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Machine learning modeling for IoUT n...

Aziz El-Banna, Ahmad A.

Machine learning modeling for IoUT networksinternet of underwater things /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning modeling for IoUT networksby Ahmad A. Aziz El-Banna, Kaishun Wu.
Reminder of title:	internet of underwater things /
Author:	Aziz El-Banna, Ahmad A.
other author:	Wu, Kaishun.
Published:	Cham :Springer International Publishing :2021.
Description:	xii, 63 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-68567-6
ISBN:	9783030685676$q(electronic bk.)

Machine learning modeling for IoUT networksinternet of underwater things /
Aziz El-Banna, Ahmad A.

Machine learning modeling for IoUT networksinternet of underwater things /[electronic resource] :by Ahmad A. Aziz El-Banna, Kaishun Wu. - Cham :Springer International Publishing :2021. - xii, 63 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

Introduction -- Seawater's Key Physical Variables -- Opportunistic Transmission -- Localization and Positioning -- ML Modeling for Underwater Communication -- Open Challenges -- Conclusion.

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.

ISBN: 9783030685676$q(electronic bk.)

Standard No.: 10.1007/978-3-030-68567-6doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .A95 2021

Dewey Class. No.: 006.31

Machine learning modeling for IoUT networksinternet of underwater things /
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505 0 $a Introduction -- Seawater's Key Physical Variables -- Opportunistic Transmission -- Localization and Positioning -- ML Modeling for Underwater Communication -- Open Challenges -- Conclusion.
520 $a 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.
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650 0 $a Underwater acoustic telemetry. $3 487205
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650 2 4 $a Artificial Intelligence. $3 212515
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