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Smart city networks are critical for delivering essential services such as healthcare, education, and business operations. However, these networks are highly susceptible to a range of threats, including natural disasters and intentional cyberattacks, which can severely disrupt their functionality. To address these vulnerabilities, we present the resilient smart city (ResiSC) system, designed to enhance the resilience of smart city communication networks through a topological design approach. Our system employs a graph‐theoretic algorithm to determine the optimal network topology for a given set of nodes, aiming to maximize connectivity while minimizing link provisioning costs. We introduce two novel connectivity measurements, All Nodes Reachability (ANR) and Sum of All Nodes Reachability (SANR), to evaluate network resilience. We applied our approach to data from two public universities of different sizes, simulating various attack scenarios to assess the robustness of the resulting network topologies. Evaluation results indicate that our solution improves network resilience against targeted attacks by 38% compared to baseline methods such as k‐nearest neighbours (k‐NN) graphs, while also reducing the number of additional links and their associated costs. Results also indicate that our proposed solution outperforms baseline methods like k‐NN in terms of network resilience against targeted attacks by 41%. This work provides a practical framework for developing robust smart city networks capable of withstanding diverse threats. [ABSTRACT FROM AUTHOR]

In urban smart city environments, traffic hazards can lead to catastrophic outcomes, including significant property losses and severe threats to public safety. Conventional traffic monitoring systems are limited in terms of accuracy and speed, presenting significant challenges for real-time traffic surveillance. To tackle these challenges, this paper introduces the GC-YOLOv9 algorithm. Specifically, we have enhanced the YOLOv9 model by incorporating Ghost Convolution, markedly improving the model's perceptual abilities and detection accuracy. Furthermore, this study designed an integrated smart city framework that includes layers for service applications, the Internet of Things, edge processing, and data centers. By deploying the enhanced YOLOv9 model within this framework, our method achieved mAP@0.5 scores of 77.15 and 74.95 on the BDD100K and Cityscapes datasets, respectively, surpassing existing technologies. Additionally, the potential applications of this method in public area fire safety management, forest fire monitoring, and intelligent security systems further underscore its significant value in improving the safety and efficiency of smart cities. [ABSTRACT FROM AUTHOR]

Clean energy has emerged as the focal point of global energy and power development. With the advancement of 5G technology and the Internet of Things (IoT), the demand for sustainable energy supply has become more pressing, leading to widespread attention to vibration energy harvesting technology. This technology enables the conversion of vibrational energy from natural phenomena such as ocean waves and wind, as well as machinery operation and human activities, into electrical energy, thus supporting the expansion of self‐sustained IoT systems. This review provides an overview of the progress in vibration energy harvesting technology and discusses the integration of this technology with self‐powered sensors and artificial intelligence. These integrations are reflected in the enhanced accuracy of environmental monitoring, increased efficiency in intelligent transportation and industrial production, and improved quality of life through intelligent healthcare and smart home. Such applications demonstrate the significant potential of self‐sustained artificial IoT in promoting environmental sustainability and elevating the level of intelligent living. In summary, exploring and applying vibration energy harvesting technology to support the autonomous operation of IoT devices is key to building a more sustainable, intelligent, and interconnected world. [ABSTRACT FROM AUTHOR]

With the application of information technology under the goal of developing a smart city, smart city streetscape design is emerging and gradually developing. Due to the lack of attention to "smartening" traditional streetscape design, the existing urban streetscape design falls short in meeting the needs of smart city development. Regarding current research, more in-depth research should integrate the streetscape design system into the development of smart cities and build a practical smart-streetscape system. This paper is part of a larger study that aims to explore suitable streetscape sustainable design guidelines within the smart city context by integrating a set of guidelines based on the four dimensions (the city, society, the economy, and the environment) of a sustainable streetscape. Methodologically, this study conducts a systematic literature review of the discussion of streetscapes in the context of smart cities, finding 7 first-level attributes and 39 s-level candidate guidelines for smart city streetscape sustainable design. In the second stage, experts' opinions were collected through a Delphi method, and the attributes and guidelines were screened and revised based on experts' theoretical and practical knowledge. Through three rounds of Delphi method review and Likert scale (5 scores) to score the attributes and guidelines, the finally 7 first-level attributes and 38 s-level guidelines were determined. Use the analytic hierarchy process (AHP) to determine the weightage of guidelines. Finally, it analyzes how the guidelines are relevant to the four dimensions of sustainable streetscape. Subsequently, the guidelines will be used to develop an SCS design framework that will be useful for advancing our understanding of smart city streetscapes promote urban sustainable development and can be used to explore how friendly the smart city streetscapes are to its residents. [ABSTRACT FROM AUTHOR]

