Industry 5.0 emphasizes human-centric manufacturing, where the operator needs to inform system design. While Operator Digital Twins monitor operator states through biometric data, existing approaches have not exploited this information to adapt industrial Human-Machine Interfaces (HMIs), which remain predominantly static and uniform. This paper proposes an architecture that extends Human Digital Twin systems with LLM-driven personalization for web-based HMIs. The framework collects operator-specific data through a mobile application integrating Health Connect and manual inputs. A Large Language Model interprets operator profiles containing biometric signals, permanent characteristics, and preferences, generating customized CSS stylesheets and configuration parameters that adapt visual properties and interaction modalities while preserving safety-critical elements. The architecture’s applicability is illustrated through diverse conceptual scenarios, such as visual impairments, protective equipment usage, elevated stress states, and ergonomic preferences. This demonstrates how a consistent operator profile format could drive varied interface adaptations to meet different operator needs.

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.

The convergence of Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT) is reshaping Cyber-Physical Systems (CPSs), enabling intelligent automation, real-time decision-making, and adaptive control across diverse industrial domains. A key enabler of this transformation is On-Device AI, where training and inference occur directly on edge devices. While deploying AI models in constrained environments presents challenges-such as limited computational resources and hardware heterogeneity-the benefits of reduced latency, improved energy efficiency, and enhanced data privacy make this approach essential for next-generation CPSs. However, scaling and managing AI-enabled CPSs introduces new complexities, including efficient coordination among sensing, computation, and actuation, as well as the need for dynamic model adaptation in resource-constrained settings. Addressing these challenges requires architectural solutions that support distributed intelligence while maintaining system responsiveness and robustness. This paper investigates the use of Digital Twins (DTs) as a cyber-physical abstraction layer that enhances the deployment and management of On-Device AI. By maintaining synchronized, high-level digital representations of physical assets, DTs facilitate local AI execution, optimize resource allocation, and support low-latency decision-making. We validate our approach through experimental evaluation in a microfactory testbed, demonstrating how DTs improve lifecycle management, operational efficiency, and system adaptability in constrained environments. The results highlight the potential of DTs as a foundational technology for scalable, secure, and efficient AI-driven CPSs, offering valuable insights into the deployment of intelligent systems in heterogeneous industrial ecosystems.

Context: The adoption of Artificial Intelligence (AI) in industrial production systems has raised significant expectations for increased efficiency and innovation. Nevertheless, challenges such as the distributed nature of industrial operations, the heterogeneity of physical devices, and the complexity of real-world processes continue to hinder AI integration. Digital Twins (DTs) have emerged as a promising abstraction to decouple physical complexity from digital representations, facilitating more effective system management. Objective: This work investigates how AI can be systematically integrated with DTs in industrial contexts. The goal is to identify and characterize a set of interaction patterns that leverage the complementary strengths of AI and DTs to enhance industrial intelligence and performance. Methods: Drawing on a structured view of how responsibilities can be shared between AI technologies and DT-enabled shop floors, the paper defines four interaction patterns—AI Observing DTs, AI Advising DTs, AI Controlling DTs, and AI Embedded in DT. Each pattern is analyzed in terms of its roles, data and control flows, and typical application scenarios, and is illustrated on a DT-enabled physical micro-factory that reproduces realistic production conditions. Results: The four patterns show how different placements and responsibilities of AI components with respect to DT layers impact modularity, reuse of AI models, maintainability, and integration with legacy industrial systems. The micro-factory illustration highlights how the patterns can support practical use cases, including root-cause analysis of performance degradation, machine-level health monitoring, and AI-based production scheduling. Conclusion: Structuring AI–DT integration around interaction patterns provides a concrete way to bridge the gap between conceptual opportunities and operational industrial systems. The proposed patterns offer a reusable design vocabulary for positioning AI with respect to DT layers in cyber–physical production systems, and for reasoning about the architectural trade-offs of alternative integration strategies.

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 increasing complexity of Cyber-Physical Systems (CPS), particularly in the industrial domain, has amplified the challenges associated with the effective integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. Fragmentation across IoT and IIoT technologies, manifested through diverse communication protocols, data formats and device capabilities, creates a substantial gap between low-level physical layers and high-level intelligent functionalities. Recently, Digital Twin (DT) technology has emerged as a promising solution, offering structured, interoperable and semantically rich digital representations of physical assets. Current approaches are often siloed and tightly coupled, limiting scalability and reuse of AI functionalities. This work proposes a modular and interoperable solution that enables seamless AI pipeline integration into CPS by minimizing configuration and decoupling the roles of DTs and AI components. We introduce the concept of Zero Configuration (ZeroConf) AI pipelines, where DTs orchestrate data management and intelligent augmentation. The approach is demonstrated in a MicroFactory scenario, showing support for concurrent ML models and dynamic data processing, effectively accelerating the deployment of intelligent services in complex industrial settings.

