12:I[386,["/_next/static/immutable/chunks/03nlifkr_qn72.js","/_next/static/immutable/chunks/31arlrx3jt-4c.js","/_next/static/immutable/chunks/3casg-e_-6uft.js","/_next/static/immutable/chunks/3m_oyfvm46wy2.js"],"default"] 13:T402,

Digital Twins (DTs) are evolving into active systems that support prediction, optimisation, and autonomous decision- making, largely through simulation-based analysis of future scenarios. However, existing research offers limited architectural guidance on managing the runtime phases of the simulation life- cycle within DTs, namely triggering, execution, coordination, and control. To address this gap, this paper proposes a catalogue of architectural patterns for simulation management in DTs. These include context-aware triggering, parallel execution, stateful control, result validation and invalidation, continuous calibration using live data, and co-simulation orchestration. The approach is framework-agnostic and provides reusable abstractions for building simulation-centric DT systems. An experimental evaluation of context-aware triggering and parallel execution demonstrates the feasibility of the approach, showing that DTs can efficiently manage external simulations with effective resource utilisation.

14:T744,

Digital Twins (DTs) are a fundamental paradigm for monitoring Cyber–Physical Systems (CPS). However, accurately quantifying the entanglement between a DT and its Physical Twin (PT) remains a critical challenge. In heterogeneous CPS, maintaining this alignment is highly complex: telemetry streams are generated at varying frequencies, reflect differing criticality levels, and face constant disruption from network latency and computational interference. Existing monolithic entanglement metrics, such as ODTE, fail to jointly capture these dynamics; by treating all data loss uniformly and ignoring ordering violations, they mask subtle state desynchronization. To resolve these limitations, we propose the Enhanced Overall Digital Twin Entanglement (E-ODTE) metric. E-ODTE explicitly models the fine-grained structure of CPS telemetry by decomposing the evaluation into a granular, per-property representation. Furthermore, it introduces a data continuity component to penalize out-of-sequence updates and incorporates semantic weighting to encode operational criticality. For rigorous validation, we introduce the Digital Twin Entanglement Benchmark (DTEB), a deterministic reference baseline founded on strict update alignment. E-ODTE is evaluated within a real-time stress-testing framework, mapping native Real-Time Operating System (RTOS) scheduling priorities directly onto its weighting structure. Experimental validation utilizing a Franka Emika Panda robotic setup controlled by a PREEMPT_RT kernel demonstrates that E-ODTE consistently and faithfully tracks the DTEB baseline, even under severe workload preemption. Beyond its effectiveness as a reliable surrogate metric, E-ODTE acts as a powerful diagnostic instrument, successfully exposing latent telemetry buffer anomalies that remain invisible to conventional evaluation approaches.

15:T4f4,

While the Microkernel paradigm is a cornerstone for achieving modularity and extensibility, its application in modern distributed environments is often hampered by a rigid dependency on specific execution contexts. Existing frameworks often entangle business logic with specific communication technologies, necessitating extensive refactoring when transitioning between monolithic and distributed deployments. In this work, we introduce Orbitalis, an open-source framework that extends the Microkernel paradigm to distributed systems. Orbitalis integrates component development, communication, and lifecycle management, enabling seamless execution across both local and distributed environments, enabling applications to operate seamlessly across heterogeneous nodes without requiring a priori knowledge of the deployment topology. At the heart of the framework lies Busline, an asynchronous, protocol-agnostic publish/subscribe backbone that supports dynamic component evolution, replication, and replacement. A DHCP-inspired discovery protocol enables cores and plugins to negotiate, connect, and reconfigure at runtime. Experimental evaluation quantifies the framework’s overhead across various deployment scenarios, demonstrating its practical viability.

16:T4d5,

The evolution of Digital Twin technology led to a new way for innovative applications, including the replication of human beings through Human Digital Twin. Human Digital Twin enables real-time monitoring, simulation, and analysis of individual states and activities, with significant potential in areas such as healthcare, fitness, and manufacturing. However, their development presents unique challenges, particularly in accurately modeling human behavior and ensuring the adaptability of machine learning models. This work addresses these challenges by integrating Human in the Loop methodologies into the Human Digital Twin system. Human in the Loop leverages human to enhance the performance of ML models. Specifically, we explore the application of Active Learning techniques for Human Activity Recognition, a critical component of Human Digital Twin. We propose a Human Digital Twin architecture that incorporates Active Learning to optimize the Human Activity Recognition model. Through this approach, we demonstrate how the integration of Human in the Loop and Active Learning can refine the virtual representation of individuals, advancing the capabilities and real-world applicability of the Human Digital Twin system.

17:T49f,

Digital Twins (DTs) have emerged as a powerful paradigm for modeling, analyzing, and optimizing cyber-physical systems across a wide range of domains. Despite their growing adoption, the application of DTs to real-time systems — characterized by stringent timing constraints — remains a significant and underexplored area of research. Gaining a deeper understanding of the interaction between DTs and real-time systems is essential for extending the applicability of DT technologies to time-critical environments. This paper aims to establish a foundation for systematically addressing the challenges inherent in developing DTs for real-time systems. Specifically, we identify the key characteristics that a real-time system must possess to enable accurate and reliable DT replication. To illustrate these concepts, we present a case study in which a DT is employed to monitor and interact with a simulated robotic arm controlled by a real-time system. Our experimental findings highlight the strategic importance of this research direction and demonstrate the advantages of implementing a structured synchronization mechanism between the DT and its physical counterpart.

