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Review
. 2021 Nov 12;21(22):7518.
doi: 10.3390/s21227518.

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

Shahid Latif  1 , Maha Driss  2   3 , Wadii Boulila  3   4 , Zil E Huma  5 , Sajjad Shaukat Jamal  6 , Zeba Idrees  1 , Jawad Ahmad  7
Affiliations
Review

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

Shahid Latif et al. Sensors (Basel). .

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.

Keywords: artificial intelligence; deep learning; industrial internet of things; internet of things; smart industry.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The worldwide market size of the IIoT from 2017 to 2025 [5].
Figure 2
Figure 2
A comparison of publication records for DL-based IoT/IIoT applications.
Figure 3
Figure 3
Reference architecture of the Industrial Internet of Things.
Figure 4
Figure 4
A general architecture of the deep feedforward neural network (DFNN).
Figure 5
Figure 5
A general architecture of the Restricted Boltzmann Machines (RBM).
Figure 6
Figure 6
A general architecture of the Deep Belief Networks (DBN).
Figure 7
Figure 7
A general architecture of an Autoencoder (AE).
Figure 8
Figure 8
A general architecture of the Convolutional Neural Network.
Figure 9
Figure 9
A general architecture of the Recurrent Neural Network (RNN).
Figure 10
Figure 10
A general architecture of the Generative Adversarial Networks (GAN).
Figure 11
Figure 11
Some well-known ML/DL frameworks.
Figure 12
Figure 12
Some well-known Development Boards for DL Implementations.
Figure 13
Figure 13
Applications of DL in agriculture.
Figure 14
Figure 14
Applications of DL in education.
Figure 15
Figure 15
Applications of DL in healthcare.
Figure 16
Figure 16
Applications of DL in intelligent transport system.
Figure 17
Figure 17
Applications of DL in manufacturing industry.
Figure 18
Figure 18
Applications of DL in aviation industry.
Figure 19
Figure 19
Applications of DL in defense.
Figure 20
Figure 20
Applications of DL in sports industry.
See this image and copyright information in PMC

References

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