# Hybrid Dual-Link Data Transmission Based on Internet of Vessels
## 4 Simulation Experiments  
In this section of experiments, this study utilizes real ship data to evaluate the performance of the algorithm. The experimental platform is configured with Intel(R) Core(TM) i7-8750H CPU @ 2.1 GHz, 16.0 GB of RAM, and 64-bit Windows 11 as the operating system. The programming language is Python 3.10.7, and the development tool is PyCharm 3.10.7, while tools such as Matlab R2022b are used for data plotting and analysis. In this study, real-time ship data provided by ShipShun ([[http://www.ships66.com](http://www.ships66.com)]([http://www.ships66.com](http://www.ships66.com))) are used. Specifically, an area within the range of east longitude 38.461963 to 38.810483 and north latitude 119.066613 to 121.986084 at a certain moment on 8 January 2025 (shown in Figure 7) is selected, and the green points represent the real-time coordinate points of the ships. In this area, 738 ships are selected as real data for the experiment, with the assumption that each ship is equipped with a dual-head node, where each dual-head node is equipped with both above-water and underwater communication modules. In addition, the positions of 100 underwater sensor nodes are randomly generated within the range of east longitude 38.461963 to 38.810483, north latitude 119.066613 to 121.986084, and depth 0 to 300 m. The three-dimensional spatial distribution and transmission paths of the sensor nodes relative to the ship's position are shown in Figure 8.  
In order to simulate the real situation of real ship workers, ship workers are divided into two types: type a and type b. Type a ship workers have a high degree of trustworthiness, and their probability of successfully completing the task is set to be 98% in the experiment, which means that there is only a 2% probability of not being able to complete the task properly. Type b ship workers, on the other hand, belong to the selfish type, and their probability of not being able to complete the task is set to 90% in the experiment, and there is only a 10% probability of being able to complete the task. After sub-simulating the HDTA algorithm for 1000 times, it is found that an initial reputation value could be attached to each ship. As shown in Figure 9a, different percentages of selfish ship workers of 10%, 20%, 30% and 40% are set, and the success rate of task completion at different distances is analyzed. Figure 7: Ship real-time position map. The green dots represent the vessels, and the yellow area represents the experimental area. The results indicate that, within the same time frame, the task transmission success rate for the experimental group with a higher proportion of selfish ship workers is significantly lower than that for the experimental group with a lower proportion. Additionally, as the transmission distance increases, the task transmission success rates for experimental groups with different proportions of selfish ship workers all show a marked decline. This is because, as the transmission distance increases, the number of relay nodes encountered also increases, which in turn raises the likelihood of encountering selfish ship workers along the transmission path, ultimately leading to a decrease in the overall task transmission success rate. Figure 8: The 3D link transmission path. Figure 9: (**a**) Task success rate of the Greedy Forwarding algorithm versus transmission distance. (**b**) Task success rate of the HDTA algorithm versus the transmission distance. In order to investigate the impact of vessel density on data transmission speed, transmission reliability, and overall system performance, we simulate various vessel density scenarios. We analyze the effect of vessel density on the time overhead of the HDTA algorithm under conditions of the same maritime area, the same underwater node density, and the same proportion of selfish vessel nodes, and compare the results with those of the Flooding algorithm.