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Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT fra...

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Autores principales: Latif, Shahid, Driss, Maha, Boulila, Wadii, Huma, Zil e, Jamal, Sajjad Shaukat, Idrees, Zeba, Ahmad, Jawad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625089/
https://www.ncbi.nlm.nih.gov/pubmed/34833594
http://dx.doi.org/10.3390/s21227518
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author Latif, Shahid
Driss, Maha
Boulila, Wadii
Huma, Zil e
Jamal, Sajjad Shaukat
Idrees, Zeba
Ahmad, Jawad
author_facet Latif, Shahid
Driss, Maha
Boulila, Wadii
Huma, Zil e
Jamal, Sajjad Shaukat
Idrees, Zeba
Ahmad, Jawad
author_sort Latif, Shahid
collection PubMed
description The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.
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spelling pubmed-86250892021-11-27 Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions Latif, Shahid Driss, Maha Boulila, Wadii Huma, Zil e Jamal, Sajjad Shaukat Idrees, Zeba Ahmad, Jawad Sensors (Basel) Review The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors. MDPI 2021-11-12 /pmc/articles/PMC8625089/ /pubmed/34833594 http://dx.doi.org/10.3390/s21227518 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Latif, Shahid
Driss, Maha
Boulila, Wadii
Huma, Zil e
Jamal, Sajjad Shaukat
Idrees, Zeba
Ahmad, Jawad
Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions
title Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions
title_full Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions
title_fullStr Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions
title_full_unstemmed Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions
title_short Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions
title_sort deep learning for the industrial internet of things (iiot): a comprehensive survey of techniques, implementation frameworks, potential applications, and future directions
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625089/
https://www.ncbi.nlm.nih.gov/pubmed/34833594
http://dx.doi.org/10.3390/s21227518
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