Machine Learning for Massive Industrial Internet of Things

Hui Zhou, Changyang She, Yansha Deng*, Mischa Dohler, Arumugam Nallanathan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

The Industrial Internet of Things (IIoT) revolutionizes future manufacturing facilities by integrating Internet of Things technologies into industrial settings. With the deployment of massive IIoT devices, it is difficult for the wireless network to support the ubiquitous connections with diverse quality of service (QoS) requirements. Although machine learning is regarded as a powerful data-driven tool to optimize wireless networks, how to apply machine learning to deal with massive IIoT problems with unique characteristics remains unsolved. In this article, we first summarize the QoS requirements of the typical massive non-critical and critical IIoT use cases. We then identify unique characteristics in the massive IIoT scenario, and the corresponding machine learning solutions with their limitations and potential research directions. We further present the existing machine learning solutions for individual layer and cross-layer problems in massive IIoT. Last but not least, we present a case study of the massive access problem based on deep neural network and deep reinforcement learning techniques, respectively, to validate the effectiveness of machine learning in the massive IIoT scenario.

Original languageEnglish
Article number9535457
Pages (from-to)81-87
Number of pages7
JournalIEEE WIRELESS COMMUNICATIONS
Volume28
Issue number4
DOIs
Publication statusPublished - Aug 2021

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