Machine learning modeling for IoUT networks : internet of underwater things / Ahmad A. Aziz El-Banna, Kaishun Wu.
Aziz El-Banna, Ahmad A., author.; Wu, Kaishun, author.
2021
Q325.5
Available Online

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Title Machine learning modeling for IoUT networks : internet of underwater things / Ahmad A. Aziz El-Banna, Kaishun Wu.
Author Aziz El-Banna, Ahmad A., author.
ISBN 9783030685676 (electronic bk.)
3030685675 (electronic bk.)
9783030685683 (print)
3030685683
9783030685669 (print)
3030685667
DOI https://doi.org/10.1007/978-3-030-68567-6
Published Cham, Switzerland : Springer, [2021]
Language English
Description 1 online resource (xii, 63 pages) : illustrations (some color)
Item Number 10.1007/978-3-030-68567-6 doi
Call Number Q325.5
Dewey Decimal Classification 006.3/1
Summary 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.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed June 3, 2021).
Added Author Wu, Kaishun, author.
Series SpringerBriefs in computer science, 2191-5768
Available in Other Form Machine learning modeling for IoUT networks.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Introduction
Seawater's Key Physical Variables
Opportunistic Transmission
Localization and Positioning
ML Modeling for Underwater Communication
Open Challenges
Conclusion.

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