To better provide urban services and build an increasingly sustainable architecture, big data can be used to make more efficient use of current assets while enhancing the caliber of services offered to local inhabitants. However, there are several challenges to incorporating big data into existing infrastructure. Therefore, this research aims to determine the problems associated with Big Data's effectiveness in developing intelligent towns and to investigate the connections between those difficulties. The 14 issues with Big Data were found through a literature study, and the precision was checked by feedback from professionals. Next, we employ a combined approach based on fuzzy interpretation, Structured simulation, and the Fuzzy Making Decisions Trial and Assessment Laboratories to decipher the connections between our identified problems incorporating Big Data into the development of smart cities is hampered, as shown by the analysis of links between challenges, primarily by the heterogeneous inhabitants in developed cities and the lack of connectivity. The findings of this study will provide creative city practitioners and policy planners with the information they need to successfully tackle these obstacles, clearing the way for the widespread adoption of smart city technologies. This research is a first step towards creating an interpretive structural model of the difficulties brought on by Big Data in cutting-edge urban planning. The study attempts, in part, to use this paradigm to better understand the relationship among the highlighted issues. [ABSTRACT FROM AUTHOR]

As a giant in energy consumption, buildings urgently need to optimize control strategies for the main energy consuming equipment inside buildings. It is of great significance to design advanced control algorithms to improve the efficiency of the main energy consuming equipment in buildings, namely air conditioning, in the current energy shortage. The model predictive control algorithms and strategies were used in this study to control the HVAC system to improve the energy utilization of the city. Then linear matrix inequality with robust model predictive feedback controller was used to optimize and get the model predictive control optimization algorithm. The research results showed that, under the influence of different factors, the three regions controlled by the model predictive control optimization algorithm showed a little overshooting in the initial state. But it was quickly corrected after adjustment. Meanwhile, the average tracking error of temperature and humidity in each region was 0.139°C and 0.13g/kg dry air, respectively. The average predicted mean vote was 0.32. In actual office buildings, the proposed algorithm controlled the temperature within the reference value range of 0.1°C throughout the entire process. The total electricity consumption and electricity price costs were reduced by 12.11% and 22.54%, respectively. In summary, the proposed method has good performance for HVAC system application, which can effectively realize energy saving and emission reduction and improve human comfort. This method makes important contributions to promote the construction of smart cities and the development of green buildings. [ABSTRACT FROM AUTHOR]

The smart city model on multi-agent systems and the Internet of Things using a wireless sensor network is designed to improve the quality of life for citizens, increase resource efficiency, and reduce costs. This model enables the collection, analysis, and sharing of information by connecting and coordinating devices and systems within the smart city. In this model, intelligent agents act as sensors, and the smart gateway plays the role of a base station. The main goal of this model is to reduce energy consumption. To achieve this goal, intelligent agents are divided into clusters, with each cluster having a cluster head. The cluster head’s task is to collect and aggregate information from the intelligent agents within its cluster and send it to the smart gateway. In the proposed method, each intelligent agent selects a cluster in a distributed manner. An intelligent agent may choose another intelligent agent as its cluster head or select itself as a cluster head and directly send the data to the smart gateway. Each intelligent agent chooses the cluster head after calculating the importance level of neighboring intelligent agents. By using this model, cities can experience increased resource efficiency and cost reduction by leveraging innovative technologies. The proposed method has been implemented in different scenarios of smart cities, such as sparse and crowded smart cities with varying message sizes. In all simulations, the proposed method demonstrated good capabilities in optimizing energy consumption management. [ABSTRACT FROM AUTHOR]

IoT sensors are crucial for visualizing multidimensional and multimodal information and enabling future IT applications/services such as cyber-physical spaces, digital twins, autonomous driving, smart cities and virtual/augmented reality (VR or AR). However, IoT sensors need to be battery-free to realistically manage and maintain the growing number of available sensing devices. Here, we provide a novel sensor design approach that employs metasurfaces to enable multifunctional sensing without requiring an external power source. Importantly, unlike existing metasurface-based sensors, our metasurfaces can sense multiple physical parameters even at a fixed frequency by breaking classic harmonic oscillations in the time domain, making the proposed sensors viable for usage with limited frequency resources. Moreover, we provide a method for predicting physical parameters via the machine learning-based approach of random forest regression. The sensing performance was confirmed by estimating the temperature and light intensity, and excellent determination coefficients larger than 0.96 were achieved. Our study affords new opportunities for sensing multiple physical properties without relying on an external power source or requiring multiple frequencies, which markedly simplifies and facilitates the design of next-generation wireless communication systems. Metasurface-based sensors provide a battery-free sensing solution for maintaining numerous IoT devices with little human resources. However, the conventional method exploited resonant mechanisms associated with multiple physical parameters through different frequencies, although available frequencies were strictly limited. We report the first sensor design approach using circuit-based metasurfaces that offer a higher degree of freedom to design time-varying scattering profiles associated with multiple physical properties at a single frequency. Our prototype detects light intensity and temperature with an excellent determination coefficient above 0.96 via a machine-learning technique. [ABSTRACT FROM AUTHOR]

