruption probability and the optimal radius. Table 1 lists the optimal coverage radii under different combinations of interruption probability and path loss exponent. It can be observed from the table that when the path loss exponent increases from 2.5 to 4, the optimal radius corresponding to the same interruption probability decreases significantly. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\alpha\) = 2.5 & \(\alpha\) = 3 & \(\alpha\) = 3.5 & \(\alpha\) = 4 \\ \hline \(\varepsilon=10^{-4}\) & 160 & 133 & 79 & 78 \\ \(\varepsilon=10^{-3}\) & 199 & 160 & 113 & 101 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Coverage radii for given (\(\varepsilon\), \(\alpha\)) pairs. Figure 9: Comparison between predicted and theoretical values. In the following section, we present simulations illustrating performance variations observed with the Dynamic Source Routing (DSR) protocol, utilizing the routing metric described above alongside classic routing metrics. In this setup, the source node transmits data packets at a fixed packet rate. The following routing metrics are employed:  
* Link persistence: as discussed above. * Shortest path: the path with the minimum number of hops is chosen. * First found path: the path carried by the initial routing reply packet received by the source node is chosen. Table 2 compares the three routing metrics (link persistence, shortest path, and first found path) in terms of network performance. The performance metrics include packet loss ratio, end-to-end delay, and throughput. It can be seen that link persistence outperforms the other two metrics in packet loss ratio, end-to-end delay, and throughput. This indicates that link persistence can better balance link stability and efficiency during path selection, thereby enhancing overall network performance. However, it is crucial to note that the delay measurement only encompasses received packets, leading to shorter delays as the packet loss ratio reaches certain thresholds. This occurs because the routing protocol discards buffered packets that have surpassed a specific threshold. In instances where a path becomes unavailable, rerouting can significantly contribute to queuing delay, leading to the dropping of more packets and the experiencing of prolonged delays. In general, the reliability of a path tends to decrease as the number of hops or links it traverses increases.