Digital twin concepts for linking live sensor data with real-time models
Open accessAbstract
For taking most advantage of live or real-time sensor measurements, data have to be processed by a single or even by a chain of models on the fly, in contrast to earlier offline simulation solutions. This requirement can be best met by concepts developed under the general term “digital twin” (DT). The step from the Internet of Things (IoT) to a full exploitation of DT solutions entails new challenges but also provides new features, which we discuss based on our example DT solution for remote monitoring of fruit during ocean transportation. A crucial challenge is the transformation of models into an updateable format, necessary to keep the physical object and its modelled representation in sync. A basic new feature of DTs is new software solutions for easy and flexible linking of different models through a streaming platform by implementing an event-driven architecture. We demonstrate a solution for controlling model execution during multiple life cycle phases of the fruit as physical object. An evaluation of response times showed that server performance is sufficient to handle more than 100 DT instances per second.
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The interlinking mechanisms include IoT devices, data communication protocols, edge and cloud computing, machine learning algorithms, and feedback control systems.
“The interlinking mechanisms include IoT devices, data communication protocols, edge and cloud computing, machine learning algorithms, and feedback control systems [26][27][28].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [28]
The Streaming platform section describes IoT sensors linked to models through MQTT, Kafka, and publish-subscribe interfaces; the Event-driven architecture section also describes control commands sent back to the physical object. The paper says machine-learning techniques can be applied, but does not establish cloud computing or machine-learning algorithms as components of its interlinking platform. The Platform performance section discusses an embedded computer and a server, which supports part of the computing description but not the full edge-and-cloud claim. (LLM, conf=0.86)
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- Authors
- Jedermann, R., Singh, K., Lang, W., & Mahajan, P.
- Affiliations
- University of Bremen (DE)
- Leibniz Institute for Agricultural Engineering and Bioeconomy (DE)
- Corresponding author
- Jedermann, Reiner
- Venue
- Journal of sensors and sensor systems
- Publisher
- Copernicus GmbH
- Published
- 2023-04-11
- Volume
- 12
- Issue
- 1
- Pages
- 111-121
- ISSN
- 2194-8771 (ISSN-L)
- 2194-878X
- OpenAlex
- W4364365683
- Cited by
- 35
- References
- 17
- Citations by year
- 2026: 15 · 2025: 13 · 2024: 5 · 2023: 2
- FWCI
- 4.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of sensors and sensor systems (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Bundesministerium für Forschung, Technologie und Raumfahrt (01IA10001)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Life Sciences › Agricultural and Biological Sciences › Food Science › Food Supply Chain Traceability
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- digital twin, real-time sensor data, Internet of Things, event-driven architecture, model linkage, maritime transport, model updating, server response time, remote monitoring
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