This article reports the proposal for the use of towers solar (solar chimneys) in urban environments in order to take advantage of landfills, unpopulated or wild hills within or near cities, clearing landfills, artificial hills; considering that the solar tower can maintain the mechanical power of its wind turbine constant. To this end, a mathematical model has been developed to determine the collector area based on solar radiation and the mechanical power of the turbine. The present proposal has the potential that at a technical level there is the possibility of producing electrical energy, production of water intended to create/maintain green environments or for the population, hydrogen production, capture of atmospheric pollutants, measurement of air quality and elimination of cloud cover. [ABSTRACT FROM AUTHOR]

Traffic flow prediction plays a crucial role in the development of smart cities. However, existing studies face challenges in effectively capturing spatio-temporal contexts, handling hierarchical temporal features, and understanding spatial heterogeneity. To better manage the spatio-temporal correlations inherent in traffic flow, we present a novel model called Dynamic Spatio-Temporal Memory-Augmented Network (DSTMAN). Firstly, we design three spatial–temporal embeddings to capture dynamic spatial–temporal contexts and encode the unique characteristics of time units and spatial states. Secondly, these three spatial–temporal components are integrated to form a multi-scale spatial–temporal block, which effectively extracts hierarchical spatial–temporal dependencies. Finally, we introduce a meta-memory node bank to construct an adaptive neighborhood graph, implicitly representing spatial relationships and enhancing the learning of spatial heterogeneity through a secondary memory mechanism. Evaluation on four public datasets, including METR-LA and PEMS-BAY, demonstrates that the proposed model outperforms benchmark models such as MTGNN, DCRNN, and AGCRN. On the METR-LA dataset, our model reduces the MAE by 4% compared to MTGNN, 6.9% compared to DCRNN, and 5.8% compared to AGCRN, confirming its efficacy in traffic flow prediction. [ABSTRACT FROM AUTHOR]

Ground level ozone (O3) is a highly oxidising gas with very reactive properties, harmful at high levels, generated by complex photochemical reactions when 'primary' pollutants from combustion of fossil materials react with sunlight. Thus, its concentration serves as an indicator of the activity of other air pollutants and plays a key role in Air Quality monitoring systems in smart cities. To increase its spatial sampling resolution over the city map, ozone low cost sensors are an interesting alternative, but they have a lack of accuracy. In this context, artificial intelligence techniques, in particular ensemble machine learning methods, can improve the raw readings from these sensors taking into account additional environmental information. In this paper, we analyse, propose and compare different techniques, reducing the estimation error in around 94 %, achieving the best results using the Gradient Boosting algorithm and outperforming the related work using sensor approximately 10 times less expensive. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is essential in several Internet application areas and remains a key technology for communication technologies. Shorter delays in transmission between Roadside Units (RSUs) and vehicles, road safety, and smooth traffic flow are the major difficulties of Intelligent Transportation System (ITS). Machine Learning (ML) was an advanced technique to find hidden insights into ITSs. This article introduces an Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control (IAOADL-TCC) for ITS in Smart Cities. The presented IAOADL-TCC model enables traffic data collection and route traffic on existing routes for avoiding traffic congestion in smart cities. The IAOADL-TCC algorithm exploits a hybrid convolution neural network attention long short-term memory (HCNN-ALSTM) method for traffic congestion control. In addition, an IAOA-based hyperparameter tuning strategy is derived to optimally modify the parameters of the HCNN-ALSTM model. The presented IAOADL-TCC model effectively enhances the flow of traffic and reduces congestion. The experimental validation was performed using the road traffic dataset from the Kaggle repository. The proposed model obtained an average accuracy of 98.03 % with an error rate of 1.97 %. The experimental analysis stated the superior performance of the IAOADL-TCC approach over other DL methods. [ABSTRACT FROM AUTHOR]

