# Hybrid Dual-Link Data Transmission Based on Internet of Vessels
## 3 Algorithm Design
### Incentive Mechanism  
To stimulate the motivation and participation of ship workers in order to improve the quality of their work, this study adopts a platform-centered user incentive mechanism. Under this mechanism, ship workers receive certain material rewards after receiving task information and completing it. The results of the experimental study showed that the completion rate of tasks was significantly improved after the introduction of this mechanism. The amount of reward is mainly determined by five key factors: the size of the data packet, the number of task participation instances, the distance of transmission, the quality of work of the ship's workers, and the density size of the ship. This dynamic compensation  
Figure 6: Transmission path. The green triangle represents ship nodes, the red dot represents the selected relay ship node, the red circle represents the transmission range, and the blue dashed arrow indicates the transmission direction. mechanism based on multiple factors aims to ensure the fairness and reasonableness of the compensation in order to motivate the ship workers to participate more actively and put in more effort to improve the task completion rate and work quality. The calculation of the reward amount is mainly composed of the following components: the number of user participation component \(Reward_{1}\), the user quality component (measured here by the user's reputation value) \(Reward_{2}\), and the ship density component of the region \(Reward_{3}\). In addition, the size of the reward amount is directly and positively correlated with the size of the data packet and the distance of transmission. In order to encourage new ship users to participate in the task, increase the participation of new users, and make them more willing to continue to participate in crowdsourcing tasks, ships that participate in the task for the first time are given an additional reward, that is, the initial value is greater, and with the increase in the number of tasks, the initial amount is returned to the normal level. This instant reward is a great way to encourage new ship workers to take on tasks. The formula of \(Reward_{1}\) is as follows:  
\[Reward_{1}=[(\frac{1}{2})^{n}+1]R_{e1} \tag{13}\]  
In this formula, \(n\) is the number of times the user participates in any task, and \(R_{e1}\) is the reward amount for \(Reward_{1}\). The first participation by a new user can earn about twice the expected reward amount. As the number of participation instances increases, the reward grows, and \(Reward_{1}\) approaches \(R_{e1}\) in magnitude. In order to improve the accuracy, completeness, and timeliness of information as well as to reduce errors and delays in information delivery, selecting high-quality workers is a good way to do this. High-quality users usually have higher reliability and can deliver information faster and better. The higher the quality of the ship's workers, which means the higher the reputation value, the higher the amount of compensation will naturally be. The measure of worker quality remains the ratio of the current ship's worker reputation value to the average of all ship's workers' reputation values. \(Q_{p}\) and \(Reward_{2}\) are calculated as follows:  
\[Q_{p}=\frac{Q_{i}}{Q_{avg}} \tag{14}\]  
\[Reward_{2}=\frac{Q_{i}}{Q_{avg}}R_{e2} \tag{15}\]  
where \(Q_{i}\) is the current ship worker's credibility value, \(Q_{avg}\) has the mean value of the ship worker's credibility value, and \(R_{e2}\) is the pre-amount of \(Reward_{2}\). For areas where the density of ship workers is sparse, it may lead to greater difficulty in information and data transmission. However, these areas are often important for data collection and play a key role in the accurate and timely transmission of information.