About a decade after the introduction of Industry 4.0 (I4.0) as a paradigm oriented towards the digitization of industrial environments, centered on the concept of industrial Cyber-physical Systems (CPS) to enable the development of intelligent and distributed industrial systems, many companies around the world are still not immersed in this digital transformation era. This transition is not straightforward and requires the aligned with the novel technologies, architectures and standards to migrate entire traditional systems into I4.0 systems. In this context, this paper presents an approach to perform the digitization of non-I4.0 components/systems into I4.0 through an approach based on the Asset Administration Shell (AAS), which is a standardized digital representation of an asset. This approach enables to hold the asset information throughout its lifecycle, provides a standard communication interface with the asset, and is based on a set of modules that are combined with the AAS to provide novel functionalities for the asset, e.g., monitoring, diagnosis and optimization. Moreover, this approach adopts Multi-agent Systems (MAS) to provide mainly autonomy and collaborative capabilities to the system. The agents are able to get information from the AASs, making intelligent decisions and perform distributed tasks following interaction strategies, e.g., collaboration, negotiation and self-organization. The feasibility of the proposed approach was tested by digitizing a small-scale production system comprising several assets.

Industry 4.0 is re-shaping the way companies and individuals operate, but it is also introducing strong demands in education processes to train professionals with adequate competencies in emergent digital technologies, e.g., Internet of Things (IoT), Artificial Intelligence and collaborative robotics. In the last decade, innovative educational methods are being applied, e.g., problem-based learning and project-based learning, to move the traditional education approach into a more student-centric process where the student has a more active role. Recent studies point out that the combination of such educational methods is beneficial, each one selected according to the particularities of the learning subject and objective. Having this in mind, this paper describes the application of a learning methodology that combines different educational methods, namely face-to-face, problem-based learning and project-based learning, in a teaching course unit focusing on IoT technologies. The achieved results show an increase of the student’s assessment performance, motivation and satisfaction, and the opportunity to consolidate their acquired knowledge with hands-on practice. This approach also stimulates the acquisition of soft skills, mainly teamwork, communication, creativity and critical thinking.

Cyber-Physical Systems (CPS) are devoted to be the main participants in Industry 4.0 (I4.0) solutions. In recent years, many authors have focused their efforts on making proposals for the design and implementation of CPS based on different digital technologies. However, the comparative evaluation of these I4.0 solutions is complex, since there is no uniform criterion when it comes to defining the test scenarios and the metrics to assess them. This paper presents a technology-independent CPS demonstrator for benchmarking I4.0 solutions. To that end, a set of testing scenarios, Key Performance Indicators and services were defined considering the available automation cells setup. The proposed demonstrator has been used to test an I4.0 solution based on a Multi-agent Systems (MAS) approach.

In the digital transformation era, the collection of data assumes a crucial relevance. In some applications, the use of real sensors to measure the target parameters is constrained by technical or economical limitations. In such situations, it is required to use alternative techniques based on soft sensors that acquire data by estimating the measurement of a variable through the correlation of the data acquired by the neighbouring sensors. However, the co-existence of real and soft sensors requires a computational infra-structure that integrates these heterogeneous data sources and supports the synchronisation of the monitoring system based on the inputs of different measurement nodes. Multi-agent systems provide this distributed infra-structure for the data collection, ensuring modularity, scalability and reconfigurability capabilities. This paper introduces a multi-agent system approach to create a modular and scalable sensing system, based on a diversity of real and soft sensors, to support the monitoring of temperature in thin-film sensing surfaces. The proposed approach was experimentally tested in a plastic injection process, presenting promising results in terms of accuracy and response time, and allowing to obtain more sampling points through the use of computational techniques to complement the real data.

The digital transformation that the world is facing has a strong impact in the professional occupations and job profiles in the factories of the future context, requiring the need of upskilling and re-qualification of the workforce. Taking this into account, an Industrial Collaborative Educational Design (ICoED) is presented comprising three stages and eight steps, and considering a democratic and collaborative participation of the different stakeholders, namely the managers, educators and learners, each one providing its own perspective on the design of the training programme. In this co-design process, the analysis of the skills’ gap is a crucial task to prepare the initial stage of the process, particularly identifying the needs in terms of soft and hard skills. The proposed ICoED process was applied to solve an upskilling problem of an industrial metal stamping company, with the participants performing three workshops to execute the eight steps, reaching a training programme with five modules, each one settled with proper activities, resources and infrastructures.

Industry 4.0 (I4.0) is promoting the digitization of traditional manufacturing systems towards flexible, reconfigurable and intelligent factories based on Cyber-Physical Systems (CPS). In this context, the Reference Architecture Model Industrie 4.0 (RAMI4.0) provides guidelines to develop I4.0 compliant solutions based on industrial standards. As the main RAMI4.0 specification, the Asset Administration Shell (AAS) is a standard digital representation of an industrial asset that plays a pivotal role in enabling interoperable communication among I4.0 components across the value chain. This paper provides an analysis of the current state-of-the-art of implementing AAS, discussing, amongst others, the key enabling technologies used to implement the AAS and the alignment of the research works found in the literature with the I4.0 components criteria.

The increase volume of vehicles circulating in large cities and the limited space for parking are factors that motivate the adoption of systems capable of dealing with such problems. In this context, smart parking systems are suitable solutions to avoid the traffic congestion, the air pollution and the long search to find a free parking spot. The inclusion of emergent ICT technologies and artificial intelligence techniques, and particularly using multi-agent systems, combined under the scope of Cyber-Physical Systems (CPS), ensure flexibility, modularity, adaptability and the decentralization of intelligence through autonomous, cooperative and proactive entities. Such smart parking systems can be easily adapted to any type of vehicle to be parked and scalable in terms of the number of parking spots and drivers/vehicles. A fundamental issue in these agent-based CPS parking systems is the interconnection between the cyber and physical counterparts, i.e. between the software agents and the physical asset controllers to access the parking spots. This paper focuses on developing an agent-based CPS for a smart parking system and particularly addressing how the software agents are interconnected with the physical asset controllers using proper Internet of Things technologies. The proposed approach was implemented in two distinct parking systems, one for bicycles and another for cars, showing an efficient, modular, adaptable and scalable operation.

In recent years, the intense urbanization, and consequently the traffic congestion, has been a major concern of large cities. In this context, the development of smart parkings is a suitable solution to deal with this problem. However, the complexity and requirements imposed by such large-scale systems are an obstacle to its easy implementation. In this sense, it is fundamental to adopt emergent ICT and Artificial Intelligence technologies that are capable to address the imposed requirements. Multi-Agent Systems (MAS) is a suitable approach to face this challenge by providing modularity, flexibility, reconfigurability and fast response to condition change based on its decentralized nature. The use of such agent-based solutions to control physical assets, create novel systems entitled Cyber-Physical Systems (CPS) where the interconnection between the cyber and the physical parts is a crucial issue. This paper focuses the interface between the software agents of a smart parking system with the physical control devices of the parking spots. For this purpose, different interface practices were implemented and tested, considering different interaction schemes and technologies. These alternative interface practices were analyzed taking into consideration the response time, scalability and re-usability parameters.