The Internet of Things has revolutionized the lifestyle in all aspects. Considering the huge number of connected objects and the plethora of real-time services, edge computing approaches have emerged. Resource allocation is one of the most important challenges in the Internet of Things. Here edge computing allows the use of resources at the edge network, hence, filling the gap between cloud and end-devices. The network resource allocation should meet users’ expectations and provide optimal use of resources. Today, most of the systems are moving toward a self-x concept, such as self-organizing. As a result, these systems must be aware of users’ preferences and the current state of the IoT ecosystem in order to adapt themselves to the conditions. In this context, we benefit from the employment of semantic technologies as these enhance the current systems with information modeling and reasoning capabilities which effectively support the allocation of IoT network resources.

In the past two decades, the use of ontologies has been proven to be an effective tool for enriching existing information systems in the digital data modelling domain and exploiting those assets for semantic interoperability. With the rise of Industry 4.0, the data produced on assembly lines within factories is becoming particularly interesting to leverage precious information. However, adding semantics to data that already exists remains a challenging process. Most manufacturing assembly lines predate the outbreak of graph data, or have adopted other data format standards, and the data they produce is therefore difficult to automatically map to RDF. This has been a topic of research an ongoing technical issue for almost a decade, and if certain mapping approaches and mapping languages have been developed, they are difficult to use for an automatic, large-scale data conversion and are not standardized. In this research, a technical approach for converting existing data to semantics has been developed. This paper presents an overview of this approach, as well as two concrete tools that we have built based on it. The results of these tools are discussed as well as recommendations for future research.

The latest developments in Model-Based Systems Engineering (MBSE) and Product Life-Cycle Management (PLM) are playing a role in the evolution of the aeronautical industry. Despite the reluctance of this domain to accept the introduction of technology leaps in the production process - mostly due to safety reasons - aircraft manufacturers are slowly moving to a new digital factory concept. The deployment of a PLM Tool for Aircraft Ground Functional testing with Eco-design criteria can be leveraged to improve both sustainability of the assembly line and efficiency of the Ground System Tests process end to end, however, heterogeneous data interoperability represents one of the major challenges in this framework. The ontology-based solution proposed in this work addresses this challenge, thus, shows how semantics can be exploited to streamline the data pipeline throughout a PLM digital platform.

With the increasing diffusion of the Industry 4.0 paradigm, the role of industrial training is becoming more and more relevant. Thanks to new technologies and innovative business processes now available, a great amount of data can be collected from the shop floor and elaborated in order to provide a real-time evaluation of the expertise level of the workers. In this regard, the paper aims at presenting and expanding the Training Data Evaluation Tool previously developed by the authors. Results achieved pave the way towards the final deployment of the Training Evaluation suite of Ontologies through an integrated software stack using semantic technologies for knowledge management along with a Training Analytics Model. Integration tests of the used technologies have been carried out using real industrial data showing the feasibility and robustness of the proposed solution.

In a previous research, a tool integrating the HAZOP analysis method and Coloured Petri Net formalism to support the analysis carried out by specialists during a HAZOP brainstorming session has been provided. In that work, the tool was used to simulate the behaviour of a few components of a chemical plant while diverse abnormal scenarios occur. In the present work, other types of components have been modelled and the behaviour of the plant has been simulated demonstrating its ability to model more complex components, simulate diverse failure types, hence, reducing the total time required to complete the analysis compared to a standard HAZOP assessment approach.

In the past two decades, the use of ontologies has been proven to be an effective tool for enriching existing information systems in the digital data modelling domain and exploiting those assets for semantic interoperability. Despite the presence of many databases for industrial skills and professions, a formal representation, namely, an ontology, which meets the requirements of an existing tool for skill and capability analysis called CaMDiF is missing. In this research, the MSDL ontology, used by the tool in its initial version, is extended by importing modules of upper level and mid level ontologies (BFO and Agent Ontology) and by developing a new ontology for industry skills and professions based on an existing non-ontological resource (O*NET). As a result, an overview of the enriched data structure is provided along with some discussions on a use case related to skill analysis enabled by the new CaMDiF’s skill modelling features.

The industrial domain is experiencing a so-called fourth industrial revolution in which the evergrowing complexity of manufacturing information, the increasing amount of knowledge and the use of web-oriented techniques, represent three crucial factors that are accelerating the growth of complexity of industrial systems. On the other hand, continuous-evolving requirements in industrial environments, due to technology outbreaks and a new global marketplace, have led to an on-going evolution of human resource management through the creation and adoption of alternative business models. In the past decade, semantic models such as ontologies have been proven to be effective for many knowledge-intensive applications, since they provide formal models of domain knowledge that can be exploited in different ways. For all these reasons, an innovative human resource optimisation (HRO) engine is introduced, which employs semantically enhanced information and conditional random field (CRFs) probabilistic models with knowledge derived from industrial shop floor level, and proposes the right person for the right job in real-time shop floor operations towards optimising decisions on how to implement and schedule either repeatedly or non-occurring tasks. Industrial information data flow and semantic enrichment were ensured through the combined use of a common interface data exchange model (CIDEM) and ontologies, after which a feasibility study at a chemical plant presented interesting preliminary results.

