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IoT, AI, and Digital Twins in Smart Cities: A Systematic Review for a Thematic Mapping and Research Agenda

Producción científica: Contribución a una revistaCríticarevisión exhaustiva

Resumen

Highlights: What are the main findings? This paper presents an SLR comprising the analysis of 64 studies on Urban Digital Twins, IoT, and AI in the development of smart cities using the PRISMA framework. The review identifies three thematic groups and key research points, while revealing gaps such as limited empirical evidence and interoperability challenges. What is the implication of the main finding? This paper highlights the need for integrated, data-driven strategies to improve urban management and policies through Urban Digital Twins, IoT, and AI. The review allows proposing a research agenda to guide future innovations, supporting the development of more sustainable, resilient, and smart cities. The accelerating complexity of urban environments has prompted cities to adopt digital technologies that improve efficiency, sustainability, and resilience. Among these, Urban Digital Twins (UDTw) have emerged as transformative tools for real-time representation, simulation, and management of urban systems. This Systematic Literature Review (SLR) examines the integration of Digital Twins (DTw), the Internet of Things (IoT), and Artificial Intelligence (AI) into the Smart City Development (SCD). Following the PSALSAR framework and PRISMA 2020 guidelines, 64 peer-reviewed articles from IEEE Xplore, Association for Computing Machinery (ACM), Scopus, and Web of Science (WoS) digital libraries were analyzed by using bibliometric and thematic methods via the Bibliometrix package in R. The review allowed identifying key technological trends, such as edge–cloud, architectures, 3D immersive visualization, Generative AI (GenAI), and blockchain, and classifies UDTw applications into five domains: traffic management, urban planning, environmental monitoring, energy systems, and public services. Persistent challenges have been also outlined, including semantic interoperability, predictive modeling, data privacy, and impact evaluation. This study synthesizes the current state of the field, by clearly identifying a thematic mapping, and proposes a research agenda to align technical innovation with measurable urban outcomes, offering strategic insights for researchers, policymakers, and planners.

Idioma originalInglés
Número de artículo175
PublicaciónSmart Cities
Volumen8
N.º5
DOI
EstadoPublicada - oct 2025

Nota bibliográfica

Publisher Copyright:
© 2025 by the authors.

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

  1. ODS 11: Ciudades y comunidades sostenibles
    ODS 11: Ciudades y comunidades sostenibles
  2. ODS 17: Alianzas para lograr los objetivos
    ODS 17: Alianzas para lograr los objetivos

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