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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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-11, Vol.21 (22), p.7518
Main Authors: Latif, Shahid, Driss, Maha, Boulila, Wadii, Huma, Zil E, Jamal, Sajjad Shaukat, Idrees, Zeba, Ahmad, Jawad
Format: Article
Language:English
Subjects:
Actuators
Algorithms
Artificial Intelligence
Big Data
Communication
Comparative analysis
Computer Security
Data analysis
Deep Learning
Industrial applications
Industrial Internet of Things
Industry
Internet of Things
Manufacturing
Review
Sensors
smart industry
Smart sensors
Software
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21227518