Internet of underwater things and big marine data analytics — a comprehensive survey

Jahanbakht, Mohammad, Xiang, Wei, Hanzo, Lajos, and Rahimi Azghadi, Mostafa (2021) Internet of underwater things and big marine data analytics — a comprehensive survey. IEEE Communications Surveys & Tutorials, 23 (2). pp. 904-956.

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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 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.

Item ID: 68687
Item Type: Article (Research - C1)
ISSN: 1553-877X
Keywords: Big Data; Sensors; Tutorials; Machine learning; Tools; Internet of Things; Distributed databases
Copyright Information: © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Funders: Australian Government (AG), Beijing Natural Science Foundation (BNSF), Engineering and Physical Sciences Research Council (EPSRC), Royal Society (RS), European Research Council (ERC)
Projects and Grants: AG Research Training Program Scholarship, BNSF Grant L182032, EPSRC Project EP/N004558/1, EPSRC Project EP/P034284/1, EPSRC Project EP/P003990/1 (COALESCE), RS Global Challenges Research Fund Grant, ERC Advanced Fellow Grant QuantCom
Date Deposited: 21 Jul 2021 03:39
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 50%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220408 Information systems @ 50%
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