18:T4d7,

The increasing complexity of production systems, supply chains, and asset management in the industrial sector demands advanced digital solutions to enhance efficiency, reduce downtime, and support data-driven decision-making. Digital Twin (DT) technology has emerged as a key enabler, allowing real-time monitoring, optimization, and simulation of industrial processes. However, many existing Digital Twin implementations remain highly application-specific, lacking a modular framework for their design, development, and deployment. Such a framework is essential to enable effective interoperability across physical and digital layers, facilitate component reuse, and support the integration of augmentation functions. This paper introduces how DTs implemented with the White Label Digital Twins (WLDT) framework can leverage its general-purpose modular approach to enhance system simplicity and extensibility. DT instances can hence be deployed as individual software components and be easily integrated to build cyber-physical systems. We showcase and validate the proposed framework in a realistic microfactory environment, demonstrating its effectiveness in developing scalable and flexible DT ecosystems of industrial systems.

19:T4b6,

The concept of Industry 5.0 is set to revolutionize the landscape of modern manufacturing, emphasizing human-centricity and elevating the well-being of industry workers as a central tenet of the production process. This paper extends this vision by integrating the dimension of health, focusing not only on the well-being of the operator but also on the detection of their health condition, predicting potential issues, and consequently enhancing their overall welfare. Building upon this enhanced perspective, our work explores the role of Operator Digital Twins (ODTs), which are instrumental in creating a symbiotic relationship between human operators and industrial machinery. ODTs act as digital counterparts, reflecting the physical and cognitive states of operators, thus facilitating real-time monitoring of their capabilities, workload, stress levels, and various health-related parameters. The paper delves into the motivations driving the development of ODTs, abstractly models their functions, and outlines the architectural blueprint. We present an initial ODT prototype with wearable technology and simulated data together with a discussion of the experimental insights and outcomes.

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My research on Digital Twins, real-time systems, and industrial IoT, published at international conferences and journals.

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The adoption of Digital Twins as a paradigm to realise a pervasive softwarisation of the physical world, across different application domains and different network levels (from cloud to edge), calls for software architectures and platforms featuring levels of flexibility beyond the ones provided by the solutions available in the state of the art. This paper introduces an architectural blueprint and implementation for an event-driven notion of Digital Twin, meant to tackle such limitations by promoting flexibility and decoupling. As validation, we present its conceptual application to the challenging context of healthcare emergency missions management and an initial experimental evaluation through open-source Digital Twin solutions.

\n"},{"slug":"2026-orbitalis-distributed-microkernel","title":"Orbitalis: A Distributed Microkernel Framework","authors":["N. Ricciardi","M. Picone","R. Morandi","N. Bicocchi"],"venue":"IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS)","venueShort":"DAIS","year":2026,"type":"conference","link":"https://doi.org/10.1007/978-3-032-27358-1_6","doi":"10.1007/978-3-032-27358-1_6","pdf":"$undefined","contentHtml":"$15"},{"slug":"2025-causality-aware-digital-twins-iiot","title":"Towards intelligent monitoring and control of industrial Internet of Things deployments with causality-aware digital twins","authors":["S. Mariani","M. Martinelli","R. Morandi","A. Barbone","M. Picone"],"venue":"International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)","venueShort":"DCOSS-IoT","year":2025,"type":"conference","link":"https://doi.org/10.1109/DCOSS-IoT65416.2025.00089","doi":"10.1109/DCOSS-IoT65416.2025.00089","pdf":"$undefined","contentHtml":"

Digital Twins are expert-designed digital replicas of the associated Physical Assets, built using top-down software engineering approaches. However, modeling the expected system behavior at design time and understanding it at runtime are both hindered by the cyber-physical complexity and heterogeneous, interdependent variables of IIoT systems. In this paper, we propose to augment Digital Twins in a data-driven manner by learning a causal model of the key variables involved in Physical Asset and Digital Twin interactions. Our methodology applies causal discovery techniques to refine Digital Twin models based on data collected during Physical Assets operation. The resulting \"Causal DT\" enables discovery of hidden causal relations, translation of insights into actionable knowledge, and planning of interventions to steer the system toward desired outcomes. We validate our approach using real hardware that emulates a scaled-down industrial Microfactory with DT-enabled machines.

