## 5 Simulation Results and Performance Analysis
### Comparative Analysis of Routing Performance with Different Buffers  
The routing overhead of the three algorithms with different buffers is shown in Figure 4. As the buffer increases, each node may store more packets and participate in more transmission and forwarding processes, so the routing overhead of all three algorithms gradually increases. With different buffers, SprayAndWait has a large number of message copies in the network, which generates a large routing overhead and consumes a lot of network resources. RSNA decreases the routing overhead compared to SprayAndWait, but there is still some transmission of useless information. The routing strategy proposed in this paper reduces the transmission of useless messages and greatly reduces the routing cost, which is less than SprayAndWait and RSNA under different buffers.  
The average transmission delay under different buffers of the three algorithms is shown in Figure 5. In SprayAndWait, the node carrying the message needs to wait for the relay forwarding from the auxiliary node, which increases the message transmission delay. The routing strategy in this paper determines the forwarding priority of all the candidate neighbor nodes more reasonably than RSNA, which effectively reduces the transmission delay, and the average transmission delay under different buffers is less than both SprayAndWait and RSNA.  
The packet delivery rates of the three algorithms under different buffers are shown in Figure 6. SprayAndWait does not take into account the prediction and motion posture of relay nodes, and the packet delivery rate is relatively low in general. The further prediction of relay nodes by RSNA improves the packet delivery rate to a certain extent compared to SprayAndWait. The routing strategy proposed in this paper improves the further prediction conditions for relay nodes based on RSNA, effectively reduces the number of message replicas, and makes the packet delivery rate higher than both SprayAndWait and RSNA under different buffers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values** & **Description** \\ \hline Node Deployment Area & 100 m \(\times\) 100 m & Square monitoring area \\ Transmission Speed & 200 kbps & The speed at which a node \\  & & transmits a message \\ Message Interval & 10 s & Packet delivery cycle \\ Maximum Message Survival & 1000 s & Packet survival time limit to \\ Time & & prevent packets from looping \\  & & indefinitely in the network \\ Nodal Velocity & 0\(\sim\)20 km/h & Random speed range \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 4: Comparison of routing overhead with different buffers: (**a**) SprayAndWait; (**b**) RSNA; (**c**) Deep learning-based routing for assessing link connectivity.  
Figure 5: Comparison of average delay with different buffers: (**a**) SprayAndWait; (**b**) RSNA; (**c**) Deep learning-based routing for assessing link connectivity.  
From the above experimental results, it can be concluded that the routing strategy based on deep learning to evaluate link connectivity proposed in this paper improves the network performance under different buffer sizes, effectively increases the message delivery rate, and reduces the resource consumption and network overhead compared to SprayAndWait and RSNA. Thus, it is an ideal choice for transmitting data in the dynamically changing maritime opportunistic network.