research-article

Machine Learning for Self-Adaptive Internet of Underwater Things

Author:

Rodolfo W. L. CoutinhoAuthors Info & Claims

DIVANet '20: Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

Pages 65 - 69

https://doi.org/10.1145/3416014.3424615

Published: 16 November 2020 Publication History

Abstract

Internet of Underwater Things (IoUTs) has gained increased momentum thanks to the advancements in underwater nodes, sensing, and communication technologies. This novel paradigm has tremendous potential to empower smart ocean applications. However, the harsh and dynamic nature of the underwater environment and underwater communication, the stringent requirements of underwater applications, and the difficulty and cost for IoUT management and maintenance have limited the development and application of IoUTs. In this regard, machine learning has been proposed to create self-adaptive IoUTs and boost the performance of smart oceans applications. In this paper, we shed light on the design of machine learning models for the on-the-fly intelligent and autonomous management of IoUT networking parameters and configurations aimed at boosting data delivery. We discuss the recent proposals for IoUT network management and how machine learning algorithms can improve such solutions at different networking layers. Finally, we point out some future research directions in need of further attention.

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Cited By

Ullah Khan SUlalh Khan ZAlkhowaiter MKhan JUllah S(2024)Energy-efficient routing protocols for UWSNs: A comprehensive review of taxonomy, challenges, opportunities, future research directions, and machine learning perspectivesJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10212836:7(102128)Online publication date: Sep-2024
https://doi.org/10.1016/j.jksuci.2024.102128
Mohsan SMazinani AOthman NAmjad H(2022)Towards the internet of underwater things: a comprehensive surveyEarth Science Informatics10.1007/s12145-021-00762-815:2(735-764)Online publication date: 7-Mar-2022
https://doi.org/10.1007/s12145-021-00762-8
Coutinho RNotare MSun P(2021)Tutorial: Edge Computing for Mobile Internet of ThingsProceedings of the 11th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications10.1145/3479243.3494705(1-1)Online publication date: 22-Nov-2021
https://dl.acm.org/doi/10.1145/3479243.3494705

Index Terms

Machine Learning for Self-Adaptive Internet of Underwater Things
1. Networks

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cover image ACM Conferences

DIVANet '20: Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

November 2020

76 pages

ISBN:9781450381215

DOI:10.1145/3416014

General Chair:
Mirela Notare
Technology University on Fly Transportation, Brazil
,
Program Chair:
Peng Sun
Duke Kunshan University, China

Copyright © 2020 ACM.

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SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2020

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MSWiM '20

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SIGSIM

MSWiM '20: 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 16 - 20, 2020

Alicante, Spain

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Cited By

Ullah Khan SUlalh Khan ZAlkhowaiter MKhan JUllah S(2024)Energy-efficient routing protocols for UWSNs: A comprehensive review of taxonomy, challenges, opportunities, future research directions, and machine learning perspectivesJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10212836:7(102128)Online publication date: Sep-2024
https://doi.org/10.1016/j.jksuci.2024.102128
Mohsan SMazinani AOthman NAmjad H(2022)Towards the internet of underwater things: a comprehensive surveyEarth Science Informatics10.1007/s12145-021-00762-815:2(735-764)Online publication date: 7-Mar-2022
https://doi.org/10.1007/s12145-021-00762-8
Coutinho RNotare MSun P(2021)Tutorial: Edge Computing for Mobile Internet of ThingsProceedings of the 11th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications10.1145/3479243.3494705(1-1)Online publication date: 22-Nov-2021
https://dl.acm.org/doi/10.1145/3479243.3494705

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