Featured Application: This work presents the use of device-free wireless sensing for crowd size estimation on subway platforms. Dense urban environments pose significant challenges when it comes to detecting and measuring crowd size due to their nature of being free-flow environments containing many dynamic factors. In this paper, we use a wireless sensor network (WSN) to perform device-free crowd size estimation in a subway station. Our sensing solution uses the change in attenuation of the communication links between sensor nodes to estimate the number of people standing on the platform. In order to achieve this, we use the same attenuation information coming from the WSN to detect the presence of a rail vehicle in the station and compensate for the channel fading caused by the introduced rail vehicle. We make use of two separately trained regression models depending on the presence or absence of a rail vehicle to estimate the people count. The detection of rail vehicles occurred with a near-perfect accuracy. When evaluating the resulting estimation model on our test set, we achieved a mean average error of 3.567 people, which is a significant improvement over 6.192 people when using a single regression model. This demonstrates that device-free sensing technologies can be successfully implemented in dynamic environments by implementing detection techniques and using different regression models depending on the environment's state. [ABSTRACT FROM AUTHOR]

This study adopts an integrative review approach to explore the differences and similarities between smart cities and sustainable cities. The research starts by performing two systematic literature reviews about both paradigms and, after that, employs a thematic analysis to identify key themes, definitions, and characteristics that differentiate and connect these two urban development concepts. The findings reveal more similarities than differences between the two paradigms. Despite this, some key differences are identified. Smart cities are characterized by their use of advanced information and communication technologies to enhance urban infrastructure, improve public services, and optimize resource management. In contrast, sustainable cities focus on environmental conservation, social equity, and economic viability to ensure long-term urban resilience and quality of life. This study is important because it clarifies both concepts and highlights the potential for integrating smart and sustainable city strategies to address contemporary urban challenges more holistically. The findings also suggest a convergence towards the concept of 'smart sustainable cities', which leverage technology to achieve sustainability goals. Finally, this study concludes by identifying research gaps and proposing a future research agenda to further understand and optimize the synergy between smart and sustainable urban development paradigms. [ABSTRACT FROM AUTHOR]

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Extensive scientific investigation is necessary because every government wants to construct smart cities. This is why examining how researchers approach this area of study is critical. This study investigates global research trends in smart cities and the Internet of Things (IoT) by analyzing 14,309 articles from the Scopus database (2010–2024). Using text mining and latent semantic analysis (LSA) via the KNIME tool, the research identifies key areas and trends in smart city development. The k-means clustering algorithm predicts future research directions, highlighting the most active countries, influential authors, and significant sources in the field. The results underscore the need for additional research in sectors where IoT applications for smart cities are still in their early stages. This analysis also offers insights into fostering international collaborations among institutions and researchers. The findings suggest that future research should focus on developing secure, scalable solutions to address challenges across various industries. Overall, this study provides a comprehensive overview of smart city research, offering valuable guidance for researchers and policymakers aiming to advance the integration of IoT technologies into urban environments. Article Highlights : This study identifies key global trends in smart city development and IoT integration using text mining and semantic analysis. Security and privacy challenges are significant concerns in the development of smart city technologies. The research emphasizes the importance of secure and scalable solutions to address urban challenges in smart city ecosystems. [ABSTRACT FROM AUTHOR]

One of the crucial issues for power grids in strengthening the urbanization around the world is imbalance between supply and demand, which leads the users to consume electricity in an anomalous manner without paying for it. Electricity theft plays a pivotal role in cutting down on the electricity bills. The existing data-oriented approaches for electricity theft detection (ETD) in the smart cities have limited ability to handle noisy high-dimensional data and features' associations. These limitations raise the misclassification rate, which makes some of the approaches unacceptable for electric utilities. A new twofold end-to-end methodology is proposed for ETD. In the first fold, it groups the similar electricity consumption (EC) cases through grey wolf optimization (GWO)-based clustering mechanism; clustering by fast search and find of density peaks (CFSFDP), we named it GC. In the second fold, a new relational stacked denoising autoencoder (RSDAE)-based semi-supervised generative adversarial network (GAN), termed as RGAN, is used for ETD. The combined methodology is named as GC-RGAN. In the methodology, RSDAE acts as both feature extraction technique and generator sub-model of the proposed RGAN. The proposed methodology utilizes the advantages of clustering, adversarial learning and semi-supervised EC data. Besides, to validate the effectiveness of the proposed solution, extensive simulations are performed using smart meter data. Simulation results validate the excellent ETD performance of the proposed GC-RGAN against existing ETD schemes, such as random forest and semi-supervised support vector machine. In comparison, GC-RGAN covers the ETD score of 98% that shows its suitability for real-world scenarios. The proposed solution has extraordinary performance for ETD as compared to traditional solutions, which shows its superiority and usefulness for real-world applications. [ABSTRACT FROM AUTHOR]

