# Hybrid Dual-Link Data Transmission Based on Internet of Vessels
## 2 Related Work
The number of vessels in the ocean has reached a certain scale. As relay nodes, vessels offer a larger communication coverage, stable energy supply, and high bandwidth support compared to AUVs, USVs, and buoys, making them suitable for long-distance, high-speed data transmission. Their strong maneuverability and flexibility enable them to adapt to dynamic environments, whereas AUVs and USVs are limited by underwater or surface constraints, and buoys are restricted by energy supply and fixed positions, resulting in a smaller communication range. Therefore, this research proposes using passing vessels as relay nodes in the multi-hop transmission method, employing a spatial crowdsourcing allocation algorithm to ensure the stability and rate of data transmission links. The concept of crowdsourcing was first introduced by [PERSON] and [PERSON] in 2006 [28]. With the development of mobile internet and the sharing economy, traditional crowdsourcing has shifted towards spatial crowdsourcing [29]. Task allocation is considered the most crucial issue in spatial crowdsourcing [30], and spatial crowdsourcing task allocation algorithms have been applied in various fields. Task allocation can be classified into two dimensions based on the arrival scenario: static matching and dynamic matching. Static matching involves the one-time matching of known task and worker information, whereas dynamic matching deals with real-time changing tasks and worker information, requiring real-time scheduling and response. [PERSON] et al. [31] proposed a time-prediction-based task allocation framework based on historical data, and introduced a heuristic algorithm for the one-time assignment of spatial crowdsourcing tasks to appropriate workers. Dynamic matching addresses real-time variations, with algorithms designed for real-time adjustment. [PERSON] et al. [32] addressed the problem of online non-rejection-aware task allocation in spatial crowdsourcing, and proposed a non \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**References** & \begin{tabular}{c} **Deep-Sea** \\ **Advantages** \\ \end{tabular} & \begin{tabular}{c} **Transmission** \\ **Bandwidth** \\ \end{tabular} & **Stability** & **Deployment Cost** & \
\begin{tabular}{c} **Energy** \\ **Consumption** \\ \end{tabular} &
\begin{tabular}{c} **Long-Distance** \\ **Transmission** \\ \end{tabular} \\ \hline
[5,6] & & ✓ & ✓ & & & & \\ \hline
[8,9] & & ✓ & & ✓ & & & \\ \hline
[10] & & ✓ & & & ✓ & & \\ \hline
[25] & ✓ & & ✓ & ✓ & & & \\ \hline
[26] & ✓ & & & & & & \\ \hline
[27] & & ✓ & & & & ✓ & \\ \hline HDTA & & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between our work and the related references. rejection threshold-based random algorithm (ONRTA-RT) to solve the issue. [PERSON] et al. [33] improved the allocation utility using reinforcement learning for dynamic online allocation. Task allocation can also be categorized based on objectives, such as maximizing the number of tasks, minimizing total cost, or achieving stable matching and fair allocation. [PERSON] et al. [34] first employed Voronoi diagrams and adaptive weighted Voronoi algorithms for geographic partitioning, followed by reinforcement learning to maximize the total number of allocated tasks while minimizing the average task allocation differences. [PERSON] et al. [35] considered fairness in task allocation from the worker's perspective and solved the Fair and Effective Task Allocation (FETA) problem. [PERSON] et al. [36] proposed a combined solution with two suggested matching techniques to improve the overall matching utility and reduce the blocking ratio, considering worker preferences. [PERSON] et al. [37] studied the multi-objective spatiotemporal task allocation (MOST) problem in spatial crowdsourcing, modeling it as a combinatorial multi-objective optimization (MOO) problem to maximize overall task completion rates and minimize average task time costs. [PERSON] et al.