The recent advancements of manufacturing towards the Industry 4.0 paradigm should be supported by the effective training of industrial workers in order to align their skills to the new requirements of companies. Therefore, the evaluation of the training is becoming in this context increasingly important, given also the possibility of exploiting a huge amount of data from the shop floor about the workers' activities. These data – indeed – can be properly collected and analysed so as to provide real-time indications about the workers' performances and an evolving classification of their skills. In order to pursue this objective, a solution can be represented by the integration of semantic technologies with training evaluation models. For this reason, the paper aims at presenting a Training Data Evaluation Tool (TDET), which is based on the integration of a Training Evaluation Ontology (TEO) with a Training Analytics Model (TAM) for the definition of the skill levels of the workers. The main components and features of the TDET are provided in order to show its suitability towards the collection of data from the shop floor and their subsequent elaboration in summary indicators to be used by the management of the company. Finally, the implications and next steps of the research are discussed.

The scope of this work is to demonstrate the potential of industrial semantics using ontologies for the proper visual notification at process units. The suggested approach is driven by the need for safe operation and maintenance (preventive or condition-based) for the operators and the technical team utilizing knowledge from the workers, combined with reasoning techniques. As a result the traditional (Human Machine Interfaces) HMIs are extended by providing enhanced online information to the workers about the status of a plant's subsystems. The semantically-enriched platform is tested in terms of feasibility at a pilot plant of CERTH/CPERI and some interesting preliminary results were derived.

Modelling and simulation are two relevant facets for thorough and effective analysis of industrial systems that nowadays have to cope with the evergrowing complexity of the industrial processes and the need of modelling flexibility and knowledge sharing. For all these reasons, the following work seeks to explore and combine together different methodologies by exploiting their best features. In particular, the current research aims to combine semantic technologies, such as ontologies, and high-level Petri nets to revamp the actual assembly systems. Thus, key research concepts are presented, explaining such potential integration and providing a short example of the dynamic configuration of an assembly system within a semantically enriched modelling environment.

In the last decade, the interest and effort towards the use of ontology-based solutions for knowledge management has significantly increased. Ontologies have been used in manufacturing to provide a formal representation of the domain knowledge in a way that is machine-understandable. However, despite the ability to formally represent the elements of a domain and their relations, ontologies themselves do not provide any kind of simulation and systems behaviour analysis capabilities. Manufacturing system knowledge may be translated into specific executable models by exploiting experience and human logical deduction. This can be also achieved using ontologies and semantic reasoning. The framework presented in this work, therefore, aims to explore a W3C standard for inference rules, such as Semantic Web Rule Language (SWRL), and OWL ontology models to transform elements of a Knowledge-Base (KB) into Petri Net (PN) primitives. The combination of semantics and mathematical modelling techniques applied to the analysis of a simple automated assembly station highlights the existence of modelling patterns and the effectiveness of inference rules to automatically instantiate PN-based manufacturing system models. As results, the inference rules-driven instantiation of a semantically enriched PN model has two positive consequences: (i) the axioms upon which the manufacturing system ontology is built are easy-to-reuse; (ii) the semantics-based bridging of the analysed domains shows the possibility of further enriching the KB with both qualitative and quantitative assessment capabilities.

The aim of this work is to demonstrate the potential of introducing ontologies for an appropriate visual status signaling at industrial process units. The need for safe operation and maintenance, either preventive or condition-based, drives the proposed approach, which supports operators and technical team utilizing knowledge from the workers, combined with reasoning techniques. As a consequence, the traditional Human Machine Interfaces (HMIs) are able to provide appropriate information to the workers about the status of a plant's subsystems. As far as the overall proposed approach is concerned, the adoption of semantics in this framework is not to say that all of the data integration issues and increasingly less distributed ontologies, are solved. Here, semantics modelling and semantics-driven (or semantic rules-based) analysis methods are exploited to conceptualize a semantically-enriched platform, and test it in terms of feasibility at a chemical pilot plant of CERTH/CPERI, showing some interesting preliminary results.

Human resources are one of the most important assets of an organization. The setup of a proper Human Resource Management system undoubtedly represents one of the pillars upon which any organization should be built. Many effective standards and solutions have been proposed in the past decades. However, the ever changing environment and the emerging technologies, such as ontologies and linked data, lead to adapt them and consider new approaches. The solution proposed in this document aims to combine existing standards for manufacturing information and ontology modelling. As a result, the development of an ontology model enhancing the HR information flow with semantics, on the one hand, enables the use of common data formats and exchange protocols promoted by the world Wide Web Consortium (W3C) and exploitable on the Sematic Web. On the other hand, it lays the foundations for an automated decision making process based on inference rules and smart data management. A study has been performed in a real-life industry revealing highly notable results.

Knowledge modelling at industrial level consists an importunate activity nowadays due to the ceaseless advances in technologies and standards applied as well as the extensive amount of unrelated real-time and historical data at shop-floor level. A Common Interface Data Exchange Model (CIDEM) is hereby introduced towards unifying continuously produced data from heterogeneous and distributed information sources - on different levels and granularities - into a shared vocabulary that can unobtrusively communicate with industrial standards and protocols (i.e. B2MML, gbXML, MIMOSA). For further enhancing the information model a conceptual definition is employed leading to a semantically enriched model which enables more understandable high level knowledge diffusion. This model has been applied to various industrial applications, one of the most important being industrial safety, through incident recognition.

To support the knowledge of specialists during a HAZOP brain-storming session, a support system, which is able to automatically generate a preliminary HAZOP report was developed. The support system, which is based on Coloured Petri Nets (CPNs), simulates the behaviour of the system when different abnormal scenarios occur. The research demonstrates that integration of CPNs and HAZOP is very effective to obtain a smart tool for risk assessment of complex systems, improving the HAZOP analysis procedures.