Search Results (732)

Smart cities are technologically advanced urban environments where interconnected systems and data-driven technologies enhance public service delivery and quality of life. These cities rely on information and communication technologies, the Internet of Things, big data, cloud computing, and other Industry 4.0 tools to support efficient city management and foster citizen engagement. Often referred to as digital cities, they integrate intelligent infrastructures and real-time data analytics to improve mobility, security, and sustainability. Ubiquitous sensors, paired with Artificial Intelligence, enable cities to monitor infrastructure, respond to residents’ needs, and optimize urban conditions dynamically. Given the increasing significance of Industry 4.0 in urban development, this study adopts a bibliometric approach to systematically review the application of these technologies within smart cities. Utilizing major academic databases such as Scopus and Web of Science the research aims to identify the primary Industry 4.0 technologies implemented in smart cities, assess their impact on infrastructure, economic systems, and urban communities, and explore the challenges and benefits associated with their integration. The bibliometric analysis included publications from 2016 to 2023, since the emergence of urban researchers’ interest in the technologies of the new industrial revolution. The task is to contribute to a deeper understanding of how smart cities evolve through the adoption of advanced technological frameworks. Research indicates that IoT and AI are the most commonly used tools in urban spaces, particularly in smart mobility and smart environments. Full article

Urban areas are facing increasing traffic congestion, pollution, and infrastructure strain. Traditional urban transportation systems are often fragmented. They require users to plan, pay, and travel across multiple disconnected services. Mobility-as-a-Service (MaaS) integrates these services into a single digital platform, simplifying access and improving the user experience. This review critically examines the role of MaaS in fostering sustainable mobility ecosystems. MaaS aims to enhance user-friendliness, service variety, and sustainability by adopting a customer-centric approach to transportation. The findings reveal that successful MaaS systems consistently align with multimodal transport infrastructure, equitable access policies, and strong public-private partnerships. MaaS enhances the management of routes and traffic, effectively mitigating delays and congestion while concurrently reducing energy consumption and fuel usage. In this study, the authors examine MaaS as a new mobility paradigm for a sustainable transportation system in smart cities, observing the challenges and opportunities associated with its implementation. To assess the environmental impact, a sustainability index is calculated based on the use of different modes of transportation. Significant findings indicate that MaaS systems are proliferating in both quantity and complexity, increasingly integrating capabilities such as real-time multimodal planning, dynamic pricing, and personalized user profiles. Full article

The integration of digital twins (DTs) with intelligent traffic systems (ITSs) holds strong potential for improving real-time management in smart cities. However, securing digital twins remains a significant challenge due to the dynamic and adversarial nature of cyber–physical environments. In this work, we propose TwinFedPot, an innovative digital twin-based security architecture that combines honeypot-driven data collection with Zero-Shot Learning (ZSL) for robust and adaptive cyber threat detection without requiring prior sampling. The framework leverages Inverse Federated Distillation (IFD) to train the DT server, where edge-deployed honeypots generate semantic predictions of anomalous behavior and upload soft logits instead of raw data. Unlike conventional federated approaches, TwinFedPot reverses the typical knowledge flow by distilling collective intelligence from the honeypots into a central teacher model hosted on the DT. This inversion allows the system to learn generalized attack patterns using only limited data, while preserving privacy and enhancing robustness. Experimental results demonstrate significant improvements in accuracy and F1-score, establishing TwinFedPot as a scalable and effective defense solution for smart traffic infrastructures. Full article

Rapid urbanization and escalating disaster risks necessitate resilient urban governance systems. Smart city initiatives that leverage digital technologies—such as the internet of things (IoT), big data analytics, and artificial intelligence (AI)—demonstrate transformative potential in enhancing emergency management capabilities. However, empirical evidence regarding their causal impact and underlying mechanisms remains limited, particularly in developing economies. Drawing on panel data from 275 Chinese prefecture-level cities over the period 2006–2021 and using China’s smart city pilot policy as a quasi-natural experiment, this study applies a multi-period difference-in-differences (DID) approach to rigorously assess the effects of smart city construction on emergency management capabilities. Results reveal that smart city construction produced a statistically significant improvement in emergency management capabilities, which remained robust after conducting multiple sensitivity checks and controlling for potential confounding policies. The benefits exhibit notable heterogeneity: emergency management capability improvements are most pronounced in central China and in cities at the extremes of population size—megacities (>10 million residents) and small cities (<1 million residents)—while effects remain marginal in medium-sized and eastern cities. Crucially, mechanism analysis reveals that digital technology application fully mediates 86.7% of the total effect, whereas factor allocation efficiency exerts only a direct, non-mediating influence. These findings suggest that smart cities primarily enhance emergency management capabilities through digital enablers, with effectiveness contingent upon regional infrastructure development and urban scale. Policy priorities should therefore emphasize investments in digital infrastructure, interagency data integration, and targeted capacity-building strategies tailored to central and western regions as well as smaller cities. Full article

