Digital Twins (DTs) are increasingly adopted in Internet of Things (IoT) systems to provide real-time virtual representations of physical assets. Their growing interconnectivity, however, exposes them to cybersecurity threats that can compromise fidelity and reliability. This work-in-progress study evaluates how representative attacks—Denial of Service, packet loss, and message manipulation—affect DT operational metrics: timeliness, reliability, availability, and consistency. Using a simulation framework based on NS-3, MQTT, and Eclipse Ditto, we emulate realistic DT environments and quantify the resulting degradations. Results show that cyber-attacks leave measurable deviations in these metrics, and that a revised Overall Digital Twin Entanglement (ODTE) score calibrated on normal operation effectively amplifies such anomalies. Metric-based observability thus emerges as a lightweight, non-intrusive approach for early detection and resilience assessment in DT-enabled infrastructures.

This paper surveys the integration of Digital Twins (DT) and Federated Learning (FL) in Industrial IoT (IIoT), highlighting opportunities for real-time monitoring, predictive analytics and distributed intelligence. We review state-of-the-art approaches, identify technological and methodological challenges and discuss how DT and FL can be jointly leveraged to support secure, resilient and adaptive industrial operations. Beyond the classical focus on assets and processes, we extend the analysis to Vulnerable Road Users (VRUs) and show how this concept can be transposed into industrial contexts, where workers and operators act as VRUs inside plants, warehouses, and construction sites. In this perspective, DTs combined with FL can provide simulation-driven insights for worker-centric safety, enabling risk prediction, proactive protection and safer coordination between humans, machines and autonomous systems.

The integration of Digital Twins (DTs) and Blockchain technologies represents a promising direction for building trustworthy, auditable, and interoperable industrial systems. Yet, most existing approaches focus on static identity anchoring rather than on the continuous certification of DT state evolution. This paper proposes a novel framework for the dynamic certification of DTs in Industrial Internet of Things (IIoT) environments, combining a lightweight, permissioned blockchain with adaptive batching and ordering mechanisms. The proposed architecture connects physical assets, DT models and a blockchain-based certification layer through three coordinated components: a DT Instance Manager, a Smart Contract for state hashing and metadata storage, and Verifier Nodes for crosspeer consistency checking. A complete experimental campaign evaluates certification latency, drop rate, commit ratio, and energy overhead under realistic IIoT network conditions. Results demonstrate sub–30ms end-to-end latency for full IIoT emulation and up to 60% energy savings with micro-batching, confirming the feasibility of scalable and energy-aware DT certification across the edge–cloud continuum.

This paper presents the Smart City Architecture (SCA), a middleware system built upon the MQTT (Message Queuing Telemetry Transport) protocol and developed within the MASA (Modena Automotive Smart Area) initiative. SCA enables intelligent urban applications by facilitating seamless and scalable communication among heterogeneous entities, including assets, services, and observers. Its structured, topic-based messaging layer supports efficient telemetry exchange, event-driven processing, and dynamic service interaction. The capabilities of SCA are exemplified through two real-world services—Vulnerable Road User (VRU) and GeoPerception—which provide real-time risk detection and localized situational awareness in smart city scenarios.

In this research, we propose automating network management through data-driven intelligence, with a particular focus on anomalies and network traffic during specific events or periods. We analyze a large dataset collected by Orange mobile network operator in France with the goal of forecasting mobile demand for different classes of services. To model the underlying network infrastructure, we introduce a model for the underlying network based on a hierarchy of virtualization layers and slices. Building on this model, we propose algorithms to optimize the resources allocated to network slices and traffic distribution within the operator’s network. Network performance is evaluated as the fraction of time the mobile traffic is within the capacity of the network. Our results demonstrate that dynamic reallocation of resources among slices, and dynamic load balancing (traffic shaping) between nodes notably improves network performance. These results provide insights into critical aspects related to future 5G network management.

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.

