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:	Shahid Latif, Maha Driss, Wadii Boulila, Zil e Huma, Sajjad Shaukat Jamal, Zeba Idrees, Jawad Ahmad
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence deep learning internet of things industrial internet of things smart industry Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/0eeb42d00b80478b88fa1683d93aa495
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id	oai:doaj.org-article:0eeb42d00b80478b88fa1683d93aa495
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spelling	oai:doaj.org-article:0eeb42d00b80478b88fa1683d93aa4952021-11-25T18:57:06ZDeep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions10.3390/s212275181424-8220https://doaj.org/article/0eeb42d00b80478b88fa1683d93aa4952021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7518https://doaj.org/toc/1424-8220The 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.Shahid LatifMaha DrissWadii BoulilaZil e HumaSajjad Shaukat JamalZeba IdreesJawad AhmadMDPI AGarticleartificial intelligencedeep learninginternet of thingsindustrial internet of thingssmart industryChemical technologyTP1-1185ENSensors, Vol 21, Iss 7518, p 7518 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence deep learning internet of things industrial internet of things smart industry Chemical technology TP1-1185
spellingShingle	artificial intelligence deep learning internet of things industrial internet of things smart industry Chemical technology TP1-1185 Shahid Latif Maha Driss Wadii Boulila Zil e Huma Sajjad Shaukat Jamal Zeba Idrees Jawad Ahmad Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions
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.
format	article
author	Shahid Latif Maha Driss Wadii Boulila Zil e Huma Sajjad Shaukat Jamal Zeba Idrees Jawad Ahmad
author_facet	Shahid Latif Maha Driss Wadii Boulila Zil e Huma Sajjad Shaukat Jamal Zeba Idrees Jawad Ahmad
author_sort	Shahid Latif
title	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_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_sort	deep learning for the industrial internet of things (iiot): a comprehensive survey of techniques, implementation frameworks, potential applications, and future directions
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0eeb42d00b80478b88fa1683d93aa495
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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

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