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

Ruhul Amin Khalil, Nasir Saeed*, Mudassir Masood, Yasaman Moradi Fard, Mohamed Slim Alouini, Tareq Y. Al-Naffouri

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

115 Scopus citations

Abstract

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.

Original languageEnglish
Article number9321458
Pages (from-to)11016-11040
Number of pages25
JournalIEEE Internet of Things Journal
Volume8
Issue number14
DOIs
StatePublished - 15 Jul 2021

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

  • Autoencoders (AEs)
  • Industrial Internet of Things (IIoT)
  • convolutional neural networks (CNNs)
  • deep learning (DL)
  • optimization
  • recurrent neural networks (RNNs)
  • smart industries

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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