Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey (2012.06712v2)

Abstract: 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 midsized 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 paper 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.

• The paper presents a comprehensive survey of IoUT and big marine data analytics, detailing network architectures and integration with machine learning.
• It evaluates critical underwater communication technologies including acoustic, electromagnetic, and optical methods while optimizing routing strategies.
• The study outlines machine learning-driven analytics for real-time processing of vast marine datasets, offering actionable insights for maritime applications.

Insights on Internet of Underwater Things and Big Marine Data

The paper "Internet of Underwater Things and Big Marine Data Analytics" serves as a comprehensive survey on the emerging paradigms of connecting underwater objects and managing the voluminous data they generate. This work intricately intertwines the fields of IoUT and BMD with machine learning, thereby highlighting their relevance in various maritime applications such as disaster prediction, environmental monitoring, and international trade facilitation.

Overview and Key Contributions

The IoUT ecosystem represents a burgeoning communication architecture designed to connect underwater nodes with overwater and terrestrial networks. It significantly extends the Internet of Things (IoT) paradigm into the aquatic environment, thus confronting unique challenges such as constrained energy resources, high signal attenuation, and limited bandwidth channels. Key contributions of the paper include:

• Evaluation of Communication Technologies: The authors detail the characteristics and limitations of the primary underwater communication technologies—acoustic, electromagnetic, and optical—and provide insights into improving link reliability through routing and hop-count optimization.
• Survey on IoUT Network Architecture: By aligning the IoUT network design with the TCP/IP model, the paper provides a structured approach to address layer-specific concerns and adaptations for the IoUT, from the physical to application layers.
• Exploration of Big Marine Data Handling: The paper addresses the enormous volume, velocity, and variety of data produced by interconnected underwater sensors. It emphasizes the relevance of adopting bespoke machine learning solutions for real-time BMD analytics, leveraging platforms that range from distributed systems to cloud computing paradigms.

Numerical Results and Application Prospects

The discussions reinforce the need for bespoke machine learning solutions to process the vast oceanic datasets, overcoming the inadequacy of traditional data processing techniques. The machine learning paradigms outlined exhibit capabilities of automatic feature learning and high adaptability to the dynamic marine environment.

Future Prospects and Research Directions

The intersection of IoUT and BMD with AI opens promising research avenues:

• Robust and Affordable Sensor Development: As current underwater sensing devices are associated with high costs, future innovations must target the development of economically feasible sensors without compromising on precision.
• Advanced Communication Protocols: By leveraging multi-modal solutions and software-defined networking, the IoUT aims for enhanced communication reliability in the intricate aquatic milieu.
• Machine Learning-Driven Analytics: The application of deep learning to BMD analytics signifies a transformative step, addressing the challenges posed by data volume and complexity. Research should focus on developing more efficient neural network architectures that can operate with limited and noisy data.

The paper positions itself as an essential resource for scholars and engineers, offering a detailed panorama of the existing and potential capabilities of IoUT and BMD. By envisioning future developments in these domains, it aspires to guide systematic advancements in underwater research infrastructure and analytics.