Modern large-scale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators. Demands for more flexible reconfiguration of production systems and optimization across different information models, standards and legacy systems challenge current system interoperability concepts. Automatic semantic translation across information models and standards is an increasingly important problem that needs to be addressed to fulfill these demands in a cost-efficient manner under constraints of human capacity and resources in relation to timing requirements and system complexity. Here we define a translator-based operational interoperability model for interacting cyber-physical systems in mathematical terms, which includes system identification and ontology-based translation as special cases. We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning. Possibilities to learn translators between artefacts without a common physical context, for example in simulations of digital twins and across layers of the automation pyramid are briefly discussed.
The development of more potent interoperability methods and technologies are of central importance for modern SOA, like the aforementioned Arrowhead Framework. For example, ontology-based XML-message translation has been extended with semantic annotations @cite_30 , see also former work in @cite_4 . That translator can map elements, perform unit conversion, detect missing data and, in certain cases, find and add the missing data. Another example is the architecture for device management using autonomic computing @cite_32 , where a manager monitors and plans execution using ontologies and a reasoning engine.
@cite_30 @cite_4 @cite_32
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In order to create distributed automation systems, it is required to ensure their interoperability; however, ensuring interoperability between heterogeneous systems (using different communication protocols, data formats, and semantics) is a challenging task. Among many interoperability challenges, this paper addresses issues concerning semantic and data interoperability, namely, it provides a contribution to support the semantic compatibility verification and the generation of translators for XML messages. Translators are generated based on XML-schemas that are annotated with a reference ontology. We base our annotations on an extension of an existing declarative annotation method. In particular, our extension explicitly addresses ambiguities of annotations, schema instance mismatches, and coverage mismatches that frequently occur on Internet-of-things message schemas. We have evaluated our approach based on a set of interaction scenarios from the domain of the Arrowhead Project. A tool prototype that supports the semantic compatibility verification and the generation of XML translators is available at http: gres.uninova.pt tag . Dissertacao para obtencao do Grau de Doutor em Engenharia Electrotecnica e de Computadores Recent advances in the Internet of Things (IoT) has kindled the possibility of a lot of smart industrial systems. With the evolution of these IoT systems in the form of size and complexity, there is a growing need for a high level of interoperability. Autonomic Computing, with the vision of equipping software systems with self-management capabilities, emerges as a potential catalyst to support interoperability. In this paper, we present an approach which exploits Autonomic Computing to facilitate the development of interoperable IoT systems at semantic level. Our approach extends state-of-the-art IoT ontologies as well as Semantic Web Technologies to fit the MAPE-K (Monitor-Analyze-Plan-Execute-Knowledge) paradigm in Autonomic Computing. By using a Smart Home Use Case, the approach is also evaluated under different performance criteria.