Urban traffic congestion remains a critical challenge to mobility efficiency and environmental sustainability. This paper presents a Digital Twin framework for real-time urban traffic management in Issy-les-Moulineaux, France. The framework integrates heterogeneous traffic data sources, including historical, real-time, and predictive data, into a unified architecture for scenario-based analytics and decision support. For traffic forecasting, deep learning models, including Long Short-Term Memory (LSTM) and Transformer-based approaches, are evaluated alongside baseline models such as SARIMA and Neural Prophet. The LSTM model is selected based on its stable and consistent performance across heterogeneous traffic conditions, achieving mean absolute error (MAE) values ranging from approximately 2 to 33 vehicles/hour across different road segments. The system also supports dynamic event handling, including full and time-based road closures, with capacity-constrained and equal-allocation diversion strategies for first-order impact estimation. Quantitative evaluations demonstrate that the framework enables proactive congestion mitigation, comparative emission impact assessment, and improved interoperability with city platforms through NGSI-LD standardization. By combining predictive modeling, real-time monitoring, and scenario-driven analytics, the proposed Digital Twin delivers actionable insights for urban planners, reducing short-term disruptions and supporting long-term sustainable mobility management.

The widespread application of medical information systems has promoted the growth of personal electronic medical records (EMRs), which are typically produced in different medical institutions and stored in data centers. Consequently, data owners no longer retain control over their medical data, nor can they establish access control rules for their EMRs. Therefore, this study designs a patient-centered EMR access control system that integrates decentralized smart contracts and role-based access control (RBAC) to provide fine-grained data access control. In this system, we integrate a role-based access control model to achieve user-permission definition and adopt a personalized data access policy definition mechanism to achieve patient-centered data access control. The proposed system allows data owners to define a series of data access policies through smart contracts, achieving decentralized management of data access control permissions. In addition, we analyze the security features of this scheme and design a series of comparative experiments to evaluate the performance. The experimental results show that this system can efficiently achieve access control of personal electronic medical records and has higher reliability compared to traditional cloud-based EMR sharing systems.

International audience

The increasing demand for smart cities calls for advanced solutions to enhance urban sustainability. Digital twin technology offers transformative potential by synchronizing virtual and physical environments in real time. However, existing approaches struggle with scalability due to the reliance on numerous specialized prediction models for individual urban components, the lack of a unified framework for different use cases limits generalization, and high latencies in real-time synchronization. To address these, this paper presents a comprehensive software architecture for smart city DT and integrates correlation-aware model reduction and dynamic adaptive forecasting to support diverse urban applications to improve generalizability and scalability. This is done while adapting a smart distribution of DT software components between edge and core servers to ensure a low-latency performance. Validated using real-world traffic and air quality data, the system demonstrates significant improvements in traffic flow, emissions reduction, and public transportation efficiency, and enhances air quality monitoring, forecasting, and pollutant management. Key contributions include a scalable and generalizable DT architecture, AI-driven adaptability, edge-core deployment, and extensive validation through predictive analytics. This work establishes a replicable blueprint for metropolitan-scale DTs, balancing computational efficiency with responsive urban analytics.

The growing need for intelligent tools to support urban planning and resource management has positioned Digital Twin (DT) technology as a cornerstone of smart city development. DTs, as dynamic virtual replicas of physical systems, offer capabilities that extend beyond mere representation, enabling monitoring, diagnostics, forecasting, and optimization. In the context of urban traffic management, DTs provide a robust solution for real-time traffic monitoring and predictive analytics. However, existing approaches often lack a systematic design methodology, leading to challenges in scalability and adaptability, particularly in heterogeneous environments. This paper presents a novel methodology for developing scalable and adaptive smart city DT architectures, with a focus on real-time traffic management. A modular and unified software framework is proposed, leveraging AI-driven approaches to address the complexity of managing diverse traffic data sources. A sequential learning model is integrated into the architecture to enhance the DT's adaptability to evolving traffic conditions and congestion patterns. The proposed framework is validated using real-world traffic data from an IoT network deployed in Madrid, demonstrating its scalability and low-latency performance. Experimental results highlight the effectiveness of the framework in handling heterogeneous traffic scenarios and its ability to deliver accurate predictions while minimizing resource overhead.

