=Paper=
{{Paper
|id=Vol-3727/abstract1
|storemode=property
|title=Digital twins for IoT systems: Exploiting synergies between industry and academia (extended abstract)
|pdfUrl=https://ceur-ws.org/Vol-3727/abstract1.pdf
|volume=Vol-3727
|authors=Daniel Lehner,Ricardo Dunkel
|dblpUrl=https://dblp.org/rec/conf/staf/LehnerD24
}}
==Digital twins for IoT systems: Exploiting synergies between industry and academia (extended abstract)==
https://ceur-ws.org/Vol-3727/abstract1.pdf
Digital twins for IoT systems: exploiting synergies
between industry and academia (extended abstract)
Daniel Lehner1 , Ricardo Dunkel2
1
Johannes Kepler University Linz, Altenberger Strasse 69, 4040 Linz, Austria
1
Open Industry 4.0 Alliance, Willy-Brandt-Platz 6, 81829 Munich, Germany
Context The increasing adoption of IoT systems unleashes more and more potential use
cases to make use of the massive amount of data omitted by these devices. On the other
side, the increasing complexity of IoT systems also poses new challenges on developing and
maintaining such use cases. Software developed to realize these use cases has to process data
from heterogeneous devices and their simulations, and interact with these different devices to
proactively influence their behavior. Digital twins (DTs) are one tool to cope with the resulting
challenges. They provide a uniform, virtual representation of the heterogenuous hardware and
simulations used in IoT systems. When developing software for our IoT systems, we can use
this uniform representation to retrieve live data and interact with devices, instead manually
crafting the connections with each individual device and simulation.
Digital Twins in industry The success of several standardization efforts, together with
the increasing availability of software to support DT development, currently drive the wide-
scale, cross-industry adoption of DTs. The Asset Administration Shell (AAS)1 is a standardized
representation of digital twins maintained by the Industrial Digital Twin Association (IDTA).
The AAS is currently widely adopted in different tools that support the development of DTs, such
as the Bosch Semantic Stack2 , Class.Room3 or twinsphere4 . Additionally, the IDTA also provides
an open-source implementation of the AAS via the Eclipse digital twins project5 The Digital
Twin Consortium (DTC) proposes the Digital Twin Definition Language (DTDL)6 together with
OPC-UA7 as an alternative to the AAS. Whereas the DTDL is supported in commercial tools
such as Azure Digital Twins8 , there are also competing digital twin tools such as the AWS
TwinMaker that use their own, proprietary representation of digital twins.
In previous work, we investigated these industrial digital twin representations and found
that they usually provide a UML class diagram-like syntax to model data emitted by a device [1].
MeSS 2024: 4th International Workshop on MDE for Smart IoT Systems, July 10, 2024, Enschede, Netherlands
$ daniel.lehner@jku.at (D. Lehner); ricardo.dunkel@openindustry4.com (R. Dunkel)
0000-0001-8840-8040 (D. Lehner)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
1
https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/Details_of_the_Asset_Administration_
Shell_Part1_V3.html
2
https://www.bosch-connected-industry.com/de/en/portfolio/bosch-semantic-stack
3
https://www.classing.de/aas/#englishversion
4
https://www.conplement.de/en/twinsphere
5
https://projects.eclipse.org/projects/dt
6
https://github.com/Azure/opendigitaltwins-dtdl/blob/master/DTDL/v3/DTDL.v3.md
7
https://opcconnect.opcfoundation.org/2021/03/iconics-opc-ua-and-digital-twins/
8
https://azure.microsoft.com/products/digital-twins
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
We believe that reusing the knowledge that we developed in the model-driven engineering
(MDE) community over the last decades on modeling such data, e.g., through models@runtime,
can raise the maturity of these industrial initiatives. Furthermore, tools developed in academia
for efficiently developing DTs, e.g., through low-code techniques [2], can benefit the efficient
engineering of DTs in practice.
Digital Twins in the MDE research community Despite their successful adoption in
industry, the above-mentioned DT standards and tools are currently still mostly neglected in
research on MDE of DTs. In the MDE research community, we tend to use our own infrastructure,
such as the Eclipse Modeling Framework9 or MontiCore10 in our work. In the context of DTs,
there are also powerful platforms available which are developed in academia, such as the DTaaS
platform [3]. Even though we have provided numerous outstanding scientific contributions
using these platforms in recent years, the divergence from industrial standardization efforts
makes it hard to transfer these contributions to industrial use cases. As a result, practitioners
are currently often missing the knowledge from the scientific community, using inefficient
approaches to DT development and maintenance, and reinventing the wheel when providing
tools to support the engineering of DTs.
Aligning Industry and Academia To better align the adoption of DTs in industry with the
research efforts of the MDE community in the future, we propose the following action points.
(1) Evaluate research using industrial DT solutions. This makes it easier for practitioners to
transfer the knowledge generated by this research to their specific use cases. It also allows
academics to reuse existing industrial models and use cases provided for these platforms to easily
assess their work in a more realistic setting. (2) Provide transformations between academic
and industrial platforms. By implementing standards in academic platforms, companies can
leverage the full potential of research tooling available in these platforms for their existing
models. Automatically transferring DT solutions developed in academic settings to industrial
platforms allows to easily evaluate research for these industrial settings, as described above.
(3) Provide structured assessments of existing DT tooling. Detailed insights into the current
landscape of DT standards and their implementations, together with existing knowledge and
tools available in academia, can be used by both practitioners and scholars as guidance for
selecting the toolkit they use to efficiently implement DTs in their specific context.
References
[1] J. Pfeiffer, D. Lehner, A. Wortmann, M. Wimmer, Modeling capabilities of digital twin
platforms-old wine in new bottles?, Journal of Object Technology (JOT) 21 (2022) 3–1.
[2] M. Dalibor, M. Heithoff, J. Michael, L. Netz, J. Pfeiffer, B. Rumpe, S. Varga, A. Wortmann,
Generating customized low-code development platforms for digital twins, Journal of
Computer Languages (CoLa) 70 (2022) 101–117.
[3] P. Talasila, C. Gomes, P. H. Mikkelsen, S. G. Arboleda, E. Kamburjan, P. G. Larsen, Digital
Twin as a Service (DTaaS): A Platform for Digital Twin Developers and Users, in: 2023 IEEE
Smart World Congress (SWC), 2023, pp. 1–8.
9
https://eclipse.dev/modeling/emf/
10
https://monticore.github.io/monticore/