Industry 5.0 redefines industrial automation by emphasizing human-centricity, sustainability, and resilience. Within this paradigm, the Operator Digital Twin (ODT) has recently emerged as a digital counterpart of the human worker, integrating biometric, contextual, and behavioral data to enable adaptive interactions with machines. However, fully exploiting the potential of ODTs introduces significant challenges, including the dynamic management of AI functionalities, privacy preservation, and context-aware deployment across heterogeneous computing devices. This paper proposes a dynamic, privacy-aware framework for managing AI capabilities within ODTs. The approach supports real-time adaptation of machine learning models based on the operator’s condition and the computational constraints of devices such as smartphones and embedded systems. By leveraging edge processing, the architecture minimizes the exposure of sensitive biometric data while ensuring reliable functionality and compliance with privacy regulations. The framework is validated in a prototypical industrial testbed using real ML models and heterogeneous hardware, demonstrating its effectiveness in enabling context-driven and secure AI orchestration in ODT-enabled industrial environments.

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.

Industry 5.0 embodies the vision for the future of factories, emphasizing the importance of sustainable industrialization and the role of industry in society, through the key concept of placing the well-being of workers at the center of the production process. Building upon this vision, we propose a new paradigm to design human-centric industrial applications. To this end, we exploit Digital Twin (DT) technology to build a digital replica for each entity on the shop floor and support and augment interaction among workers and machines. While so far DTs in automation have been proposed for machine digitalization, the core element of the proposed approach is the Operator Digital Twin (ODT). In this scenario, biometrics allows to build a reliable model of those operator’s characteristics that are relevant in working contexts. Biometric traits are measured and processed to detect physical, emotional, and mental conditions, which are used to define the operator’s state. Perspectively, this allows to manage and monitor production and processes in an operator-in-the-loop manner, where not only is the operator aware of the state of the plant, but also any technological agent in the plant acts and reacts according to the operator’s needs and conditions. In this paper, we define the modeling of the envisioned ecosystem, present the designed DT’s blue-print architecture, discuss its implementation in relevant application scenarios, and report an example of implementation in a collaborative robotics scenario.Note to Practitioners—This paper was motivated by the problem of designing human-cyber-physical systems, where production processes are managed by concurrently taking into account operators, machines and plant status. This answers the needs of the novel Industry 5.0 paradigm, which aims to enhance social sustainability of modern factories. To this end, we propose an architecture based on digital twins that allows to develop a digital layer, detached from the physical one, where the plant can be monitored and managed. This allows the creation of a digital ecosystem where machines, operators, and the interactions among them are represented, augmented, and managed. We discuss how the proposed architecture can be applied to three relevant scenarios: remote training and maintenance, line operation and line supervision. Moreover, the implementation in a collaborative robotics scenario is presented, to provide an example of the proposed architecture can be implemented in industrial scenarios.

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.

A defining feature of a Digital Twin (DT) is its level of ”entanglement”: the degree of strength to which the twin is interconnected with its physical counterpart. Despite its importance, this characteristic has not been yet fully investigated, and its impact on applications' design is underestimated. In this paper, we define the concept of “Degree of Entanglement” (DoE), which provides an operational model for assessing the strength of the entanglement between a DT and its physical counterpart. We also propose an interoperable representation of DoE within the Web of Things (WoT) framework, which enables DT-driven applications to dynamically adapt to changes in the physical environment. We evaluate our proposal using two realistic use cases, demonstrating the practical utility of DoE in supporting, for instance, context-awareness decisions and adaptiveness.

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.

The livestock industry in the Po Valley is economically significant but generates challenging by-products, notably pig manure. Disposal typically involves spreading manure on agricultural land, requiring daily transportation by hundreds of tankers and leading to soil, water, and air pollution. This method incurs high costs and poses monitoring challenges for government agencies. This research develops a simulated environment that mimics waste dissemination activities in the Po Valley to support the development of an Internet of Things (IoT) architecture for cost-effective environmental monitoring. A custom-built emulator was designed to map driver behaviors, identify limitations, and foresee challenges before real-world implementation, enabling scalable completely renovated and distributed monitoring tools.