Network Digital Twin (NDT) collects data from physical, virtual, and software components and supports real-time network performance analysis, emulation, and intelligent physical network control. This paper surveys the current state of NDT specifications and explores NDT benefits for Network Operators (NOs) and its possible roles in future network management. It discusses the NDT key components, architecture, and integration of Machine Learning and Artificial Intelligence models in the NDT. Further, it covers virtualization technology management, suitability of Software-Defined Networking capabilities, and simulation tools to empower NDT. Two perspectives make the position of this survey different from existing studies; first, it highlights NDT limitations regarding Edge–Cloud Continuum (ECC) contextualization. ECC is a purposeful trending integration of Edge and Cloud Computing , involving multiple stakeholders like Service Providers, Customers, and Platform or Infrastructure Providers. However, current NDT specifications have not mentioned the ways to benefit stakeholders other than NOs. We also discuss notable computing and communication technologies transformations necessary to consider during NDT modeling, the existing data models, and reusable vocabularies that can be extended to achieve a detailed ECC representation for all stakeholders, essentially for Service Providers and Customers. Secondly, a data model is proposed that covers descriptive and prescriptive features and aims to provide a granular representation of ECC components to meet stakeholders’ requirements and render particular user information views. Different explored NDT perspectives, and proposed data model reduces the impact of existing NDT limitations in ECC representation.

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data from Industrial Internet of Things (IIoT) devices, have highlighted the critical need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. We defined a storage model and data format for private data through the design and deployment of smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in the real-world simulations, Hyper-IIoT shows well-controlled contract execution time, stable system throughput and fast consensus process, and is capable of handling high throughput and effective consensus in distributed systems even in large-scale request scenarios.

This paper presents a modular Digital Twin software architecture designed for smart cities, leveraging Edge-Cloud Continuum to enable the development of flexible and scalable DT-based solutions. Digital Twin technology provides a powerful framework for simulating, analyzing, and optimizing urban environments by integrating real-time and historical data from various city sensors through IoT, AI, and cloud computing. The proposed architecture addresses the limitations of existing DT frameworks by focusing on smart city-specific requirements such as dynamic resource management, real-time data processing, and autonomous decision-making. The viability of the proposed framework is demonstrated through a case study on autonomous traffic management in the city of Issy-les-Moulineaux. It shows how the proposed framework predicts traffic patterns and manages network resource allocation by adjusting the data sampling frequency to balance prediction accuracy and communication costs. The architecture’s modular design supports seamless integration and adaptability, making it suitable for various smart city applications, thereby advancing the development of more efficient, sustainable, and resilient urban environments.

Specifications for Network Digital Twin (NDT) from Standardization Development Organizations (SDOs), such as the Internet Engineering Task Force (IETF), and academic contributions focus primarily on benefiting network operators. However, they often overlook the needs of stakeholders in the Edge-Cloud Continuum (ECC), such as Service Providers, customers, and Platform or Infrastructure Providers. In ECC, resource heterogeneity, agile software component integration, and quality of service requirements are challenges. To address these challenges, continuous and granular monitoring of software and physical resources is required. In this paper, we present the design and ongoing implementation of a data model. It captures and characterizes the physical and software properties, i.e., Key Performance Indicators (KPIs), of Kubernetes-managed components in the ECC. Collected data is structured in NGSI-LD-compliant format and managed through interoperable context brokers for authenticating the requests of various stakeholder applications. We also demonstrate sample data curation from a lab-configured platform, its integration into the context broker, and how it responds to the queries concerning a particular component information, thereby representing a partial implementation of proposed data model to render the views for particular stakeholders.

Redundancy reduction plays a critical role in optimizing sensor network performance. This research proposes a deep-learning approach to identify and eliminate redundant sensors in a traffic network. This strategy aims to create a more cost-effective, efficient and reliable traffic monitoring system, ultimately leading to improvements in the transportation infrastructure. Leveraging traffic data from the Madrid Open Data Portal (focusing on ’District 19’), we employed sensor correlation (cosine) and similarity analysis (VGG16-based model) to identify significant correlations among sensors. This allows for accurate prediction (using Long Short-Term Memory(LSTM)-based models) of values from highly correlated sensors, leading to a potential reduction in District 19’s sensor nodes by 43% (from 32 to 18) and connectivity edges by 82% (from 106 to 19). Notably, the predictive accuracy for ’highly similar’ sensors achieved an average R-squared score of 0.82, validating the reliability of LSTM model predictions. These initial results encourage a larger analysis of the methodology to better prove the potential of our deep learning approach in optimizing and streamlining smart city infrastructure. This promising approach can be extended to analyze districts with higher sensor density and be adapted for application in other cities. We aim to utilize deep learning algorithms to optimize future sensor deployment planning.

