A Comparative Study of UniTwin and Established Digital Twin Frameworks for IoT Applications | Zendy

T. M. Haussermann | Zendy; J. Lehmann | Zendy; A. Rache | Zendy; F. Kolb | Zendy; J. Reichwald | Zendy

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A Comparative Study of UniTwin and Established Digital Twin Frameworks for IoT Applications

Author(s) -

T. M. Haussermann,

J. Lehmann,

A. Rache,

F. Kolb,

J. Reichwald

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3619153

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Digital Twins (DTs) have become pivotal in enabling real-time monitoring, simulation, and optimization of complex systems across diverse domains. Despite the growing number of DT frameworks, objective comparisons remain difficult due to inconsistent terminologies and varying abstraction levels. This paper presents a comprehensive comparison between the UniTwin framework and three established DT frameworks: Microsoft’s Azure Digital Twins, White Label Digital Twin, and Eclipse Ditto™. A representative Internet of Things (IoT) device use case serves as the foundation for the evaluation. The dedicated device includes a sensor and actuators in order to assess dynamic control and feedback mechanisms. The comparison comprises two parts: a qualitative analysis and a quantitative analysis. The qualitative analysis assesses each framework’s architectural and functional dimensions, including modularity, scalability, adaptability, reconfigurability, migratability, protocol support, control integration, intelligence localization, DT aggregation, and provision type. The quantitative analysis benchmarks the frameworks using key performance metrics: synchronization latency, data processing throughput, operational availability, scaling efficiency, reconfiguration time, event response time, and resource utilization cost. The UniTwin framework stands out particularly in terms of modularity, scalability, and real-time efficiency and dominates in the dynamic reconfigurability and integration depth of the DT deployment.

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