The ever increasing pace of IoT deployment is opening the door to concrete implementations of smart city applications, enabling the large-scale sensing and modeling of (near-)real-time digital replicas of physical processes and environments. This digital replica could serve as the basis of a decision support system, providing insights into possible optimizations of resources in a smart city scenario. In this article, we discuss an extension of a prior work, presenting a detailed proof-of-concept implementation of a Digital Twin solution for the Urban Facility Management (UFM) process. The Interactive Planning Platform for City District Adaptive Maintenance Operations (IPPODAMO) is a distributed geographical system, fed with and ingesting heterogeneous data sources originating from different urban data providers. The data are subject to continuous refinements and algorithmic processes, used to quantify and build synthetic indexes measuring the activity level inside an area of interest. IPPODAMO takes into account potential interference from other stakeholders in the urban environment, enabling the informed scheduling of operations, aimed at minimizing interference and the costs of operations.

Condition Monitoring (CM) is an extremely critical application of the Internet of Things (IoT) within Industry 4.0 and Smart City scenarios, especially following the recent energy crisis. CM aims to monitor the status of a physical appliance over time and in real time in order to react promptly when anomalies are detected, as well as perform predictive maintenance tasks. Current deployments suffer from both interoperability and management issues within their engineering process at all phases – from their design to their deployment, to their management –, often requiring human intervention. Furthermore, the fragmentation of the IoT landscape and the heterogeneity of IoT solutions hinder a seamless onboarding process of legacy devices and systems. In this paper, we tackle these problems by first proposing an architecture for CM based on both abstraction layers and toolchains, i.e ., automated pipelines of engineering tools aimed at supporting the engineering process. In particular, we introduce four different toolchains, each of them dedicated to a well-defined task ( e.g ., energy monitoring). This orthogonal separation of concerns aims to simplify both the understanding of a complex ecosystem and the accomplishment of independent tasks. We then illustrate our implementation of a complete CM system that follows said architecture as a real Structural Health Monitoring (SHM) pilot of the Arrowhead Tools project, by describing in detail every single tool that we developed. We finally show how our pilot achieves the main objectives of the project: the reduction of engineering costs, the integration of legacy systems, and the interoperability with IoT frameworks.

Driven by rapid urbanization, Smart Cities are emerging as interconnected ecosystems that leverage advanced technologies to enhance urban living. The growing diversity of urban services and citizen needs highlights the urgent demand for transformative solutions capable of managing the increasing complexity of urban environments. To address these challenges, Digital Twins (DTs) have emerged as a key enabling technology for Smart Cities. By creating virtual replicas of city assets and processes, DTs support closed-loop adaptive systems for analyzing city dynamics and enabling (semi-)autonomous optimization strategies. While early DT deployments were primarily cloud-based, recent advances have shifted toward a resource continuum that incorporates edge computing, giving rise to Digital Twin Networks (DTNs). Hosting fine-grained models closer to end users improves responsiveness and helps meet stringent operational requirements of critical city services. This dissertation investigates the role of DTNs in Smart City contexts, analyzing their applicability and deriving key design requirements. Its primary contribution is the definition of a research roadmap that structures and guides the development of DTN-supporting solutions, with emphasis on edge infrastructures. Along this roadmap, the thesis proposes and evaluates methods and tools addressing the main challenges associated with DTN realization. Specifically, this work contributes: (i) a scalable edge-based approach for near-real-time traffic simulation that combines automated scenario generation from vehicular data with distributed execution and adaptive load balancing to reduce simulation time; (ii) approaches enabling application-aware networking to support heterogeneous and dynamic Quality of Service requirements of DTN services; and (iii) a solution for large-scale DTN service deployment at the edge, extending the Multi-Access Edge Computing architecture to leverage far-edge resources, particularly vehicles. All solutions have been thoroughly validated, highlighting both advantages and limitations. Overall, this framework lays the foundation for efficient and scalable DTNs capable of supporting the continuous evolution and growing complexity of modern urban environments.