The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a mid-sized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this article is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed. Accordingly, the reader will become familiar with the pivotal issues of IoUT and BMD processing, whilst gaining an insight into the state-of-the-art applications, tools, and techniques. Finally, we analyze the architectural challenges of the IoUT, followed by proposing a range of promising direction for research and innovation in the broad areas of IoUT and BMD. Our hope is to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.

中文翻译：

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水下物联网与海洋大数据分析综合综述


水下物联网（IoUT）是一种新兴的通信生态系统，旨在连接海洋和水下环境中的水下物体。物联网技术与智能船舶、智慧海岸和海洋、自动化海上运输、定位导航、水下探索、灾害预测与预防、智能监控与安全等有着紧密的联系。 IoUT 具有各种规模的影响力，从小型科学观测站到中型港口，再到覆盖全球海洋贸易。 IoUT 的网络架构本质上是异构的，并且应该具有足够的弹性以在恶劣的环境中运行。这给水下通信带来了重大挑战，同时依赖有限的能源。此外，物联网中的传感器、水听器和摄像头产生的数据量、速度和种类都非常巨大，催生了海洋大数据 (BMD) 的概念，但它也有其自身的处理挑战。因此，传统的数据处理技术将会失效，必须采用定制的机器学习 (ML) 解决方案来自动学习特定的 BMD 行为和特征，以促进知识提取和决策支持。本文的动机是全面调查 IoUT、BMD 及其综合。它还旨在探索 BMD 与 ML 的联系。我们从水下数据收集出发，然后讨论 IoUT 数据通信技术系列，重点讨论最先进的研究挑战。然后，我们审查适合 BMD 处理和分析的 ML 解决方案套件。 我们从教育的角度演绎地对待这个主题，批判性地评价所调查的材料。因此，读者将熟悉 IoUT 和 BMD 处理的关键问题，同时深入了解最先进的应用、工具和技术。最后，我们分析了 IoUT 的架构挑战，然后提出了 IoUT 和 BMD 广泛领域的一系列有前景的研究和创新方向。我们的希望是激励研究人员、工程师、数据科学家和政府机构进一步推动该领域的发展，开发新的工具和技术，并做出明智的决策并制定与世界各地海洋和水下环境相关的法规。