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Enabling Distributed and Hybrid Digital Twins in the Industry5.0 Cloud Continuum

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Software Engineering and Formal Methods. SEFM 2021 Collocated Workshops (SEFM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13230))

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Abstract

The efficient exploitation of the cloud continuum (interworking of industrial cloud, edge cloud, 5G/B5G base stations, and fog node technologies) is a key factor for future Industry4.0 and beyond applications. For instance, with their ability to work more locally to data sources and to controllable actuators, cloud continuum-based solutions are considered very promising for dynamic manufacturing line control/reconfiguration and for sustainability optimizations (reduction of power and materials consumption). In these contexts, privacy, data sovereignty/control, latency, reliability, and scalability are crucial. This short paper, summarizing some key concepts from the associated keynote speech, aims at offering an overview of the concept of Digital Twins (DTs) for Industry5.0 [1] and at showing some primary and state-of-the-art solution guidelines that are emerging in the field. In particular, in our H2020 IoTwins project [2], we promote, design, implement, and evaluate Industry5.0 DTs that are both distributed because they are able to run over differentiated cloud continuum virtualized resources, also by changing their location dynamically, and hybrid because they combine data-driven machine learning models and simulations based on mathematical-physical modeling of the cyber-physical systems they represent. Distributed and hybrid DTs based on cloud continuum technologies are showing their effectiveness and efficiency in different application cases and scenarios, as demonstrated by our experience of IoTwins testbeds development.

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Correspondence to Paolo Bellavista .

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Bellavista, P. (2022). Enabling Distributed and Hybrid Digital Twins in the Industry5.0 Cloud Continuum. In: Cerone, A., et al. Software Engineering and Formal Methods. SEFM 2021 Collocated Workshops. SEFM 2021. Lecture Notes in Computer Science, vol 13230. Springer, Cham. https://doi.org/10.1007/978-3-031-12429-7_10

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  • DOI: https://doi.org/10.1007/978-3-031-12429-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12428-0

  • Online ISBN: 978-3-031-12429-7

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