  Industrial Internet of Things (IIoT) revolutionizes the future manufacturing
facilities by integrating the 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 network, how to apply machine
learning to deal with the massive IIoT problems with unique characteristics
remains unsolved. In this paper, 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 its 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 the
least, we present a case study of massive access problem based on deep neural
network and deep reinforcement learning techniques, respectively, to validate
the effectiveness of machine learning in massive IIoT scenario.
