# Research on Networking Protocols for Large-Scale Mobile Ultraviolet Communication Networks
## 4 Simulations
### Node Density  
When node density \(\rho\) is low, the number of relays required for communication between nodes increases, and the network is more likely to be partitioned into multiple disjoint subnets as nodes move. When node density is high, connections between nodes become tighter. In this case, each established transmission path occupies more nodes, leading to a reduction in the number of simultaneously establishable transmission paths. Both scenarios may have adverse effects on network performance. This section simulates the impact of \(\rho\) on network performance. \(v\) is set to 10 mps, the arrival rate \(\lambda\) is 0.7, the number of nodes is 100, the link rate remains at 10 Mbps, the bit error rate is \(10^{-6}\), the communication range is 100 m, and \(T_{\text{ref}}\) is approximately equal to \(10T_{\text{trans}}\). In LSM-UVCN, each node in the network follows the equal probability principle to randomly select any node as the destination node for data transmission. If the source node and the destination node belong to non-connected subnets, the source node will directly discard the data. As shown in Figure 7, when \(\rho\) is low, the probability that data is directly discarded upon generation is relatively high, and this probability decreases significantly as \(\rho\) increases. Compared with the case when \(\rho=0.00006\), the probability of direct data discarding decreases by approximately 0.62 when \(\rho=0.00026\). Additionally, during data transmission, the probability of transmission failure caused by path breakage or interference between paths shows a trend of first rising rapidly and then gradually decreasing with the increase of \(\rho\). When \(\rho=0.00014\), the data loss rate increases by approximately 0.032 compared with that when \(\rho=0.00006\). Figure 6: Throughput versus number of nodes and BER. As \(\rho\) decreases continuously, the network is highly likely to split into multiple independent and non-interfering subnets. In this situation, the probability that nodes directly discard the generated data increases significantly. As \(\rho\) gradually increases, most nodes in the network are located in the same subnet, and the probability of direct data discarding drops sharply. However, during data transmission, the probability of transmission failure due to path breakage or interference between paths exhibits a non-linear trend of first increasing and then decreasing with the change of \(\rho\). When \(\rho\) is low, the network is highly likely to split into multiple independent subnets. The distance between nodes and the number of nodes within each subnet are relatively limited, resulting in transmission paths mainly composed of short-distance links. According to the theoretical derivations of Equations (3) and (6), in a network environment dominated by short links, the probability of successful data transmission is relatively high. Figure 8 simulates the number of transmission paths of different lengths established per second on average in the network under different values of \(\rho\). It can be observed that as \(\rho\) gradually increases, the number of transmission paths generated per second in the network increases significantly. However, as \(\rho\) continues to rise, the generation rate of transmission paths begins to decline. Figure 8 also reveals that the rate of generating long-distance transmission paths first increases and then decreases with the growth of \(\rho\). When \(\rho=0.00006\), the rate of generating transmission paths with more than five hops is approximately 22% of the rate of generating paths within one hop. When \(\rho\) increases to 0.00012, this rate rises to 45%. Figure 8: Transmission path generation rate versus node density \(\rho\). Figure 7: Data loss rate versus node density \(\rho\). As \(\rho\) increases, more nodes become interconnected, forming larger subnets.