Modern industrial systems, characterised by distributed and fragmented equipment, present challenges due to their inherent heterogeneity and complexity. This should not impact the stakeholders' business logic, who are more concerned with the information itself rather than how it is collected or processed. Recently, Digital Twins - software copies of physical assets and systems - emerged as a pivotal strategy to bridge the cyber-physical world into an effective digital layer decoupling applications from the management and interaction with physical assets. Fostering this vision, we propose a structured industrial Digital Twins ecosystem exploiting twin relationships and hier-archies to build a digitalised replica of the whole manufacturing system structure enabling a simplified navigation and interaction with the physical world and the data it generates. To support the depicted visions, a fully functioning prototype has been implemented and evaluated in an experimental scenario.

The rise of Digital Twin (DT) technology has revolutionized various sectors, offering simulation, monitoring, and optimization capabilities for complex systems. However, the rapid expansion of DT platforms, coupled with the need to deploy twins across the edge-to-cloud computing spectrum, has led to significant fragmentation and management challenges. These complexities should not hinder the application layer’s goal of achieving interoperability and integrated management of DT instances. This paper introduces the Digital Twin Continuum (DTC) as a unified framework to address the challenges of DT coordination, hiding platform intricacies, while tackling the orchestration of communication and computing resources in the edge-to-cloud continuum. In this study, we define the concept of DTC, outlining its modeling principles, core functionalities and architectural components. We conduct an initial experimental evaluation of a DTC prototype in a realistic automotive use case, targeting two reference DT runtimes.

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.

Intensive livestock production, notably in the Po Valley (Italy), poses significant environmental challenges due to the high concentration of nitrate-rich effluents. These effluents, generated by millions of animals, are directly disposed of onto fertile soil as a common irrigation technique. Unfortunately, this leads to widespread contamination, compromising essential resources like water, soil, and air. This paper focuses on analyzing and modeling the main phases associated with livestock waste management and designing and prototyping an Internet of Things (IoT) enabled architecture for mission management and facilitating real-time monitoring of operations. The possibility to build a comprehensive representation of involved processes and data allows for filling a crucial gap for scientific, commercial, and policy purposes by enabling precise reporting to control agencies and supporting sustainable interventions to mitigate environmental impact.

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.

R4.2 In the Industry 4.0 era, Digital Twins (DTs) serve as virtual representations of physical objects and intermediaries between the physical world and the digital realm. DTs require proper modeling, design, and development to ensure their seamless integration along the cloud-to-edge continuum. In particular, this work introduces a microservices-based and serverless-ready model for DTs, laying the foundation for cost-effective DT deployment and orchestration. The joint adoption of microservices and serverless computing offers significant potential to address various challenges, including accommodating variable application requirements, managing load imbalances, and mitigating network faults. The proposed DT model has been implemented in different flavors: two serverless implementations—one that relies on a serverless framework of a cloud provider and one running at the edge on-premises—and a microservices one. These implementations have been experimentally evaluated with particular emphasis on the quality of cyber–physical entanglement. This work not only discusses the advantages and drawbacks of different implementations from a qualitative perspective but also quantitatively evaluates them with the in-the-field collection of experimental performance results. Notably, we report that a serverless implementation typically performs an order of magnitude worse than a microservices one in terms of entanglement, i.e., hundreds vs. tens of milliseconds.

The development of the Digital Twin (DT) approach is tilting research from initial approaches that aim at promoting early adoption to sophisticated attempts to develop, deploy, and maintain applications based on DTs. In this context, we propose a highly dynamic and distributed ecosystem where containerized DTs co-evolve with an orchestration middleware. DTs provide digitalized representations of the targeted physical systems, while the orchestration middleware monitors and re-configures the deployed DTs in light of application constraints, available resources, and the quality of cyber-physical entanglement. First, we lay out the reference scenario. Then, we discuss the limitations of current approaches and identify a set of requirements that shape both DTs and the orchestration middleware. Subsequently, we describe a blueprint architecture that meets those requirements. Finally, we report empirical evidence on both the feasibility and the effectiveness of a proof-of-concept implementation of the proposed ecosystem.

Digital Twins (DTs) have recently emerged as a valuable approach for modeling, monitoring, and controlling physical objects in Industrial Internet of Things applications. Measuring the quality of entanglement between the digital and physical counterparts plays a crucial role in the adoption of DTs. In this paper, we propose a concise yet expressive metric for representing the quality of entanglement, namely Overall Digital Twin Entanglement (ODTE), based on two key factors: timeliness and completeness. Furthermore, the paper presents the development of our industrial testbed implemented on top of Kubernetes, where we show practical applications of the proposed ODTE metric by highlighting and discussing its benefits in realistic use cases.