The telecommunications sector is devoting an initial interest in the representation of complex networks as Digital Twins. The concept of a Digital Twin Network (DTN) is a research topic, but it promises to be an important step for harmonizing different models of the Edge-Cloud Continuum. The DTN software framework aims at helping network operations by providing updated and complete views on the network or parts of it, and it also introduces the possibility to simulate the network behavior or to learn from network events history (Machine Learning) without jeopardizing the actual operations of resources. In addition, thanks to the representation capabilities of the DT, its usage in the network promises to support different stakeholders’ views on their virtualized and physical infrastructure. This work tries to consolidate a DTN data model representing the elements of the Edge-Cloud Continuum by providing a layered (horizontal) and segmented (vertical) view of the infrastructure to all the involved stakeholders. The DTN model is an ontology where the linked classes represent properties and relations of networked components. This work aims to design a flexible and extensible ontology that describes the Edge-Cloud continuum usable in the telecommunications as well in the Cloud (IT and web) industries creating a bridge between the two.

Cities have undergone significant changes due to the rapid increase in urban population, heightened demand for resources, and growing concerns over climate change. To address these challenges, digital transformation has become a necessity. Recent advancements in Artificial Intelligence (AI) and sensing techniques, such as synthetic sensing, can elevate Digital Twins (DTs) from digital copies of physical objects to effective and efficient platforms for data collection and in-situ processing. In such a scenario, this paper presents a compre-hensive approach for developing a Traffic Monitoring System (TMS) based on Edge Intelligence (EI), specifically designed for smart cities. Our approach prioritizes the placement of intelligence as close as possible to data sources, and leverages an “opportunistic” interpretation of DT (ODT), resulting in a novel and interdisciplinary strategy to re-engineering large-scale distributed smart systems. The preliminary results of the proposed system have shown that moving computation to the edge of the network provides several benefits, including (i) enhanced inference performance, (ii) reduced bandwidth and power consumption, (iii) and decreased latencies with respect to the classic cloud -centric approach.

Digital twin (DT) is an emerging concept that is gaining attention in various industries. It refers to the ability to clone a physical object (PO) into a software counterpart. The softwarized object, termed logical object, reflects all the important properties and characteristics of the original object within a specific application context. To fully determine the expected properties of the DT, this article surveys the state-of-the-art starting from the original definition within the manufacturing industry. It takes into account related proposals emerging in other fields, namely augmented and virtual reality (e.g., avatars), multiagent systems, and virtualization. This survey thereby allows for the identification of an extensive set of DT features that point to the “softwarization” of POs. To properly consolidate a shared DT definition, a set of foundational properties is identified and proposed as a common ground outlining the essential characteristics (must-haves) of a DT. Once the DT definition has been consolidated, its technical and business value is discussed in terms of applicability and opportunities. Four application scenarios illustrate how the DT concept can be used and how some industries are applying it. The scenarios also lead to a generic DT architectural model. This analysis is then complemented by the identification of software architecture models and guidelines in order to present a general functional framework for the DT. This article, eventually, analyses a set of possible evolution paths for the DT considering its possible usage as a major enabler for the softwarization process.

The articles in this special section were presented at the IEEE World Forum (WF) on IoT held in Seoul, Korea, from March 6–8, 2015.

Future processing, storage and communication services will be highly pervasive: people, smart objects, machines and the surrounding space (all embedding devices such as with sensors, RFID tags etc.) will define a highly decentralized cyber environment of resources interconnected by dynamic networks of networks. As communications will extend to cover any combination of ’people, machines and things’, future networks will be increasingly complex and heterogeneous, yet always endorsed with the challenging task of ensuring end-to-end QoS. This paper proposes the groundwork for an advanced cognitive networking paradigm exploitable in future wired and wireless infrastructures: a decentralized cognitive plane to allow for cross-layer, cross-node and cross-network domain self-management, self-control and self-optimization, while being compatible with legacy management and control systems.