# Research on Networking Protocols for Large-Scale Mobile Ultraviolet Communication Networks
## 4 Simulations
### Node Speed and \(T_{\text{rrf}}\)  
LSM-UVCN relies on the established connection matrix. As the network topology evolves with node mobility, transmission paths established based on outdated connection matrices are highly susceptible to issues such as path disruption or mutual interference  
Figure 9: Throughput versus node density \(\rho\).  
between paths, thus leading to transmission failures. In the random movement model, a larger velocity \(v\) implies more frequent changes in network topology, making the connection matrix more prone to obsolescence. The proposed protocol mitigates the adverse effects of connection matrix obsolescence to a certain extent through periodic updates of the connection matrix. This section simulates the impact of \(v\) and \(T_{\text{ref}}\) on network performance, where the node density \(\rho\) is configured as 0.00012 and other conditions remain unchanged.  
Figure 10 illustrates the impact of adjusting \(T_{\text{ref}}\) on the data loss rate under different \(v\). When \(v\) = 5 mps, reducing \(T_{\text{ref}}\) from \(30T_{\text{trans}}\) to \(10T_{\text{trans}}\) decreases the data loss rate by approximately 0.017. In contrast, when \(v\) = 15 mps, the same adjustment to \(T_{\text{ref}}\) results in a reduction of approximately 0.029. This discrepancy arises because, at lower \(v\), the intensity of network topology changes is relatively low; consequently, increasing \(T_{\text{ref}}\) has a diminished effect on the data loss rate.  
In Figure 10, the data loss rate exhibits a characteristic trend: it initially decreases rapidly, followed by a gradual stabilization through oscillatory behavior. Specifically, in Figure 10b, the data loss rate enters a stable oscillatory state after approximately 1000 s of simulation time. Conversely, in Figure 10a, the data loss rate declines more slowly and does not stabilize until around 3000 s. This divergence is attributed to the network compactness, which influences network performance 20. When \(\rho\) is fixed, network compactness fluctuates within a certain range due to random node mobility, thereby causing temporal variations in the data loss rate. Notably, the simulation settings specify constant network density and initial topology, resulting in a relatively stable initial data loss rate. Notably, the simulation parameters include fixed network density and initial topology, which anchor the initial data loss rate at a relatively stable baseline. Higher \(v\) accelerates topological changes, enabling the network to reach oscillatory stability more rapidly.  
Figure 11 illustrates the impact of adjusting \(v\) on the data loss rate under different values of \(T_{\text{ref}}\). When \(T_{\text{ref}}\) is small, the network can update the connection matrix in a timely manner, significantly reducing data transmission failures caused by outdated connection matrices. As shown in Figure 11a, setting a lower \(T_{\text{ref}}\) mitigates performance degradation arising from intensified network topology changes at higher \(v\). Conversely, when \(T_{\text{ref}}\) is large, the influence of network topology change intensity on the data loss rate becomes more pronounced, as demonstrated in Figure 11b. For example, when \(T_{\text{ref}}\) = 30\(T_{\text{trans}}\), the data loss rate at \(v\) = 15 mps is approximately 0.013 higher than that at \(v\) = 5 mps.  
Figure 10: Data loss rate versus different \(v\): (a) \(v\) = 5 mps; (b) \(v\) = 15 mps.  
Therefore, adjusting the magnitude of \(T_{\text{ref}}\) according to different network mobility states can optimize network performance.