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. 2020 May;7(5):10.1109/jiot.2019.2963635.
doi: 10.1109/jiot.2019.2963635.

Towards Edge-Based Deep Learning in Industrial Internet of Things

Fan Liang  1 , Wei Yu  1 , Xing Liu  1 , David Griffith  2 , Nada Golmie  2
Affiliations

Towards Edge-Based Deep Learning in Industrial Internet of Things

Fan Liang et al. IEEE Internet Things J. 2020 May.

Abstract

As a typical application of the Internet of Things (IoT), the Industrial Internet of Things (IIoT) connects all the related IoT sensing and actuating devices ubiquitously so that the monitoring and control of numerous industrial systems can be realized. Deep learning, as one viable way to carry out big data-driven modeling and analysis, could be integrated in IIoT systems to aid the automation and intelligence of IIoT systems. As deep learning requires large computation power, it is commonly deployed in cloud servers. Thus, the data collected by IoT devices must be transmitted to the cloud for training process, contributing to network congestion and affecting the IoT network performance as well as the supported applications. To address this issue, in this paper we leverage fog/edge computing paradigm and propose an edge computing-based deep learning model, which utilizes edge computing to migrate the deep learning process from cloud servers to edge nodes, reducing data transmission demands in the IIoT network and mitigating network congestion. Since edge nodes have limited computation ability compared to servers, we design a mechanism to optimize the deep learning model so that its requirements for computational power can be reduced. To evaluate our proposed solution, we design a testbed implemented in the Google cloud and deploy the proposed Convolutional Neural Network (CNN) model, utilizing a real-world IIoT dataset to evaluate our approach. Our experimental results confirm the effectiveness of our approach, which can not only reduce the network traffic overhead for IIoT, but also maintain the classification accuracy in comparison with several baseline schemes.

Keywords: Distributed deep learning; Edge Computing; Fog Computing; Industrial IoT.

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Figures

Fig. 1.
Fig. 1.
The Problem Space of the IIoT
Fig. 2.
Fig. 2.
Examples of the T-Less Dataset
Fig. 3.
Fig. 3.
Test Accuracy for Different Number of CNN Layers
Fig. 4.
Fig. 4.
Training Speed for Different Number of CNN Layers
Fig. 5.
Fig. 5.
Structure of CNN Model
Fig. 6.
Fig. 6.
Testbed Structure
Fig. 7.
Fig. 7.
Computation Power Estimation, with 95% confidence intervals shown
Fig. 8.
Fig. 8.
Loss Comparison Between Cloud-Based and Edge-Based Model, with 95% confidence intervals shown
Fig. 9.
Fig. 9.
Accuracy Comparison Between Cloud-Based and Edge-Based Model, with 95% confidence intervals shown
Fig. 10.
Fig. 10.
Loss Comparison for Different CNN Models, with 95% confidence intervals shown
Fig. 11.
Fig. 11.
Accuracy Comparison for Different CNN Models, with 95% confidence intervals shown
Fig. 12.
Fig. 12.
Training Time for Different CNN Models, with 95% confidence intervals shown
See this image and copyright information in PMC

References

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