Effective demand forecasting is crucial for reducing food waste. However, data privacy concerns often hinder collaboration among retailers, limiting the potential for improved predictive accuracy. In this study, we explore the application of Federated Learning (FL) in Sustainable Supply Chain Management (SSCM), with a focus on the grocery retail sector dealing with perishable goods. We develop a baseline predictive model for demand forecasting and waste assessment in an isolated retailer scenario. Subsequently, we introduce a Blockchain-based FL model, trained collaboratively across multiple retailers without direct data sharing. Our preliminary results show that FL models have performance almost equivalent to the ideal setting in which parties share data with each other, and are notably superior to models built by individual parties without sharing data, cutting waste and boosting efficiency.

The concept of Fluid Computing entails a dynamic resource allocation approach, enabling seamless task migration between computing nodes. This paper investigates the fusion of Fluid Computing principles with the Internet of Things (IoT) and introduces the concept of Fluid Digital Twins (FDTs) i.e. cyber-physical entities that bridge the complexities of this integration. FDTs serve as intermediaries, overseeing fluid task migration, optimizing resource use, and simplifying interactions for external digital applications. The paper delves into challenges arising from this fusion, including limited IoT device capabilities, fragmentation, and the necessity of an intelligent intermediary layer. This research article models and presents FDT mechanics, features a prototype with experimental evaluation and concludes by discussing findings and potential future research directions.

This paper proposes a groundbreaking architecture that reimagines Industry 5.0, emphasizing human-centric technological integration via the Web of Things (WoT) standard. Our approach innovatively digitizes human operators and machinery, creating a responsive industrial ecosystem attentive to real-time human conditions. Central to this is the Operator Thing (OT), a digital replica representing the human operator's status and needs. This system not only recognizes operator stress and discomfort but intelligently adjusts, ensuring optimal human-machine synergy. Our methodology extends to redefining operational parameters and tasks in response to human states, balancing well-being with production efficiency. The ultimate goal is a transformative, adaptive, and empathetic Industry 5.0 environment, validated through rigorous interdisciplinary evaluation.

The combination of Fluid Computing with the Internet of Things has enabled dynamic orchestration of tasks and functionalities, enhancing performance and responsiveness. Integrating Digital Twins has bridged the cyber-physical gap and the recent introduction of the concept of Fluid Digital Twins (FDTs) opened to the dynamic reconfiguration of functions and simplified augmentation of physical assets’ capabilities. However, introducing new functions or updating existing ones to improve performance or fix bugs poses significant challenges in validating, testing, and deploying these changes in a production environment without disrupting operations. This paper proposes and experimental evaluate an FDT approach for dynamic function management by spawning twin replicas for testing and automatically synchronizing data between production and validation instances.

This paper makes a compelling case for the adoption of the recently proposed Data Mesh architecture within IoT-Edge-Cloud Continuum scenarios, particularly in the context of Intelligent Transportation Systems and Data-driven Mobility Services. Unlike centralized cloud-based approaches, based on data warehouses/lakes connected with ETL (Extract, Transform, and Load) pipelines, Data Mesh promotes a decentralized data ownership model which brings several advantages in addressing open challenges in IoT-Edge-Cloud Continuum scenarios. First, we present an overview of the Data Mesh concepts, and how they advance the state of the art in data management architectures. Secondly, we discuss how their adoption might ease the development of IoT -Edge-Cloud applications in terms of: (i) hiding the heterogeneity of the IoT Layer, (ii) mitigating latency by enabling full domain migrations, and (iii) promoting the adoption of AI techniques, such as MLOps and Federated Learning at the edge of the net-work. Finally, we provide practical guidelines for implementing such an architecture to enhance the safety of pedestrians and vulnerable users, based on our experience with the Modena Automotive Smart Area.