Handover (HO) management plays a key role in ensuring uninterrupted connectivity across evolving wireless networks. While previous generations such as 4G and 5G have introduced several HO strategies, these techniques are insufficient to meet the rigorous demands of sixth-generation (6G) networks in ultra-dense, heterogeneous smart city environments. Existing studies often fail to provide integrated HO solutions that consider key concerns such as energy efficiency, security vulnerabilities, and interoperability across diverse network domains, including terrestrial, aerial, and satellite systems. Moreover, the dynamic and high-mobility nature of smart city ecosystems further complicate real-time HO decision-making. This survey aims to highlight these critical gaps by systematically categorizing state-of-the-art HO approaches into AI-based, fuzzy logic-based, and hybrid frameworks, while evaluating their performance against emerging 6G requirements. Future research directions are also outlined, emphasizing the development of lightweight AI–fuzzy hybrid models for real-time decision-making, the implementation of decentralized security mechanisms using blockchain, and the need for global standardization to enable seamless handovers across multi-domain networks. The key outcome of this review is a structured and in-depth synthesis of current advancements, which serves as a foundational reference for researchers and engineers aiming to design intelligent, scalable, and secure HO mechanisms that can support the operational complexity of next-generation smart cities. Full article

This study conducts a comprehensive systematic review of the economic impact of Digital Twin (DT) technology within the Architecture, Engineering, and Construction (AEC) industry, following the PRISMA methodology. While DT adoption has been accelerated by advancements in Building Information Modelling (BIM), the Internet of Things (IoT), and data analytics, significant challenges persist—most notably, high initial investment costs and integration complexities. Synthesising the literature from 2016 onwards, this review identifies sector-specific barriers, regulatory burdens, and a lack of standardisation as key factors constituting DT implementation costs. Despite these hurdles, DTs demonstrate strong potential for enhancing construction productivity, optimising lifecycle asset management, and enabling predictive maintenance, ultimately reducing operational expenditures and improving long-term financial performance. Case studies reveal cost efficiencies achieved through DTs in modular construction, energy optimisation, and infrastructure management. However, limited financial resources and digital skills continue to constrain the uptake across the sector, with various extents of impact. This paper calls for the development of unified standards, innovative public–private funding mechanisms, and strategic collaborations to unlock and utilise DTs’ full economic value. It also recommends that future research explore theoretical frameworks addressing governance, data infrastructure, and digital equity—particularly through conceptualising DT-related data as public assets or collective goods in the context of smart cities and networked infrastructure systems. Full article

Assessing a city’s level of smartness according to global indices is a relatively new area of investigation. It is useful in encouraging a rethinking of urban digital strategies, although the different approaches to global smart city rankings have been subject to criticism. This paper highlights the methodological features of constructing the Smart City Index (SCI) from the IMD (International Institute for Management Development) based on residents’ assessments, their satisfaction with electronic services, and their perception of the priority of urban infrastructure areas. The Central European cities of the Visegrad region (Prague/Czech Republic, Budapest/Hungary, Bratislava/Slovakia, Warsaw and Krakow/Poland) were chosen as the basis for an in-depth analysis. The architectonics, i.e., the internal system of constructing and calculating city rankings by SCI, is analyzed. A comparative analysis of the technology indicators (e-services) in five cities of the Visegrad region, presented in the SCI, showed the smart features of each city. The progressive and regressive trends in the dynamics of smartness in the cities in the Visegrad region were identified in five urban spheres indicated in the Index: Government, Activity, Health and Safety, Mobility, and Opportunities. This also made it possible to identify certain methodological gaps in the SCI in establishing interdependencies between the data on the residents’ perception of the priority of areas of life in a particular city and the residents’ level of satisfaction with electronic services. In particular, the structural indicators “Affordable housing” and “Green spaces” are not supported by e-services. This research aims to bridge this methodological gap by proposing a model for evaluating the e-service according to the degree of coverage of different spheres of life in the city. The application of the project, as well as cross-sectoral and systemic approaches, made it possible to develop basic models for assessing the value of e-services. These models can be implemented by municipalities to assess and monitor e-services, as well as to select IT projects and elaborate strategies for smart sustainable city development. Full article

