Research Output
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 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.

  • Type:

    Article

  • Date:

    12 November 2021

  • Publication Status:

    Published

  • Publisher

    MDPI AG

  • DOI:

    10.3390/s21227518

  • Cross Ref:

    10.3390/s21227518

  • Funders:

    Edinburgh Napier Funded

Citation

Latif, S., Driss, M., Boulila, W., Huma, Z. E., Jamal, S. S., Idrees, Z., & Ahmad, J. (2021). Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions. Sensors, 21(22), Article 7518. https://doi.org/10.3390/s21227518

Authors

Keywords

artificial intelligence; deep learning; internet of things; industrial internet of things; smart industry

Monthly Views:

Available Documents