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Internet of Underwater Things and Big Marine Data Analytics – a Comprehensive Survey

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Internet of Underwater Things and Big Marine Data Analytics – a Comprehensive Survey ACCEPTED BY IEEE COMMUNICATION SURVEYS & TUTORIALS, 2021 1 Internet of Underwater Things and Big Marine Data Analytics – A Comprehensive Survey Mohammad Jahanbakht , Student Member, IEEE, Wei Xiang , Senior Member, IEEE, Lajos Hanzo , Fellow, IEEE, and Mostafa Rahimi Azghadi , Senior Member, IEEE Abstract—The Internet of Underwater Things (IoUT) is an as well as to make informed decisions and set regulations related emerging communication ecosystem developed for connecting to the maritime and underwater environments around the world. underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine trans- Index Terms—Internet of Things, Big Data, Underwater Net- portations, positioning and navigation, underwater exploration, work Architecture, Data Acquisition, Marine and Underwater disaster prediction and prevention, as well as with intelligent Databases/Datasets, Underwater Wireless Sensor Network, Image monitoring and security. The IoUT has an influence at various and Video Processing, Machine Learning, Deep Neural Networks. 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 NOMENCLATURE creates major challenges in terms of underwater communications, ASIC Application-Specific Integrated Circuit whilst relying on limited energy resources. Additionally, the AUV Autonomous Underwater Vehicles volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise BER Bit Error Rate to the concept of Big Marine Data (BMD), which has its BMD Big Marine Data own processing challenges. Hence, conventional data processing CNN Convolutional Neural Networks techniques will falter, and bespoke Machine Learning (ML) DBN Deep Belief Network solutions have to be employed for automatically learning the DCS Distributed Computing System specific BMD behavior and features facilitating knowledge ex- traction and decision support. The motivation of this paper is DL Deep Learning to comprehensively survey the IoUT, BMD, and their synthesis. FPGA Field-Programmable Gate Array It also aims for exploring the nexus of BMD with ML. We set GIS Geographic Information System out from underwater data collection and then discuss the family GPU Graphics Processing Unit of IoUT data communication techniques with an emphasis on IaaS Infrastructure as a Service the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We IoT Internet of Things treat the subject deductively from an educational perspective, IoUT Internet of Underwater Things critically appraising the material surveyed. Accordingly, the IP Internet Protocol reader will become familiar with the pivotal issues of IoUT MAC Media Access Control and BMD processing, whilst gaining an insight into the state- MEC Mobile Edge Computing of-the-art applications, tools, and techniques. Finally, we analyze the architectural challenges of the IoUT, followed by proposing ML Machine Learning a range of promising direction for research and innovation in NN Neural Network the broad areas of IoUT and BMD. Our hope is to inspire PaaS Platform as a Service arXiv:2012.06712v2 [cs.NI] 18 Jan 2021 researchers, engineers, data scientists, and governmental bodies QoS Quality of Service to further progress the field, to develop new tools and techniques, RBM Restricted Boltzmann Machine RFID Radio Frequency Identification This work is funded by the Australian Government Research Training RNN Recurrent Neural Network Program Scholarship, and in part by Beijing Natural Science Foundation under Grant L182032 (Corresponding authors: Wei Xiang and M. Rahimi Azghadi). RNTN Recursive Neural Tensor Network M. Jahanbakh and M. Rahimi Azghadi are with the College of Science ROV Remotely Operated Vehicles and Engineering, James Cook University, Queensland, Australia (e-mail: SaaS Software as a Service [email protected]; [email protected]). Wei Xiang is with the School of Engineering and Mathematical Sci- SDN Software-Defined Network ences, La Trobe University, Melbourne, VIC 3086, Australia (e-mail: SLAM Simultaneous Localization and Mapping [email protected]). SNR Signal-to-Noise Ratio L. Hanzo is with the School of Electronics and Computer Sci- ence, University of Southampton, Southampton SO17 1BJ, U.K. (e-mail: SVM Support Vector Machine [email protected]). TCP Transmission Control Protocol L. Hanzo would like to acknowledge the financial support of the En- ToF Time of Flight gineering and Physical Sciences Research Council projects EP/N004558/1, EP/P034284/1, EP/P034284/1, EP/P003990/1 (COALESCE), of