The security challenges faced by smart cities are attracting more attention from more people. Criminal activities and disasters can have a significant impact on the stability of a city, resulting in a loss of safety and property for its residents. Therefore, predicting the occurrence of urban events in advance is of utmost importance. However, current methods fail to consider the impact of road information on the distribution of cases and the fusion of information at different scales. In order to solve the above problems, an urban spatiotemporal event prediction method based on a convolutional neural network (CNN) and road feature fusion network (FFN) named CNN-rFFN is proposed in this paper. The method is divided into two stages: The first stage constructs feature map and structure of CNN then selects the optimal feature map and number of CNN layers. The second stage extracts urban road network information using multiscale convolution and incorporates the extracted road network feature information into the CNN. Some comparison experiments are conducted on the 2018–2019 urban patrol events dataset in Zhengzhou City, China. The CNN-rFFN method has an R2 value of 0.9430, which is higher than the CNN, CNN-LSTM, Dilated-CNN, ResNet, and ST-ResNet algorithms. The experimental results demonstrate that the CNN-rFFN method has better performance than other methods. [ABSTRACT FROM AUTHOR]

Smart urban planning needs to have a multicriteria-based approach to prevent the deteriorating local thermal climate. Maximizing the cooling potential using the available grey infrastructure would be the utmost priority of future smart cities. Remote sensing and GIS can be the appropriate tools to develop a climate-resilient urban planning framework. Studies are needed to include different features of vertical and horizontal landscaping to mitigate heat stress and enhance liveability at the city level. With this goal, the current work outlined a holistic approach to efficiently using green spaces with minimal reconstruction. The problem of regional climate threat was evaluated with urban heat island characterization. Moran's I clustering identified nearly 12% of the study area to be under considerable heat stress during summer days. Multiple techniques, such as mapping local climate zones, segment mean shift-based roof extraction, vegetation index computation, solar azimuth-based green wall site selection, etc., were applied to formulate solutions and provide an integrated method for city-level environment enhancement. A considerable area was identified as most suitable for green roof cover, and it was also computed that the transition towards green roof at only these locations may bring down the maximum heat island intensity by 0.74 °C. Additionally, solar zenith, illumination effect, and building height information were combined to create a distinct method where vertical plantation would flourish exceptionally. A rigorous assessment of more than 130 urban green spaces further quantified the relation between landscape geometry and cooling effect to provide optimum green space designs for future urban planning. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization and the continuous growth of transportation demand, the traffic management of smart city road networks has become increasingly complex and critical. Traffic flow prediction, as an important component of smart transportation systems, is of great significance for optimizing traffic planning and improving traffic efficiency. The study collected and preprocessed traffic data in the smart city road network, including multi-dimensional information such as traffic flow, road conditions, and meteorological data. Then, based on the idea of graph neural networks, we constructed the topological structure of the urban road network and abstracted elements such as roads and intersections into nodes, using edges to represent their connection relationships, thus forming a graph dataset. Next, we introduced an attention mechanism to extract more representative node features through the weighted aggregation of node features, thereby achieving effective modeling of urban road network traffic flow. During the model training phase, we used real traffic datasets for experimental verification and integrated various information such as time, space, and road features into the model. The experimental results show that compared to traditional methods, this research prediction method has achieved better performance in traffic flow prediction tasks, with higher prediction accuracy and robustness. It has stronger applicability and effectiveness in different traffic scenarios. By integrating multi-dimensional information and introducing attention mechanisms, this method has significant advantages in improving the accuracy and robustness of traffic flow prediction, and has important practical significance and application prospects for the construction of smart transportation systems and the development of smart cities. [ABSTRACT FROM AUTHOR]

