Towards Industry 5.0: Intelligent Reflecting Surface (IRS) in Smart Manufacturing

Industry 5.0 envisions close cooperation between humans and machines that requires ultra-reliable and low latency communications (URLLC). Intelligent Reflecting Surface (IRS) has the potential to play a crucial role in realizing wireless URLLC for Industry 5.0. IRS is forecasted to be a key enabler of 6G wireless communication networks as it can significantly improve the wireless network's performance by creating a controllable radio environment. In this paper, we first provide an overview of IRS technology and then conceptualize the potential for IRS implementation in a smart manufacturing environment to support the emergence of Industry 5.0 with a series of applications. Finally, to stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of the IRS technology in modern smart manufacturing.

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Network Control Systems (NCSs) have attracted much interest over the past decade as part of a move towards more decentralised control applications and the rise of cyberphysical system applications. Many practical NCSs face the challenges of limited communication bandwidth resources, reliability and lack of knowledge of network dynamics, particularly when wireless networks are involved. Machine learning (ML) combined with event-triggered control (ETC) has the potential to ease some of these challenges. For example, ML can be used to overcome the problem of a lack of network models by learning system behaviour or adapt to dynamically changing models by continually learning model dynamics. ETC can help to conserve bandwidth resources by communicating only when needed or when resources are available. Here, we present a review of the literature on work that combines ML and ETC. The literature on supervised, semi-supervised, unsupervised and reinforcement learning based approaches such as deep reinforcement learning and statistical learning in combination with ETC is explored. Furthermore, the difference between the application of these learning algorithms on model-based and model-free systems are discussed. Following the analysis of the literature, we highlight open research questions and challenges related to ML-based ETC and propose approaches to possible solutions to these challenges.