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Machine Learning Modeling for IoUT Networks [electronic resource] : Internet of Underwater Things /

By: Contributor(s): Material type: TextTextSeries: SpringerBriefs in Computer SciencePublisher: Cham : Springer International Publishing : Imprint: Springer, 2021Edition: 1st ed. 2021Description: XII, 63 p. 32 illus., 24 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783030685676
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 621.382 23
LOC classification:
  • TK5101-5105.9
Online resources:
Contents:
Introduction -- Seawater’s Key Physical Variables -- Opportunistic Transmission -- Localization and Positioning -- ML Modeling for Underwater Communication -- Open Challenges -- Conclusion.
In: Springer Nature eBookSummary: 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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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.

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