Object detection, as a crucial aspect of computer vision, plays a vital role in traffic management, emergency response, autonomous vehicles, and smart cities. Despite the significant advancements in object detection, detecting small objects in images captured by high-altitude cameras remains challenging, due to factors such as object size, distance from the camera, varied shapes, and cluttered backgrounds. To address these challenges, we propose small object detection YOLOv8 (SOD-YOLOv8), a novel model specifically designed for scenarios involving numerous small objects. Inspired by efficient generalized feature pyramid networks (GFPNs), we enhance multi-path fusion within YOLOv8 to integrate features across different levels, preserving details from shallower layers and improving small object detection accuracy. Additionally, we introduce a fourth detection layer to effectively utilize high-resolution spatial information. The efficient multi-scale attention module (EMA) in the C2f-EMA module further enhances feature extraction by redistributing weights and prioritizing relevant features. We introduce powerful-IoU (PIoU) as a replacement for CIoU, focusing on moderate quality anchor boxes and adding a penalty based on differences between predicted and ground truth bounding box corners. This approach simplifies calculations, speeds up convergence, and enhances detection accuracy. SOD-YOLOv8 significantly improves small object detection, surpassing widely used models across various metrics, without substantially increasing the computational cost or latency compared to YOLOv8s. Specifically, it increased recall from 40.1% to 43.9%, precision from 51.2% to 53.9%, mAP0.5 from 40.6% to 45.1%, and mAP0.5:0.95 from 24% to 26.6%. Furthermore, experiments conducted in dynamic real-world traffic scenes illustrated SOD-YOLOv8's significant enhancements across diverse environmental conditions, highlighting its reliability and effective object detection capabilities in challenging scenarios. [ABSTRACT FROM AUTHOR]

Today's increasingly complex energy systems require innovative approaches to integrate and optimize different energy sources and technologies. In this paper, we explore the system of systems (SoS) approach, which provides a comprehensive framework for improving energy systems' interoperability, efficiency, and resilience. By examining recent advances in various sectors, including photovoltaic systems, electric vehicles, energy storage, renewable energy, smart cities, and rural communities, this study highlights the essential role of SoSs in addressing the challenges of the energy transition. The principal areas of interest include the integration of advanced control algorithms and machine learning techniques and the development of robust communication networks to manage interactions between interconnected subsystems. This study also identifies significant challenges associated with large-scale SoS implementation, such as real-time data processing, decision-making complexity, and the need for harmonized regulatory frameworks. This study outlines future directions for improving the intelligence and autonomy of energy subsystems, which are essential for achieving a sustainable, resilient, and adaptive energy infrastructure. [ABSTRACT FROM AUTHOR]

The performance of heat pump systems for heating and cooling heavily relies on the thermal conditions of their reservoirs. This study introduces a novel thermal reservoir, detailing a 2017 project where the Municipality of Stavanger installed a heat exchanger system on the wall of a main wastewater tunnel beneath the city center. It provides a comprehensive account of the system's design, installation, and performance, and presents an Artificial Neural Network (ANN) model that predicts heat pump capacity, electricity consumption, and outlet temperature across seasonal variations in wastewater temperatures. By integrating domain knowledge with the ANN, this study demonstrates the model's capability to detect anomalies in heat pump operations effectively. The network also confirms the consistent performance of the heat exchangers from 2020 to 2024, indicating minimal fouling impacts. This study establishes wastewater heat exchangers as a safe, effective, and virtually maintenance-free solution for heat extraction and rejection. [ABSTRACT FROM AUTHOR]

The rapid urbanization trend underscores the need for effective management of city resources and services, making the concept of smart cities increasingly important. This study leverages the IMD Smart City Index (SCI) dataset to analyze and rank smart cities worldwide. Our research has a dual objective: first, we aim to apply a set of unsupervised learning models to cluster cities based on their smartness indices. Second, we aim to employ supervised learning models such as random forest, support vector machines (SVMs), and others to determine the importance of various features that contribute to a city's smartness. Our findings reveal that while smart living was the most critical factor, with an importance of 0.259014. Smart mobility and smart environment also played significant roles, with the importance of 0.170147 and 0.163159, respectively, in determining a city's smartness. While the clustering provides insights into the similarities and groupings among cities, the feature importance analysis elucidates the critical factors that drive these classifications. The integration of these two approaches aims to demonstrate that understanding the similarities between smart cities is of limited utility without a clear comprehension of the importance of the underlying features. This holistic approach provides a comprehensive understanding of what makes a city 'smart' and offers a robust framework for policymakers to enhance urban living standards. [ABSTRACT FROM AUTHOR]

