Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications

Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of interconnected devices, allowing the use of various smart applications. The enormous number of IoT devices generates a large volume of data that requires further intelligent data analysis and processing methods such as deep learning (DL). Notably, DL algorithms, when applied to the Industrial IoT (IIoT), can provide various new applications, such as smart assembling, smart manufacturing, efficient networking, and accident detection and prevention. Motivated by these numerous applications, in this article, we present the key potentials of DL in IIoT. First, we review various DL techniques, including convolutional neural networks, autoencoders, and recurrent neural networks, as well as their use in different industries. We then outline a variety of DL use cases for IIoT systems, including smart manufacturing, smart metering, and smart agriculture. We delineate several research challenges with the effective design and appropriate implementation of DL-IIoT. Finally, we present several future research directions to inspire and motivate further research in this area.

Automated summary

Recent advances in IoT have led to a proliferation of interconnected devices and the use of various smart applications, with the application of deep learning algorithms in IIoT providing various new applications. This article introduces various DL techniques and their applications in different industries, as well as use cases and research challenges in smart manufacturing, smart metering, and smart agriculture in IIoT systems.