## 2 Fundamental Models and Literature Search
### Literature Search  
We performed an extensive search of the existing literature utilizing the Scopus ([[https://www.scopus.com/](https://www.scopus.com/)]([https://www.scopus.com/](https://www.scopus.com/)), accessed on 5 August 2023) academic search tool. The search was executed using the prescribed topic framework and keywords outlined in Equation (1). The symbol \"\" was employed to ensure precise term matching. The Scopus platform was instrumental in retrieving relevant papers on this topic across diverse fields of study, encompassing ocean engineering, computer science, automation and control, and electrical engineering. This literature search was carried out in August 2023. By employing the search structure and keywords enumerated in Equation (1), a total of 85 articles pertaining to this subject were identified and subsequently evaluated. Importantly, it should be emphasized that no filtering or exclusion of papers relevant to this section was undertaken. \[\text{TITLE-ABS-KEY (``USV'' OR ``ASV'' OR ``AUV'' OR ``unmanned surface vehicle'' OR ``autonomous underwater vehicle'' OR ``autonomous surface vehicle'') AND TITLE-ABS-KEY (``data collection'' OR ``data harvesting'' OR ``data gathering'' OR ``data acquisition'') AND TITLE-ABS-KEY (``path planning'' OR ``trajectory planning'' OR ``route planning'')  
Figure 6 depicts the categorization of 85 research documents along with a visual representation of keyword cloud. As indicated in Figure 6, these documents encompass a diverse spectrum of 15 distinct categories, underscoring the extensive purview of knowledge in oceanic data collection. It encompasses fields such as engineering, computer science, mathematics, geology, and environmental science, among others. This body of work reflects a comprehensive multidisciplinary engagement. Notably, the Engineering and Computer Science categories constitute the majority, accounting for 35.5% and 33.1%, respectively. Following is the mathematics category, representing 7.8%. This observation underscores the pivotal role of knowledge derived from the domains of engineering and computer science in shaping the field of oceanic data collection. Figure 7 illustrates the distribution of these literature sources among journals and conferences, as well as the variation in the number of publications over the years. From the graph, it can be observed that there is a general upward trend in the quantity of literature on this topic. Compared to the publications before 2020, the number of papers in the last three years has notably increased. We attribute this phenomenon to the rapid advancements in wireless communication and marine robotics, which have stimulated a growth in the number of papers related to ocean data collection. We anticipate that this trend will continue  
Figure 6: Subject categories and keyword cloud. to expand in the future. Furthermore, the pie chart reveals that a significant portion of these publications are featured in IEEE journals (28.6%) and conferences (16.7%), closely followed by journals (15.5%) pertaining to wireless communication. Additionally, some other literature is published in journals focusing on marine engineering, robotics, and sensor domains. This highlights the predominant advantage of IEEE publications in the realm of wireless data transmission research, followed by certain journals specializing in ocean engineering and robotics. Besides considering the quantity of published articles, another significant measure for assessing the quality and influence of these publications is through citation analysis. Table 1 showcases the top 10 most extensively referenced research works in this specific domain. Among these, the most cited piece is a study from 2010, which employs predictive ocean models to devise path planning for AUVs, facilitating underwater data collection missions. This research appeared in the _International Journal of Robotics Research_, and its citation frequency on Scopus has reached 160 times.