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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

Latif, Shahid; Driss, Maha; Boulila, Wadii; Huma, Zil e; Jamal, Sajjad Shaukat; Idrees, Zeba; Ahmad, Jawad

Authors

Shahid Latif

Maha Driss

Wadii Boulila

Zil e Huma

Sajjad Shaukat Jamal

Zeba Idrees



Abstract

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.

Journal Article Type Article
Acceptance Date Nov 8, 2021
Online Publication Date Nov 12, 2021
Publication Date 2021-11
Deposit Date Jan 31, 2022
Publicly Available Date Jan 31, 2022
Journal Sensors
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 21
Issue 22
Article Number 7518
DOI https://doi.org/10.3390/s21227518
Keywords artificial intelligence; deep learning; internet of things; industrial internet of things; smart industry
Public URL http://researchrepository.napier.ac.uk/Output/2839882

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