In earthquake-prone areas such as Tokyo, accurate estimation of bearing stratum depth is crucial for foundation design, liquefaction assessment, and urban disaster mitigation. However, traditional methods such as the standard penetration test (SPT), while reliable, are labor-intensive and have limited spatial distribution. In this study, 942 geological survey records from the Tokyo metropolitan area were used to evaluate the performance of three machine learning algorithms, random forest (RF), artificial neural network (ANN), and support vector machine (SVM), in predicting bearing stratum depth. The main input variables included geographic coordinates, elevation, and stratigraphic category. The results showed that the RF model performed well in terms of multiple evaluation indicators and had significantly better prediction accuracy than ANN and SVM. In addition, data density analysis showed that the prediction error was significantly reduced in high-density areas. The results demonstrate the robustness and adaptability of the RF method in foundation soil layer identification, emphasizing the importance of comprehensive input variables and spatial coverage. The proposed method can be used for large-scale, data-driven bearing stratum prediction and has the potential to be integrated into geological risk management systems and smart city platforms. Full article

Tiny Machine Learning (TinyML) extends edge AI capabilities to resource-constrained devices, offering a promising solution for real-time, low-power intelligence in smart cities. This review systematically analyzes 66 peer-reviewed studies from 2019 to 2024, covering applications across urban mobility, environmental monitoring, public safety, waste management, and infrastructure health. We examine hardware platforms and machine learning models, with particular attention to power-efficient deployment and data privacy. We review the approaches employed in published studies for deploying machine learning models on resource-constrained hardware, emphasizing the most commonly used communication technologies—while noting the limited uptake of low-power options such as Low Power Wide Area Networks (LPWANs). We also discuss hardware–software co-design strategies that enable sustainable operation. Furthermore, we evaluate the alignment of these deployments with the United Nations Sustainable Development Goals (SDGs), highlighting both their contributions and existing gaps in current practices. This review identifies recurring technical patterns, methodological challenges, and underexplored opportunities, particularly in the areas of hardware provisioning, usage of inherent privacy benefits in relevant applications, communication technologies, and dataset practices, offering a roadmap for future TinyML research and deployment in smart urban systems. Among the 66 studies examined, 29 focused on mobility and transportation, 17 on public safety, 10 on environmental sensing, 6 on waste management, and 4 on infrastructure monitoring. TinyML was deployed on constrained microcontrollers in 32 studies, while 36 used optimized models for resource-limited environments. Energy harvesting, primarily solar, was featured in 6 studies, and low-power communication networks were used in 5. Public datasets were used in 27 studies, custom datasets in 24, and the remainder relied on hybrid or simulated data. Only one study explicitly referenced SDGs, and 13 studies considered privacy in their system design. Full article

Air quality monitoring systems based on Internet of Things (IoT) technology are critical for addressing environmental and public health challenges, but their energy efficiency poses a significant challenge to their autonomous and scalable deployment. This study investigates strategies to enhance the energy efficiency of IoT-based air quality monitoring systems. A comprehensive analysis of sensor types, data transmission protocols, and system architectures was conducted, focusing on their energy consumption. An energy-efficient system was designed using the Smart Air sensor, Zigbee gateway, and Mini UPS, with its performance evaluated through daily energy consumption, backup operation time, and annual energy use. An integrated efficiency index (IEI) was introduced to compare sensor models based on functionality, energy efficiency, and cost. The proposed system achieves a daily energy consumption of 72 W·h, supports up to 10 h of autonomous operation during outages, and consumes 26.28 kW·h annually. The IEI analysis identified the Ajax LifeQuality as the most energy-efficient sensor, while Smart Air offers a cost-effective alternative with broader functionality. The proposed architecture and IEI provide a scalable and sustainable framework for IoT air quality monitoring, with potential applications in smart cities and residential settings. Future research should explore renewable energy integration and predictive energy management. Full article