\n"},{"slug":"2025-harmonizing-physical-digital-twins-lifecycles","title":"Harmonizing physical and digital twins lifecycles","authors":["M. Picone","R. Morandi","A. Barbone","S. Burattini","M. Fogli","N. Bicocchi","C. Giannelli","A. Ricci"],"venue":"IEEE International Conference on Software Architecture Companion (ICSA-C)","venueShort":"ICSA-C","year":2025,"type":"conference","link":"https://doi.org/10.1109/ICSA-C65153.2025.00039","doi":"10.1109/ICSA-C65153.2025.00039","pdf":"$undefined","contentHtml":"

The Digital Twin (DT) lifecycle plays a pivotal role in accurately and reliably representing its associated Physical Twin (PT). Modeling such representation may be complicated since PTs can adapt telemetry frequency and have different value ranges over time in different phases of their lifecycle with respect to their operational context. If not appropriately accounted for, these variations introduce complexity in maintaining synchronization with the DT: the changes may be misinterpreted as failures while only reflecting expected alterations in the PT's behavior in a given phase. The proposed approach provides a foundation for explicitly modeling and harmonizing the phases of PTs and DTs lifecycles, improving decision-making and operational awareness in cyber-physical systems. We present an experimental evaluation within a reference industrial use case, demonstrating the practical benefits of explicitly mirroring the PT lifecycle in the DT.

\n"},{"slug":"2025-human-in-the-loop-active-learning","title":"Human in the loop in digital twins enabled active learning: A proposed architecture","authors":["L. Lamazzi","F. Franco","R. Morandi","M. Picone","L. Bedogni"],"venue":"IEEE International Conference on Software Architecture Companion (ICSA-C)","venueShort":"ICSA-C","year":2025,"type":"conference","link":"https://doi.org/10.1109/ICSA-C65153.2025.00054","doi":"10.1109/ICSA-C65153.2025.00054","pdf":"$undefined","contentHtml":"$16"},{"slug":"2025-modena-automotive-smart-area","title":"From physical to digital: Exploring digital twins within the Modena automotive smart area","authors":["M. Picone","A. Barbone","R. Morandi","E. Rossini","A. Masola","M. Pietri","R. Cavicchioli","C.A. Grazia","M. Mamei","M. Bertogna"],"venue":"IEEE Consumer Communications & Networking Conference (CCNC)","venueShort":"CCNC","year":2025,"type":"conference","link":"https://doi.org/10.1109/CCNC54725.2025.10976222","doi":"10.1109/CCNC54725.2025.10976222","pdf":"$undefined","contentHtml":"

The Modena Automotive Smart Area (MASA) is a cutting-edge testing environment featuring a variety of dynamic physical assets, including smart cameras, roadside units, and connected vehicles. These assets support numerous digital applications, ranging from real-time safety systems to mobility intelligence and 3D visualization of the MASA area. However, the complexity of the physical environment and the diverse needs of these digital applications necessitate a decoupling strategy to ensure efficient operation. This paper presents the design of the MASA Digital Twin, detailing its hierarchical structure, the associated design challenges, and the technological approaches used in its implementation. The MASA Digital Twin serves as a crucial tool for managing the interplay between physical and digital elements, enabling a more structured and adaptable approach to connected mobility and smart city applications.

\n"},{"slug":"2025-on-device-ai-digital-twins","title":"On-device AI and digital twins: A synergistic approach to intelligent cyber-physical systems","authors":["A. Barbone","N. Bicocchi","M. Martinelli","R. Morandi","M. Picone"],"venue":"Future Generation Computer Systems","venueShort":"FGCS","year":2025,"type":"journal","link":"https://doi.org/10.1016/j.future.2025.108068","doi":"10.1016/j.future.2025.108068","pdf":"$undefined","contentHtml":"

This article focuses on the combination of on-device AI and digital twins as a synergistic approach for intelligent cyber-physical systems.

\n"},{"slug":"2025-real-time-systems-digital-twins","title":"Real-Time Systems & Digital Twins: Exploring Integration Challenges and Requirements","authors":["R. Morandi","M. Picone","N. Bicocchi"],"venue":"IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","venueShort":"WETICE","year":2025,"type":"conference","link":"https://doi.org/10.1109/WETICE67341.2025.11091961","doi":"10.1109/WETICE67341.2025.11091961","pdf":"$undefined","contentHtml":"$17"},{"slug":"2025-wldt-modular-engineering","title":"Modular engineering of industrial digital twins: the WLDT approach","authors":["M. Martinelli","A. Barbone","R. Morandi","M. Picone","S. Burattini","A. Ricci"],"venue":"International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)","venueShort":"DCOSS-IoT","year":2025,"type":"conference","link":"https://doi.org/10.1109/DCOSS-IoT65416.2025.00077","doi":"10.1109/DCOSS-IoT65416.2025.00077","pdf":"$undefined","contentHtml":"$18"},{"slug":"2024-operator-digital-twins-industry5","title":"Towards operator digital twins in industry 5.0: Design strategies & experimental evaluation","authors":["M. Picone","R. Morandi","V. Villani","M. Pietri","L. Bedogni"],"venue":"IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","venueShort":"PerCom","year":2024,"type":"conference","link":"https://doi.org/10.1109/PerComWorkshops59983.2024.10502969","doi":"10.1109/PerComWorkshops59983.2024.10502969","pdf":"$undefined","contentHtml":"$19"}]}]]}]