This paper addresses the enduring issue of spam, scams, and robocalls within the telecommunications sector. The diffusion of generative AI technologies has escalated these challenges, as advancements in natural language processing and related tools enhance the sophistication of scams, facilitating the implementation of convincing social engineering attacks. The economic impact of these nefarious activities is significant, as evidenced by the vast number of spam calls and robocalls generated every day that lead to significant financial losses. Although technologies such as blocklists, STIR/SHAKEN, and Caller ID Verification methods are being implemented, the adoption of these solutions by phone companies remains slow due to industry barriers and varied regulatory frameworks. This paper evaluates the effectiveness of current anti-spam countermeasures and highlights the practical limits of these solutions, underscoring the need for improved decision-making tools.

Enhancing the safety of Vulnerable Road Users (VRUs) poses a significant research challenge in the context of connected mobility and a plethora of technological opportunities trying to balance efficiency and widespread applicability. This paper presents a VRUs’ safety application focused on applying 5G Multi-Access Edge Computing (MEC), commercial mobile devices, public cellular networks, and data fusion between vehicle positioning and city camera infrastructure. The application showcases the designed system and its experimental evaluation in the Modena Automotive Smart Area (MASA) through an experimental 5G MEC infrastructure to build a secure and efficient connected mobility environment.

This paper explores the integration of Digital Twins (DTs) in Industry 4.0 and 5.0, highlighting their role in enhancing intelligent, collaborative industrial ecosystems. By representing processes, machinery, operators, and products, DTs enable comprehensive life-cycle support and improved shop-floor operations. Intelligent applications and services can harness DTs as structured and interoperable virtual replicas, entrusted with the responsibility of interfacing with the physical world and facilitating access and mediation of interactions therein. Our study proposes structured DT modeling in industrial ecosystems to demonstrate how DTs enable an effective decoupling of responsibilities and capabilities supporting precise monitoring and data synthesis, optimizing production workflows and maintenance. We discuss DTs’ potential in industrial quality control, highlighting efficiency gains and operational improvements in electric motor production through case studies.

Enhancing the safety of Vulnerable Road Users (VRUs) poses a significant research challenge in the context of connected mobility and a plethora of technological opportunities trying to balance efficiency and widespread applicability. This paper presents a demo focused on applying 5G Multi-Access Edge Computing (MEC) to address this challenge through the combination of commercial mobile devices, public cellular networks, and data fusion between vehicle positioning and city camera infrastructure. The demo showcases the designed system and its experimental evaluation in the Modena Automotive Smart Area (MASA) through the 5G MEC infrastructure of Telecom Italia (TIM) with the aim to build a secure and efficient connected mobility environment.

Large scale cloud-based services are built upon a multitude of hardware and software resources, disseminated in one or multiple data centers.Controlling and managing these resources requires the integration of several pieces of software that may yield a representative view of the data center status.Today's both closed and open-source monitoring solutions fail in different ways, including the lack of scalability, scarce representativity of global state conditions, inability in guaranteeing persistence in service delivery, and the impossibility of monitoring multi-tenant applications.In this paper, we present a novel monitoring architecture that addresses the aforementioned issues.It integrates a hierarchical scheme to monitor the resources in a cluster with a distributed hash table (DHT) to broadcast system state information among different monitors.This architecture strives to obtain high scalability, effectiveness and resilience, as well as the possibility of monitoring services spanning across different clusters or even different data centers of the cloud provider.We evaluate the scalability of the proposed architecture through a bottleneck analysis achieved by experimental results.

In large scale systems, real-time monitoring of hardware and software resources is a crucial means for any management purpose. In architectures consisting of thousands of servers and hundreds of thousands of component resources, the amount of data monitored at high sampling frequencies represents an overhead on system performance and communication, while reducing sampling may cause quality degradation. We present a real-time adaptive algorithm for scalable data monitoring that is able to adapt the frequency of sampling and data updating for a twofold goal: to minimize computational and communication costs, to guarantee that reduced samples do not affect the accuracy of information about resources. Experiments carried out on heterogeneous data traces referring to synthetic and real environments confirm that the proposed adaptive approach reduces utilization and communication overhead without penalizing the quality of data with respect to existing monitoring algorithms.