Urban road traffic congestion poses significant challenges to sustainable mobility in smart cities. Traditional traffic light systems, reliant on static or semi-fixed timers, fail to adapt to dynamic traffic conditions, exacerbating congestion and limiting inclusivity. To address these limitations, this paper proposes H-ATLM (a hybrid adaptive traffic lights management), a system utilizing the deep deterministic policy gradient (DDPG) reinforcement learning algorithm to optimize traffic light timings dynamically based on real-time data. The system integrates advanced sensing technologies, such as cameras and inductive loops, to monitor traffic conditions and adaptively adjust signal phases. Experimental results demonstrate significant improvements, including reductions in congestion (up to 50%), increases in throughput (up to 149%), and decreases in clearance times (up to 84%). These findings open the door for integrating accessibility-focused features such as adaptive signaling for accessible vehicles, dedicated lanes for paratransit services, and prioritized traffic flows for inclusive mobility. Full article

The increase in garbage generated in modern societies demands the implementation of a more sustainable model as well as new methods for efficient waste management. This article describes the development and implementation of a prototype of a smart bin that automatically sorts waste using a multi-agent system and blockchain integration. The proposed system has sensors that identify the type of waste (organic, plastic, paper, etc.) and uses collaborative intelligent agents to make instant sorting decisions. Blockchain has been implemented as a technology for the immutable and transparent control of waste registration, favoring traceability during the classification process, providing sustainability to the process, and making the audit of data in smart urban environments transparent. For the computer vision algorithm, three versions of YOLO (YOLOv8, YOLOv11, and YOLOv12) were used and evaluated with respect to their performance in automatic detection and classification of waste. The YOLOv12 version was selected due to its overall performance, which is superior to others with mAP@50 values of 86.2%, an overall accuracy of 84.6%, and an average F1 score of 80.1%. Latency was kept below 9 ms per image with YOLOv12, ensuring smooth and lag-free processing, even for utilitarian embedded systems. This allows for efficient deployment in near-real-time applications where speed and immediate response are crucial. These results confirm the viability of the system in both accuracy and computational efficiency. This work provides an innovative solution in the field of ambient intelligence, characterized by low equipment cost and high scalability, laying the foundations for the development of smart waste management infrastructures in sustainable cities. Full article

The pervasive scale of networks brought about by smart city applications has created infeasible energy footprints and necessitates the inclusion of sensing sustained operations with minimal human intervention. Consequently, integrated sensing and communication (ISAC) is emerging as a key technology for 6G systems. ISAC systems realize dual functions using shared spectrum, which complicates interference management. This motivates the development of advanced signal processing and multiplexing techniques. In this context, extremely large antenna arrays (ELAAs) have emerged as a promising solution. ELAAs offer substantial gains in spatial resolution, enabling precise beamforming and higher multiplexing gains by operating in the near-field (NF) region. Despite these advantages, the use of ELAAs increases energy consumption and exacerbates carbon emissions. To address this, NF multiple-input multiple-output (NF-MIMO) systems must incorporate sustainable architectures and scalable solutions. This paper provides a comprehensive review of the various methodologies utilized in the design of energy-efficient NF-MIMO-based ISAC systems. It introduces the foundational principles of the latest research while identifying the strengths and limitations of green NF-MIMO-based ISAC systems. Furthermore, this work provides an in-depth analysis of the open challenges associated with these systems. Finally, it offers a detailed overview of emerging opportunities for sustainable designs, encompassing backscatter communication, dynamic spectrum access, fluid antenna systems, reconfigurable intelligent surfaces, and energy harvesting technologies. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

Smart cities necessitate ultra-secure and scalable communication frameworks to manage billions of interconnected IoT devices, particularly in the face of the emerging quantum computing threats. This paper proposes the QESIF, a novel Quantum-Enhanced Secure IoT Framework that integrates Quantum Key Distribution (QKD) with classical IoT infrastructures via a hybrid protocol stack and a quantum-aware intrusion detection system (Q-IDS). The QESIF achieves high resilience against eavesdropping by monitoring quantum bit error rate (QBER) and leveraging entropy-weighted key generation. The simulation results, conducted using datasets TON IoT, Edge-IIoTset, and Bot-IoT, demonstrate the effectiveness of the QESIF. The framework records an average QBER of 0.0103 under clean channels and discards over 95% of the compromised keys in adversarial settings. It achieves Attack Detection Rates (ADRs) of 98.1%, 98.7%, and 98.3% across the three datasets, outperforming the baselines by 4–9%. Moreover, the QESIF delivers the lowest average latency of 20.3 ms and the highest throughput of 868 kbit/s in clean scenarios while maintaining energy efficiency with 13.4 mJ per session. Full article