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Machine Learning Modeling for IoUT Networks

Internet of Underwater Things

  • Book
  • © 2021

Overview

Authors:
  1. Ahmad A. Aziz El-Banna

    1. Shenzhen University, Nanshan, China

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  2. Kaishun Wu

    1. Shenzhen University, Nanshan, China

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    You can also search for this author in PubMed   Google Scholar

  • Presents the basics of the Internet of Underwater Things (IoUT) architecture and underwater transmission
  • Includes applications of machine learning techniques for underwater communication
  • Features open challenges in the two perspectives of communication and machine learning for underwater networking

Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xii
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  2. Introduction to Underwater Communication and IoUT Networks

    • Ahmad A. Aziz El-Banna, Kaishun Wu
    Pages 1-8

  3. Seawater’s Key Physical Variables

    • Ahmad A. Aziz El-Banna, Kaishun Wu
    Pages 9-15

  4. Opportunistic Transmission in IoUT Networks

    • Ahmad A. Aziz El-Banna, Kaishun Wu
    Pages 17-35

  5. Localization and Positioning for Underwater Networks

    • Ahmad A. Aziz El-Banna, Kaishun Wu
    Pages 37-44

  6. ML: Modeling for Underwater Communication in IoUT Systems

    • Ahmad A. Aziz El-Banna, Kaishun Wu
    Pages 45-53

  7. Open Challenges for IoUT Networks

    • Ahmad A. Aziz El-Banna, Kaishun Wu
    Pages 55-59

  8. Back Matter

    Pages 61-63
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Keywords

About this book

This book discusses how machine learning and the Internet of Things (IoT) are playing a part in smart control of underwater environments, known as Internet of Underwater Things (IoUT). The authors first present seawater’s key physical variables and go on to discuss opportunistic transmission, localization and positioning, machine learning modeling for underwater communication, and ongoing challenges in the field. In addition, the authors present applications of machine learning techniques for opportunistic communication and underwater localization. They also discuss the current challenges of machine learning modeling of underwater communication from two communication engineering and data science perspectives.

Authors and Affiliations

  • Shenzhen University, Nanshan, China

    Ahmad A. Aziz El-Banna, Kaishun Wu

About the authors

Ahmad A. Aziz El-Banna received his Master’s degree in 2011 from Benha University and his Ph.D in 2014 from Egypt-Japan University of Science & Technology. Since June 2018, he has been a postdoctoral fellow at Shenzhen University, China. He also holds the position of an assistant professor at Benha University, Egypt. He served as a visiting researcher at Osaka University at Japan (2013–2014). His research interests include cooperative networking, MIMO, space-time coding, IoT, machine learning, and underwater communication.

 

Kaishun Wu received the Ph.D. degree in computer science and engineering from HKUST in 2011. After that, he worked as a Research Assistant Professor with HKUST. In 2013, he joined SZU as a Distinguished Professor. He has coauthored two books and published over 100 high quality research articles in international leading journals and primer conferences, such as IEEE TMC, IEEE TPDS, ACM MobiCom, and IEEE INFOCOM. He is also the inventor of 6 U.S. and over 90 Chinese pending patents. He is a fellow of IET. He received the 2012 Hong Kong Young Scientist Award and the 2014 Hong Kong ICT Awards: Best Innovation and 2014 IEEE ComSoc Asia–Pacific Outstanding Young Researcher Award.

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