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Digital Twins: Modelling Languages Comparison

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Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13811))

Abstract

A digital twin (DT) is a virtual representation of a physical entity that can be used to improve and automate decision-making. For instance, a DT can be used for real-time performance monitoring and optimisation. In this work, we investigate DTs from the point of view of the telecommunication industry (TI). First, we provide an introduction to the main concepts, tools, and applications related to DTs. Then, we clarify the benefits of DTs for the TI. Also, we identify the main challenges hindering widespread adoption of DTs within the TI. Finally, we argue that modelling languages are the key to overcome some of the main challenges. This provides a good starting point for discussion and further research.

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Acknowledgement

This research is funded by Huawei Technologies Ireland.

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Authors and Affiliations

  1. National University of Ireland, Galway, Ireland

    Abdul Wahid, Jiafeng Zhu, Stefano Mauceri, Lei Li & Minghua Liu

Authors
  1. Abdul Wahid
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  2. Jiafeng Zhu
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  3. Stefano Mauceri
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  4. Lei Li
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  5. Minghua Liu
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Corresponding author

Correspondence to Abdul Wahid .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Wahid, A., Zhu, J., Mauceri, S., Li, L., Liu, M. (2023). Digital Twins: Modelling Languages Comparison. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_13

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  • DOI: https://doi.org/10.1007/978-3-031-25891-6_13

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  • Online ISBN: 978-3-031-25891-6

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