The influence of mobility modes within Positive Energy Districts (PEDs) has gained limited attention, despite their crucial role in reducing energy consumption and greenhouse gas emissions. Buildings in the European Union (EU) account for 40% of energy consumption and 36% of greenhouse gas emissions. In comparison, transport contributes 28% of energy use and 25% of emissions, with road transport responsible for 72% of these emissions. This study aims to design and optimize a synthetic PED in Istanbul that integrates renewable energy sources and public mobility systems to address these challenges. The renewable energy sources integrated into the synthetic PED model include solar energy, hydrogen energy, and regenerative braking energy from a tram system. Solar panels provided a substantial portion of the energy, while hydrogen energy contributed to additional electricity generation. Regenerative braking energy from the tram system was also utilized to further optimize energy production within the district. This system powers a middle school, 10 houses, a supermarket, and the tram itself. Optimization techniques, including Linear Programming (LP) for economic purposes and the Weighted Sum Method (WSM) for environmental goals, were applied to balance cost and CO2 emissions. The LP method identified that the PED model can achieve cost competitiveness with conventional energy grids when hydrogen costs are below $93.16/MWh. Meanwhile, the WSM approach demonstrated that achieving a minimal CO2 emission level of 5.74 tons requires hydrogen costs to be $32.55/MWh or lower. Compared to a conventional grid producing 97 tons of CO2 annually, the PED model achieved reductions of up to 91.26 tons. This study contributes to the ongoing discourse on sustainable urban energy systems by addressing key research gaps related to the integration of mobility modes within PEDs and offering insights into the optimization of renewable energy sources for reducing emissions and energy consumption. [ABSTRACT FROM AUTHOR]

Considering human influence and its negative impact on the environment, the world will have to transform the current energy system into a cleaner and more sustainable one. In residential as well as office buildings, there is a demand to minimize electricity consumption, improve the automation of electrical appliances and optimize electricity utilization. This paper describes the implementation of a smart switch with extended facilities compared to traditional switches, such as visual indication of evacuation routes in case of fire and acoustic alerts for emergencies. The proposed embedded system implements Modbus RTU serial communication to receive information from a fire alarm-control panel. An extension to the Modbus communication protocol, called Modbus Extended (ModbusE), is also proposed for smart switches and emergency switchboards. The embedded smart switch described in this paper as a scientific and practical contribution in this field, based on a performant microcontroller system, is integrated into the Building Internet of Things (BIoT) concept and uses the innovative ModbusE protocol. The proposed smart lighting system integrates building lighting access control for smart switches and sockets and can be extended to incorporate functionality for smart thermostats, access control and smart sensor-based information acquisition. [ABSTRACT FROM AUTHOR]

The transport sector emits 18% of global CO2. Industry and consumers must adopt green mobility to reduce emissions and climate change. This will help achieve sustainability by improving efficiency and reducing greenhouse gas emissions. Thus, smart electric vehicles (SEVs) have emerged. Digital twins concept and technology may help launch SEVs to the market by analysing and optimising supporting infrastructure. This work aims to fill in the gaps between different pieces of research by giving a full review from a technical and scientifically neutral point of view. The study looks at how digital twin technology can be used in smart car systems by looking at its promise and the hurdles faced. Based on a comprehensive literature survey, this is the first in-depth look at how digital twin technology can be used in smart electric cars. The review has been organised into specific areas of the smart vehicle system, such as drive train system battery management system, driver assistance system, vehicle health monitoring system, vehicle power electronics. This review goes into detail about each component of the car to provide an overall view of the smart vehicle system as a whole. This review makes it easier to understand how digital twin technology can be utilized into each area from a scientific point of view. Lastly, the work looks at the technological and economic impact of digital twin technology, which will make considerable changes in car manufacturing processes, as well as help address current obstacles in utilizing advanced technologies. [ABSTRACT FROM AUTHOR]

The "Metaverse" is evaluated as having significant potential in a "Smart city" design and operation. Despite growing interest, there is still a lack of comprehensive quantitative analysis on the "Metaverse", particularly in the context of smart cities. This study conducts a bibliometric analysis of 604 articles selected from the "WoS" database and employs three dimensions of technology, people, and institutions as a balanced perspective on smart cities, providing a comprehensive understanding of research trends on the "Metaverse" in the context of smart cities. This study identifies the "Metaverse" as a Virtual reality technology, popular since 2021, and provides information on the active years, countries, fields, journals, authors, and institutions involved in "Metaverse" research on smart cities. This study also identifies three stages of research development as follows: Stage 1 (2007–2013) to Stage 2 (2014–2020) and Stage 3 (2021–20 October 2023), revealing the research focus evolution from basic "urban planning" to complex "urban governance" and "Smart city" construction with consideration of multi-stakeholders' perspectives. Additionally, this study reveals that "Metaverse" research studies on the "technology" dimension have consistently outnumbered that on "institutions" and "people" across all stages in the "Smart city" domain. These findings address current theoretical gaps and offer a foundation for future research. [ABSTRACT FROM AUTHOR]