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.

Industry 5.0 promotes the development of human-centered industrial operations fueled by a fresh wave of disruptive technologies that encourage synergistic human-machine integration. Its focus is on understanding how human cognition contributes to a more secure and harmonious coexistence between humans and machines in industrial scenarios, employing solutions that prioritize fundamental worker demands while preserving or enhancing industrial productivity. In this context, the ability to assess fatigue objectively is crucial for occupational health and safety because it can reduce cognitive and motor function, ultimately lowering productivity and raising the risk of harm to human operators. To this end, wearable systems provide a promising solution for continuous, non-intrusive, and long-term monitoring of biological signals for fatigue detection. However, the adoption of these devices presents unique challenges, such as inter-individual variability that renders traditional one-size-fits-all machine learning models unsuitable. This paper provides an analysis of the current state-of-the-art for wearable device monitoring, including ongoing issues and current knowledge gaps. In addition, an experimental analysis is presented, employing a pattern discovery pipeline based on unsupervised learning on a real-world dataset. Our analysis provides experimental evidence of the limitations of one of the classical approaches to fatigue assessment, thus highlighting the need for more advanced models.

IoT systems based on Digital Twins (DTs) — virtual copies of physical objects and systems — can be very effective to enable data-driven services and promote better control and decisions, in particular by exploiting distributed approaches where cloud and edge computing cooperate effectively. In this context, digital twins deployed on the edge represents a new strategic element to design a new wave of distributed cyber-physical applications. Existing approaches are generally focused on fragmented and domain-specific monolithic solutions and are mainly associated to model-driven, simulative or descriptive visions. The idea of extending the DTs role to support last-mile digitalization and interoperability through a set of general purpose and well-defined properties and capabilities is still underinvestigated. In this paper, we present the novel Edge Digital Twins (EDT) architectural model and its implementation, enabling the lightweight replication of physical devices providing an efficient digital abstraction layer to support the autonomous and standard collaboration of things and services. We model the core capabilities with respect to the recent definition of the state of the art, present the software architecture and a prototype implementation. Extensive experimental analysis shows the obtained performance in multiple IoT application contexts and compares them with that of state-of-the-art approaches.

Smart homes usually consist of smart objects (SOs) with limited resources and capabilities, and therefore constrain the complexity of applications that can be performed on them. In particular, updating smart objects within a smart home is a challenging undertaking, as seemingly insignificant updates affect the longevity of the deployment if they cause previously established dependencies to break. In this paper, we propose an architecture that we call Longevity Digital Twins (LDTs) as a strategic counterpart of SOs, aimed at running at the edge, as local to the smart home as possible. With this architecture, the capabilities of a SO can be virtually enhanced to support the software update process in the smart home. In this context, foresighted software management requires both a local capability to describe involved functionalities together with awareness about existing dependencies in this distributed system. Using a simulated smart home environment, we first measure the impact of conventional update strategies and then present the noticeable improvement that LDTs offer to this problem. Going further, we present the analysis of a real-world use case that showcases the potential of LDTs on how it could not only prevent the installation of breaking updates but also extend a SOs capabilities and its overall longevity.

In recent years, digital twins have been pervading different application domains—from manufacturing to healthcare—as an approach for virtualising different kinds of physical entities (things, products, machines). The dominant view developed in the literature so far is about the virtualisation of individual physical assets in a closed-system perspective. In this article, we introduce and explore a broader perspective that we call Web of Digital Twins (WoDT), in which the digital twin paradigm is exploited for the pervasive softwarisation of possibly large-scale interrelated physical realities. A WoDT can be conceived as an open, distributed and dynamic ecosystem of connected digital twins, functioning as an interoperable service-oriented layer for applications running on top, especially smart applications and multiagent systems. The article introduces an abstract model and architecture aimed to capture key aspects of the idea not bound to any specific application domains or implementing technologies and discusses their adoption in engineering real-world systems. To this purpose, two concrete case studies are considered, in the context of healthcare and smart mobility. Finally, the article includes a discussion of a selected set of research directions.

