# MMNET: A Multi-Modal Network Architecture for Underwater Networking
## 5 Simulation  
In order to evaluate the performance of our proposed MMNET, we simulated the multi-mode routing and multi-mode communication of MMNET respectively. In order to evaluate MMNET's multi-mode communication capability, we considered a classic AUV-assisted underwater data acquisition scenario. Both AUV node and collection node were equipped with acoustic and optical communication equipment. The effective distance of optical communication equipment was 100 m, the packet rate conformed to the Poisson distribution of \(\lambda=40\) packets per second, and the effective communication distance of acoustic communication equipment was 1000 m. According to the Poisson distribution of \(\lambda=0.4\) packets per second, the AUV collected the underwater environment information from the origin with the graph path, as shown in Figure 18. The yellow path represented the optical communication range, and the blue path represented the acoustic communication range. The simulation was carried out for 310 s.  
Figure 17: (**a**) Data collection application emergency network resource request; (**b**) detailed information of interaction between terminals of the underwater data collection network emergency task.  
Figure 18: Available bandwidth of AUV trajectory and corresponding position.  
Figure 19a showed the average number of packets per 10 s of acoustic and MMNET multi-mode communication systems. Since acoustic communication cannot communicate within 100 m, it was not compared with MMNET. It could be seen from Figure 19a that, in the optical communication range, MMNET had a transmission rate much higher than that of acoustic communication. Even if it exceeded the optical communication range, MMNET could still communicate at the same rate as acoustic communication. It could be seen that the performance of MMNET communication was better than that of traditional single mode communication.  
In order to evaluate the multi-mode routing capability of MMNET, we considered a classic underwater data acquisition scenario. Multiple acquisition nodes were deployed around a sink node and acoustic communication was used between nodes. The transmission rate of network packets conformed to the Poisson distribution of \(\lambda\) = 0.4 packets per second. Each node used MMNET to deploy address-based routing and NDN-based routing. The simulation time was 600 s. Assuming that the application had special interest in 5% of the data packets, it needed to be recollected.  
Figure 20a-c showed the ratio of the actual received packets to the expected packets when there were three collection nodes, six collection nodes and nine collection nodes, respectively. Because NDN-based packets needed to send interest packets first, they occupy more network resources and were less efficient than MMNET and address-based routing. However, when the application was interested in some packets, the method based on NDN would reduce the sending of interest packets due to its content aggregation function, while the address-based method needed to send interest packets many times. MMNET used two methods at the same time, which was more  
routing method. It could be seen from the figure that as the number of nodes increases, the delay of address-based routing increased gradually when repeated collection was needed due to the lack of aggregation function. MMNET could be used to provide a variety of application services.