| Description |
1 online resource (xii, 63 pages) : illustrations (some color) |
| Series |
SpringerBriefs in computer science, 2191-5768 |
|
SpringerBriefs in computer science, 2191-5768
|
| Contents |
Introduction -- Seawater's Key Physical Variables -- Opportunistic Transmission -- Localization and Positioning -- ML Modeling for Underwater Communication -- Open Challenges -- Conclusion |
| 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 |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed June 3, 2021) |
| Subject |
Machine learning.
|
|
Internet of things.
|
|
Underwater acoustic telemetry.
|
|
Internet of things
|
|
Machine learning
|
|
Underwater acoustic telemetry
|
| Form |
Electronic book
|
| Author |
Wu, Kaishun, author
|
| ISBN |
9783030685676 |
|
3030685675 |
|
9783030685683 |
|
3030685683 |
|
