# Hybrid Dual-Link Data Transmission Based on Internet of Vessels
## 4 Simulation Experiments
The Flooding algorithm is a widely used network data transmission method that broadcasts data at each node in the absence of explicit routing information. Vessel density \(p\) represents the amount of vessels per square kilometer. We examine the time overhead for different vessel densities \(p=1\), \(p=2\), \(p=3\), and compare it with that of the Flooding algorithm as shown in Figure 10. The analysis shows that the ship density has a significant impact on the performance of the HDTA algorithm. When \(p=0\), it indicates that there are no ships in the surrounding area, and the HDTA algorithm relies entirely on the underwater network and underwater sensor nodes for multi-hop transmission. In this case, the algorithm performs at its lowest, with the highest time overhead. When \(p=0.2\), it represents a low-density ship area. The performance of the HDTA algorithm becomes unstable, as there may be no suitable ships available as relay nodes, and data can only be transmitted to nearby underwater sensor nodes or wait for a suitable ship. When the ship density reaches or exceeds \(p=1\), it is considered a high-density ship area, and the HDTA algorithm performs at its best. Figure 11 shows a comparison of the task transmission success rate of the algorithm with the addition of the reputation selection algorithm versus the algorithm without the addition of the reputation selection algorithm at different distances with 10% selfish ship workers. The results show that the task transmission success rate of the algorithm without adding reputation matching algorithm keeps decreasing as the distance increases, while the HDTA algorithm with the addition of the reputation selection algorithm always maintains a high and stable task transmission success rate. Figure 10: The impact of different vessel densities on the performance of the HDTA algorithm. Figure 12 illustrates the results of different algorithms in terms of packet transmission success rate. We compare advanced algorithms such as DC-K-means [25] for clustering algorithms to optimize multi-hop data transmission in underwater acoustic sensor networks, data collection based on a hierarchical approach for optimizing data transmission and energy management in underwater acoustic sensor networks. SDCS [26], energy-optimized clustering (EOCA) for multi-hop underwater acoustic cooperative sensor networks [39], selective dynamic coded cooperative communication (S-DCC) for multi-hop underwater acoustic sensor network [27], and other advanced algorithms, respectively, are subjected to 1000 simulations, and the probabilities of each algorithm successfully completing the packet transmission are 78.5%, 81.3%, 88.6%, and 77.6%, respectively, whereas the HDTA algorithm achieves a success rate of 95.3%. Based on the 95% confidence interval analysis, the success rates of the other algorithms are [77.1%, 79.9%], [80.0%, 82.6%], [87.4%, 89.8%], and [76.2%, 79.0%], while the success rate confidence interval for the HDTA algorithm is [94.5%, 96.1%]. Traditional multi-hop data transmission has a relatively low success rate due to factors such as channel quality, propagation conditions (e.g., signal attenuation and multipath effects), node energy, and state. In contrast, the HDTA algorithm leverages vessels as multi-hop relay nodes, which significantly improves performance. As the underwater part of the transmission is shifted to above-water, where vessel energy consumption is negligible, HDTA avoids many of the inevitable drawbacks of traditional multi-hop algorithms. Furthermore, the HDTA algorithm filters out selfish vessels and focuses on the recent behavior of vessel nodes. When a vessel's behavior deviates from normal, its reputation is rapidly reduced, ensuring stable link transmission. These results indicate that the HDTA algorithm proposed in this paper outperforms other advanced algorithms in terms of packet delivery rate.