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

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 DittoTM. 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.