Digital Twins (DTs) are becoming a reference design abstraction for many Internet of Things (IoT) application scenarios. Also, data processing is shifting to a decentralised setting leveraging the edge computing paradigm to move computation closer to the physical devices. In this context, Multi-access Edge Computing (MEC) technologies on 5G cellular networks are redefining the IoT networking infrastructure by enabling ultra low latency, and reliable and responsive connectivity. However, evaluation of the MEC architecture from the application developer standpoint is currently missing from literature, as well as an assessment of performance while adopting DT on top of MEC. Therefore, this paper reports on a MEC implementation based on OpenNESS toolkit, in the context of DT-based mobility, and an evaluation of its service-level performance.

The application of Internet of Things (IoT) within industrial environments is fostering the adoption of the digital twin (DT) approach, applied at the edge of the network to handle heterogeneity stemming from siloed application management solutions and from protocols originated by different manufacturing tools and enterprise services. In this challenging context, network heterogeneity also represents a critical element that can significantly limit the design and deployment of DT-oriented applications. This article proposes the Application-driven digital twin networking middleware with the twofold objective of: 1) Simplifying the interaction among heterogeneous devices by allowing DTs to exploit IP-based protocols instead of specialized industrial ones and to enhance packet content expressiveness, by enriching data via well-defined standards. 2) Dynamically managing network resources in edge industrial environments, applying software defined networking to exploit the communication mechanisms most suitable to application requirements, ranging from native IP to more articulated based on packet content.

Summary The widespread and ubiquitous availability of Internet access enables the collective sharing of huge amount of data generated by heterogeneous sources. For example, the information, which will be exchanged among entities (sensors, people, and services) of future smart cities to enhance the security and lifestyle of their citizens, poses the challenging question of how this information can be efficiently and effectively maintained across the city. In this article, we propose a decentralized approach, based on the distributed geographic table (DGT) overlay scheme, which exploits geo‐referenced information about nodes to achieve efficient data management. After recalling DGT main concepts, we illustrate the possible node types and how information can be published and retrieved within the network. To cope with the unavoidable node failures and disconnections, our approach leverages upon randomized network coding to increase the robustness of publish/retrieval operations. Evaluation is carried out through an extensive simulation analysis for a realistic urban scenario using the metrics of efficiency in data publication/search, resource availability, and storage occupancy requirements. Results show the approach effectiveness for large‐scale sharing of geo‐referenced information and tradeoffs between redundancy overhead and resource availability. A few results obtained with a preliminary DGT implementation are also presented in the paper. Copyright © 2014 John Wiley & Sons, Ltd.

One envisioned distinctive feature of smart cities is the interconnection among mobile users and vehicles, to support the fulfillment of location-based services. This can be obtained with centralized architectures, and with all the problems of scalability and robustness that such a solution involves. On the other hand, a more complex but more reliable, completely distributed approach can overcome this kind of problems. In this paper, we present the Adaptive Distributed Geographic Table (ADGT), a peer-to-peer overlay scheme suitable for the development of location-based services. In particular, the ADGT allows to efficiently retrieve peers or resources, to broadcast messages within any geographical region, and to be automatically notified about any type of information around any geographical location, following the publish/subscribe model. What mainly differentiates the ADGT from the other solutions in literature is the adaptivity of the overlay's topology to peers' mobility. Actually, the ADGT has the capability to adapt the neighborhood of each mobile peer depending on speed and direction. We have evaluated the ADGT by simulating different scenarios, and the results show that it acts well, ensuring high quality of messages dissemination and low cost in terms of data usage.

The Internet of Things (IoT) aims at connecting billions of devices in an Internet-like structure. This gigantic information exchange enables new opportunities and new forms of interactions among things and people. A crucial enabler of robust applications and easy smart objects' deployment is the availability of mechanisms that minimize (ideally, cancel) the need for external human intervention for configuration and maintenance of deployed objects. These mechanisms must also be scalable, since the number of deployed objects is expected to constantly grow in the next years. In this work, we propose a scalable and self-configuring peer-to-peer (P2P)-based architecture for large-scale IoT networks, aiming at providing automated service and resource discovery mechanisms, which require no human intervention for their configuration. In particular, we focus on both local and global service discovery (SD), showing how the proposed architecture allows the local and global mechanisms to successfully interact, while keeping their mutual independence (from an operational viewpoint). The effectiveness of the proposed architecture is confirmed by experimental results obtained through a real-world deployment.

