Internet of Underwater Things and Big Marine Data Analytics-A Comprehensive Survey

被引:278
作者
Jahanbakht, Mohammad [1 ]
Xiang, Wei [2 ]
Hanzo, Lajos [3 ]
Rahimi Azghadi, Mostafa [1 ]
机构
[1] James Cook Univ, Coll Sci & Engn, Townsville, Qld 4814, Australia
[2] La Trobe Univ, Sch Engn & Math Sci, Melbourne, Vic 3086, Australia
[3] Univ Southampton, Sch Elect & Comp Sci, Southampton SO17 1BJ, Hants, England
基金
欧洲研究理事会; 英国工程与自然科学研究理事会; 北京市自然科学基金;
关键词
Big Data; Sensors; Tutorials; Machine learning; Tools; Internet of Things; Distributed databases; big data; underwater network architecture; data acquisition; marine and underwater databases; datasets; underwater wireless sensor network; image and video processing; machine learning; deep neural networks; WIRELESS SENSOR NETWORKS; OF-THE-ART; INERTIAL NAVIGATION; ACOUSTIC NETWORKS; LINK RELIABILITY; NEURAL-NETWORK; LIVE FISH; LOW-POWER; CLASSIFICATION; ALGORITHM;
D O I
10.1109/COMST.2021.3053118
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:904 / 956
页数:53
相关论文
共 316 条
[1]   Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges [J].
Aalsalem, Mohammed Y. ;
Khan, Wazir Zada ;
Gharibi, Wajeb ;
Khan, Muhammad Khurram ;
Arshad, Quratulain .
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2018, 113 :87-97
[2]   A Smart Sensor Network for Sea Water Quality Monitoring [J].
Adamo, Francesco ;
Attivissimo, Filippo ;
Carducci, Carlo Guarnieri Calo ;
Lanzolla, Anna Maria Lucia .
IEEE SENSORS JOURNAL, 2015, 15 (05) :2514-2522
[3]   Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises [J].
Adeli, Ehsan ;
Thung, Kim-Han ;
An, Le ;
Wu, Guorong ;
Shi, Feng ;
Wang, Tao ;
Shen, Dinggang .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2019, 41 (02) :515-522
[4]   Solving ill-posed inverse problems using iterative deep neural networks [J].
Adler, Jonas ;
Oktem, Ozan .
INVERSE PROBLEMS, 2017, 33 (12)
[5]   The role of big data analytics in Internet of Things [J].
Ahmed, Ejaz ;
Yaqoob, Ibrar ;
Hashem, Ibrahim Abaker Targio ;
Khan, Imran ;
Ahmed, Abdelmuttlib Ibrahim Abdalla ;
Imran, Muhammad ;
Vasilakos, Athanasios V. .
COMPUTER NETWORKS, 2017, 129 :459-471
[6]   A simplified formula for viscous and chemical absorption in sea water [J].
Ainslie, MA ;
McColm, JG .
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 1998, 103 (03) :1671-1672
[7]   SoftWater: Software-defined networking for next-generation underwater communication systems [J].
Akyildiz, Ian F. ;
Wang, Pu ;
Lin, Shih-Chun .
AD HOC NETWORKS, 2016, 46 :1-11
[8]   Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications [J].
Al-Fuqaha, Ala ;
Guizani, Mohsen ;
Mohammadi, Mehdi ;
Aledhari, Mohammed ;
Ayyash, Moussa .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2015, 17 (04) :2347-2376
[9]   Survey of computer vision algorithms and applications for unmanned aerial vehicles [J].
Al-Kaff, Abdulla ;
Martin, David ;
Garcia, Fernando ;
de la Escalera, Arturo ;
Maria Armingol, Jose .
EXPERT SYSTEMS WITH APPLICATIONS, 2018, 92 :447-463
[10]   Building Novel VHF-Based Wireless Sensor Networks for the Internet of Marine Things [J].
Al-Zaidi, Rabab ;
Woods, John C. ;
Al-Khalidi, Mohammed ;
Hu, Huosheng .
IEEE SENSORS JOURNAL, 2018, 18 (05) :2131-2144