the Royal UWSN Underwater Wireless Sensor Network Society’s Global Challenges Research Fund Grant as well as of the European Research Council’s Advanced Fellow Grant QuantCom. ACCEPTED BY IEEE COMMUNICATION SURVEYS & TUTORIALS, 2021 2 Amalgamation of Subjects Internet of Underwater Things Big Marine Data Challenges and Directions Architecture, Edge Computing Distributed and Cloud-based Energy Sensors Comm. Routing Security Chanel Tracking NDT AIIssues Protocols, and Storage and Processing Topologies Data Acquisition, SDN Aggregation, and Fusion Machine Learning for BMD Analytics Link Reliability Communication Network Sensor, Image, and Marine Data Feature Technologies Security Video Data Evaluation Cleaning Extraction and Channel Modeling BMD Applications ML Techniques (Monitoring, Tracking, for Big Marine Localization, etc.) Hardware Platforms Sensor, Image, Sensors Simulation Tools and Video Data Fig. 1. Qualitative relationship between the diverse system components of the IoUT and BMD analytics, starting from underwater sensors and ending up to ML solutions and future directions. I. INTRODUCTION ”On the surface of the ocean, men wage war and destroy each other; but down here, just a few feet beneath the surface, there is a calm and peace, unmolested by man.” HE INTERNET OF THINGS (IoT) augmented with — Jules Verne T machine intelligence and big data analytics is expected to transform and revolutionize the way we live in almost Although the IoUT has many technical similarities with every technological area. Broadly speaking, IoT can be defined its ground-based counterpart (IoT) such as its structure and as an infrastructure of the information society that connects function, it has many technical differences arising from equipment/devices (things) to the Internet and to one another. its different communication/telecommunication environments, By this means, the IoT could connect devices in any place computational limitations, and constrained energy resources. on earth to help us have better interaction with our living To address these gaps between the IoT and IoUT, technical environment [1]. concepts in the field of IoUT will be extensively discussed. To date, the existing networks in terrestrial and urban areas These include both the underwater communications [6]–[8], have been the domain of influence for the IoT and have as well as the underwater sensors and devices [9]. been researched extensively. This has made a fairly strong By connecting an increasing number of devices and ma- foundation for the industrial IoT developments, which are chines to the Internet, the IoT and IoUT ecosystems produce emerging with an astonishing pace at the time of writing [2]. enormous amounts of data. This high volume of data is However, the underwater section of IoT, i.e. IoUT has not referred to in parlance as big data. Big data is currently being attracted as much attention as it deserves and it is a rather generated by various technological ecosystems and perhaps unexplored research area. This is mainly because underwater the most ubiquitous data types in today’s world is the data applications are still in their infancy and the new era of produced throughout the IoT. This is also set to increase, scientific endeavor to better understand, control, and interact since the number of Internet-connected devices is projected with the oceans and seas through underwater technologies is to increase from the current 30 billion to over 50 billion by yet to flourish. 2020 [10]. Although 44% of the earth’s population lives within 150 km In the age of sparse data production, analytical mathematics of the sea, 95% of sea area remains unexplored by the and statistical techniques, widely known as data mining, were humankind [3]. Oceans cover more than 70% of the earth’s employed to infer knowledge from data. However, in the surface and 90% of international trades are through nautical current era of data proliferation, when the produced data transportations [4]. Astonishingly, 12 people have spent 300 volume in the last five years exceeds the whole amount hours on the surface of the moon, while only 3 people have of data generated before that, conventional data processing spent about 3 hours at 6 km depth of the ocean. In addition, techniques will soon fall short [11], [12]. These traditional about 90 million tons of salt-water fish are caught worldwide big data handling methods relying on statistical descriptive, each year, and the coral reefs are estimated to provide food for predictive, and prescriptive analytics usually suffer from the almost 500 million people [5]. Hence, underwater research and lack of generalization.
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