Recent years have seen the relentless market explosion of mobile devices, whose ever increasing capabilities (in terms of computational power, networking, and sensing) make them attractive to an endless number of connected applications and services (especially in business and infotainment domains) which can be fully experienced in mobility. This huge market growth naturally involves a constant increase of mobile internet accesses with a consequent overload for mobile operators and potentially a reduced performance for mobile users. In this scenario and during last years, the research field of data offloading and Vertical HandOver (VHO) has gained a significant attention by service providers to start offloading mobile data traffic from 3G/4G networks to WiFi networks. The reduction of the load on cellular network is instrumental to allow the user to be Always Best Connected (ABC) with limited costs. In this paper, we present and analyze the performance of a real VHO-enabled ABC mobile application for Android Platform. The application has been tested in a national trial involving several users all over Italy, commercial (Guglielmo Srl) and private WiFi networks and cellular networks of the main Italian mobile operators for more than a month and 150.000 distinct logs collected during the evaluation.

The main reason of wasting time in search of free parking spaces is the lack of information, in particular for open/roadside parking availability. Various ICT-based solutions have been proposed to solve this issue, but still suffering from limited integration among each other and with external online services, such as touristic information services. In this paper we illustrate a modular, service-oriented smart parking system, which includes web applications for parking operators and end users, as well as mobile applications for end users and parking controllers. The proposed system allows (1) operators to draw parking areas and define their details, (2) end users to be guided to the most suitable parking area, with also the indication of points of interest, and (3) controllers to monitor all vehicles that have been parked in their area. Another important feature is the possibility for end users to share their knowledge about parking occupancy, which is very useful when a parking area is not provided with precise availability counters. The smart parking system has been successfully evaluated in our Campus.

Mobile Cloud Computing (MCC) is a model for transparent elastic augmentation of mobile device capabilities via ubiquitous wireless access to cloud storage and computing resources. The main purpose of MCC is to exploit the context-aware dynamic offload of demanding mobile applications to the Cloud, in order to improve their performance while saving energy and extending battery lifetime of devices. In this paper we extend a pre-existing MCC taxonomy, and we illustrate how the autonomic approach enabled by the open source NAM4J middleware with code migration support can effectively address MCC requirements. We recall the architecture of NAM4J and show its capabilities in the context of an Ambient Intelligence (AmI) MCC application for the Android platform.

Location-Based Services (LBSs) are information or entertainment services where the request, the response and served contents depend on the physical position of the requesting device. LBS are frequently used to implement Traffic Information Systems (TIS), which are increasingly based on user-contributed information. In this paper we present the first prototype of our solution for a decentralized, smartphone-based TIS, called D4V, that allows each participant vehicle to efficiently discover data or services located near any chosen geographic position. The experimental evaluation has shown that D4V could be effectively used on the road to reduce the number of drivers involved in traffic jams, as well as to disseminate alert messages about potentially dangerous road stretches, thus allowing drivers to reduce risks and nuisances along their paths.

To cope with millions users becoming increasingly connected to Internet on the move, location-based services may be better supported by decentralized infrastructures enabling improved scalability, access rate and resiliency. In this context, our previous work introduced the Distributed Geographical Table (DGT), an overlay scheme that builds and maintains virtual neighborhood relationships between peers with heterogeneous connections. In this paper we illustrate a smartphone-based vehicular network that uses the DGT, and we show its robustness against disconnections caused by the unavailability of connectivity/coverage (mostly occurring in rural areas), as well as overlay reconnections due to vertical handovers (mostly occurring in highly serviced urban areas). The simulative analysis of sample scenarios based on experimental measurements of coverage and connection throughput, carried out across/around Parma urban area, gives us valuable insights for defining an integrated model that will combine the DGT, user/vehicle mobility and connectivity/coverage types.

Real-time tracking of massive numbers of mobile devices, either carried by humans or embedded into vehicles, is a challenging problem whose solution may pave the way for a large set of valuable applications, ranging from social networking to ambient intelligence. A centralized approach, i.e. a server collects position data and provides it to interested consumers, is highly questionable, as performance can hardly scale up to the needs several million concurrent users. On other hand, a decentralized peer-to-peer approach, for which positioning data would flow directly among mobile devices may be very appealing, provided that messages to be routed are not too frequent and too expensive in terms of bandwidth usage. In this context we propose a peer-to-peer overlay scheme called Distributed Geographic Table (DGT), where each participant can efficiently retrieve node or resource information (data or services) located near any chosen geographic position. In particular, we describe a DGT-based localization protocol, that allows each peer for proactively discovering and tracking all the peers that are geographically near to itself. We provide a performance analysis of our protocol, referring to a simulated (although realistic) scenario where several hundred vehicles move on a real map. Our results show that the solution is efficient, scalable and highly adaptable to different application scenarios.