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Today's factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transformation of industrial processes. When implemented, Industry 4.0 technologies have a huge impact on efficiency, productivity and profitability of businesses. The adoption and implementation of Industry 4.0, however, require to overcome a number of practical challenges, in most cases, due to the lack of modernisation and automation in place with traditional manufacturers. This paper presents a first of its kind case study for moving a traditional food manufacturer, still using the machinery more than one hundred years old, a common occurrence for small- and medium-sized businesses, to adopt the Industry 4.0 technologies. The paper reports the challenges we have encountered during the transformation process and in the development stage. The paper also presents a smart production control system that we have developed by utilising AI, machine learning, Internet of things, big data analytics, cyber-physical systems and cloud computing technologies. The system provides novel data collection, information extraction and intelligent monitoring services, enabling improved efficiency and consistency as well as reduced operational cost. The platform has been developed in real-world settings offered by an Innovate UK-funded project and has been integrated into the company's existing production facilities. In this way, the company has not been required to replace old machinery outright, but rather adapted the existing machinery to an entirely new way of operating. The proposed approach and the lessons outlined can benefit similar food manufacturing industries and other SME industries. [ABSTRACT FROM AUTHOR]

Established methods of communication are based mainly on Shannon's theory of information, which purposefully overlooks semantic elements of communication. The future wireless technology should promise to facilitate many services, based on content, needs, and semantics, precisely customized to network capabilities. This gave rise to significant concern for Semantic Communication (SC), a novel paradigm considering the message's meaning during transmission. Federated learning (FL) and Asynchronous Advantage Actor Critic (A3C) are the two emerging distributed and artificially intelligent approaches that provide diverse and possibly massive network coverage for data-driven SC solutions of industry 4.0 automation. Although SC is still in an early development stage, FL-empowered architecture has been recognized as one of the most promising solutions to meet the ubiquitous intelligence in the anticipated sixth-generation (6G) networks. This paper identifies industry 4.0 automation needs that drive the convergence of artificial intelligence and 6G for learning from data streams. We develop a novel SC framework based on the FL and A3C networks and discuss its potential along with transfer learning to address most of the new difficulties anticipated in 6G for industrial communication networks. Our proposed framework has been evaluated with extensive simulation results. [ABSTRACT FROM AUTHOR]