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.

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.

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.

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 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.

The increasing complexity and unpredictability of many ICT scenarios will represent a major challenge for future intelligent systems.The capability to dynamically and autonomously adapt to evolving and novel situations, with a partial or limited knowledge of the domain, both at the level of individual components and at the collective level, will become a crucial need for smart devices acting in many application domains.In this paper, we envision future systems able to selfdevelop mental models of themselves and of the environment they act in.Key properties will include: learning models of own capabilities; learning how to act purposefully towards the achievement of specific goals; and learning how to act in the presence of others, i.e., at the collective level.In our work, we will introduce the vision of self-development in ICT systems, by framing its key concepts and by illustrating suitable application domains.Then, we overview the many research areas that are contributing or can potentially contribute to the realisation of the vision, and identify some key research challenges.