## 4 Results and Discussion  
In this section, we evaluate the performance of the proposed opportunistic routing algorithm. Simulations are implemented on an Intel Core i7-1065G7 1.30 GHz PC with 8G memory and performed in Matlab R2016b. After the shipw occurs, 100 marine nodes are supposed to be randomly distributed in the MSR area with a side length of 2 km. The maximum and minimum speed of the sensor node are 20 m/s and 10 m/s, respectively. Other simulation parameters settings are shown in Table 2. Figure 4 is the deployment diagram of marine nodes. To properly verify the proposed routing algorithm, a marine node that is far from the gateway node is selected as the sink. Three routing algorithms including prediction based opportunistic routing (POR) [4], E-Ant-DSR [29], DORAHP [30] are chosen as the benchmark algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(e_{0}\) & 3 J & \(\varepsilon_{mp}\) & 0.0013 pJ/bit/m4 \\ \(r\) & 100 m & \(\alpha\) & 3 \\ Simulation time & 70 s & \(\sigma^{2}\) & 30 dB \\ Channel bandwidth & 2 Mbps & \(\lambda^{-1}\) & 1 s \\ \(E_{elcc}\) & 50 nJ/bit & \(\gamma_{\Phi}\) & 2 \\ \(\varepsilon_{fs}\) & 10 pJ/bit/m\({}^{2}\) & \(\theta\) & 30 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 4: Marine nodes deployed in the MSR sea area. Figure 5a reveals that our proposed algorithm has a higher packet delivery ratio than that of the benchmark algorithms. The outperformance could be explained by the facts that (1) the proposed algorithm effectively predicts the link connectivity based on the consideration of node mobility, and (2) the proposed methods could periodically update the measurement value of link connectivity, which can, to some extent, delete the link with a smaller metric value and ensure the reliability of the transmission path. Meanwhile, our opportunistic routing algorithm forms multiple communication links, which effectively improves the probability of successfully forwarding marine data packets to the sink. Compared with POR, E-Ant-DSR, and DORAHP, the packet delivery ratio of the proposed opportunistic routing protocol is improved by 21.4%, 23.9% and 42.9%, respectively. Figure 5b shows that the proposed algorithm achieves the lowest end-to-end delay (average time required by the data packets to reach the sink). The proposed algorithm reduces the end-to-end delay by using the opportunistic routing technology incorporated with the optimal packet advancement prediction. Compared with POR, E-Ant-DSR, and DORAHP, the delay performance of the proposed opportunistic routing protocol is improved by 39.2%, 41.9% and 55.5%, respectively, which rises the efficiency of MSR to a certain extent. It can be seen from Figure 6, as the increase of the node moving speed, the performance of all algorithms has declined to varying degrees. In DORAHP, due to ignorance of the node mobility and the high computational complexity, the communication link that becomes formed is not stable. New routing path must be formed frequently that will greatly reduce the packet delivery ratio. However, in our proposed algorithm, nodes broadcast the data and attempt to find a better forwarding node at each time slot, which increases the probability of being successfully and timely forwarded for the data. Since our proposed algorithm has a relatively low computational complexity, it has achieved the best results in terms of the energy consumption. To be specific, when the average node speed is 20 m/s, compared with POR, E-Ant-DSR, and DORAHP, the average node energy consumption of the proposed opportunistic routing protocol is decreased by 19.6%, 21.8% and 18.1%, respectively. Figure 7a reflects the packet delivery ratios of the proposed algorithm, POR, and E-Ant-DSR gradually increase to a stable state over the rise in the node's communication radius.