Smart Cities

Smart cities seek to improve urban living by embedding advanced technologies into infrastructures, services, and governance. Edge Artificial Intelligence (Edge AI) has emerged as a critical enabler by moving computation and learning closer to data sources, enabling real-time decision-making, improving privacy, and reducing reliance on centralized cloud infrastructure. This survey provides a comprehensive review of the foundations, challenges, and opportunities of edge AI in smart cities. In particular, we begin with an overview of layer-wise designs for edge AI-enabled smart cities, followed by an introduction to the core components of edge AI systems, including applications, sensing data, models, and infrastructure. Then, we summarize domain-specific applications spanning manufacturing, healthcare, transportation, buildings, and environments, highlighting both the softcore (e.g., AI algorithm design) and the hardcore (e.g., edge device selection) in heterogeneous applications. Next, we analyze the sources of sensing data generation, model design strategies, and hardware infrastructure that underpin edge AI deployment. Building on these, we finally identify several open challenges and provide future research directions in this domain. Our survey outlines a future research roadmap to advance edge AI technologies, thereby supporting the development of adaptive, harmonic, and sustainable smart cities.

The beginning of the 21st century is associated with a significant technological leap on a global scale, which has had a substantial impact on production and economic processes at the national and regional levels. This radical technological change in the economy is linked to the emergence and development of artificial intelligence and effective knowledge management, which are the main drivers of economic growth. The use of AI today can be traced in many different areas of applied science—medicine, physics, mathematics, and engineering design, including modeling, planning, and management of territorial systems. The accumulation of large databases and other information necessary for AI to function is directly related to the spatial aspects of economic development, which is also based on local potential (a place-based approach). At the same time, local knowledge resources and innovation potential are not fully utilized in the context of technology diffusion and AI implementation in individual countries and regions. In this regard, this study aims to analyze the role of regional innovation systems, with a focus on AI development, and to track their impact across individual European regions, using NUTS2 spatial-level data to ensure objectivity. The authors consider AI innovation a modern tool for decision-making in the implementation of regional policy, with a specific impact on cohesion between EU regions. The results of the study show a direct link between the localization of regional innovation systems, R & D expenditure, AI implementation, and the economic development of European regions. Important factors influencing this process are the degree of Internet coverage, the capacity to generate innovation, the degree of AI implementation in the individual economic sectors of the countries, the growth of the ICT sector in relation to the overall development of GDP and the economy, and the result of the smart specialisation of regional innovation systems.

Cities increasingly face urban sustainability challenges due to rapid urbanization, climate pressures, and infrastructure demands. In response, smart city frameworks have emerged as transformative strategies that promote sustainability, efficiency, and resilience. Among the enabling technologies, the integration of the Internet of Things (IoT) and blockchain is gaining traction for supporting data-driven, transparent, and inclusive forms of sustainable smart cities. This systematic review analyzes peer-reviewed studies to examine how IoT and blockchain contribute to smart and sustainable urban development. The findings are organized into five thematic areas: (1) applications of IoT and blockchain for sustainable urban development; (2) operational applications across urban sustainability sectors such as energy, mobility, waste, and environmental management; (3) blockchain-enabled urban governance mechanisms including smart contracts, identity systems, and emergency response; (4) direct citizen engagement through transparent participation platforms and incentive-based systems; and (5) challenges and opportunities associated with IoT and blockchain technologies in the context of sustainable city development. In addition, the study proposes a conceptual framework that illustrates how IoT and blockchain integration support sustainable urban innovation. The review highlights the transformative potential of IoT–blockchain convergence in shaping future smart and sustainable cities and aligns with the United Nations Sustainable Development Goal 11.