This review paper explores Urban Digital Twins (UDTs) and their crucial role in developing smarter cities, focusing on making urban areas more sustainable and well-planned. The methodology adopted an extensive literature review across multiple academic databases related to UDTs in smart cities, sustainability, and urban environments, conducted by a bibliometric analysis using VOSviewer to identify key research trends and qualitative analysis through thematic categorization. This paper shows how UDTs can significantly change how cities are managed and planned by examining examples from cities like Singapore and Dubai. This study points out the main hurdles like gathering data, connecting systems, handling vast amounts of information, and making different technologies work together. It also sheds light on what is missing in current research, such as the need for solid rules for using UDTs effectively, better cooperation between various city systems, and a deeper look into how UDTs affect society. To address research gaps, this study highlights the necessity of interdisciplinary collaboration. It also calls for establishing comprehensive models, universal standards, and comparative studies among traditional and UDT methods. Finally, it encourages industry, policymakers, and academics to join forces in realizing sustainable, smart cities. [ABSTRACT FROM AUTHOR]

The rapid evolution of digital technologies has revolutionized the architecture, engineering, and construction (AEC) industry, driving the wide-spread adoption of digital twins for structures. These virtual replicas, developed using Building Information Modeling (BIM) methodology, incorporate extensive information databases, proving indispensable for enhancing project management throughout a structure's entire lifecycle and towards smart city development. As the impact of climate change continues to grow, hazardous weather alerts play a critical role as an early-warning system that notifies stakeholders of imminent threats, thereby influencing decision-making processes in construction projects. Surprisingly, despite its evident value, the integration of alert systems for hazardous weather conditions into BIM is often overlooked. To fill this gap, this paper proposes Robot Process Automation (RPA) protocols to automate the integration of real-time weather parameters into a structure's BIM models. These very protocols are also used as alert systems, enabling the timely notification of stakeholders in the event of detected hazardous weather conditions. The effectiveness of the proposed methodology is demonstrated through its practical application in enhancing the safety of an actual building in Viña del Mar, Chile. [ABSTRACT FROM AUTHOR]

Many countries encourage their populations to use public urban transport to decrease pollution and traffic congestion. However, this can generate overcrowded routes at certain times and low economic efficiency for public urban transport companies when buses carry few passengers. This article proposes a Public Urban Transport Scheduling System (PUTSS) algorithm for allocating a public urban transport fleet based on the number of passengers waiting for a bus and considering the efficiency of public urban transport companies. The PUTSS algorithm integrates artificial intelligence (AI) methods to identify the number of people waiting at each station through real-time image acquisition. The technique presented is Azure Computer Vision. In a case study, the accuracy of correctly identifying the number of persons in an image was computed using the Microsoft Azure Computer Vision service. The proposed PUTSS algorithm also uses Google Maps Service for congestion-level identification. Employing these modern tools in the algorithm makes improving public urban transport services possible. The algorithm is integrated into a software application developed in C#, simulating a real-world scenario involving two public urban transport vehicles. The global accuracy rate of 89.81% demonstrates the practical applicability of the software product. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel demand-side management (DSM) system designed to optimize electric vehicle (EV) charging at public stations using model predictive control (MPC). The system adjusts to real-time grid conditions, electricity prices, and user preferences, providing a dynamic approach to energy distribution in smart city infrastructures. The key focus of the study is on reducing peak loads and enhancing grid stability, while minimizing charging costs for end users. Simulations were conducted under various scenarios, demonstrating the effectiveness of the proposed system in mitigating peak demand and optimizing energy use. Additionally, the system's flexibility enables the adjustment of charging schedules to meet both grid requirements and user needs, making it a scalable solution for smart city development. However, current limitations include the assumption of uniform tariffs and the absence of renewable energy considerations, both of which are critical in real-world applications. Future research will focus on addressing these issues, improving scalability, and integrating renewable energy sources. The proposed framework represents a significant step towards efficient energy management in urban settings, contributing to both cost savings and environmental sustainability. [ABSTRACT FROM AUTHOR]

Numerous investigations and research projects carried out over the past several years in a wide range of application domains have revealed the potential of IoT (Internet of Things). Solar energy is a renewable source of energy and a sustainable foundation for human civilization; thus, the use of IoT with solar energy-powered devices has definitely been a revolutionary reformation in technology. Researchers have looked into ways to use IoT to change the network structure by recognizing different ecosystem components for intelligent solar-powered city control. Furthermore, countless studies have been made on solar monitoring and solar tracking systems using different IoT technologies in order to target better efficiency, automated control, and monitoring, maximum energy generation etc. The contribution of this study is to make aware everyone about the integration of the renewable energy with the revolution 4.0 which is the major primary topic of the current technology nowadays. Also, the major projects like smart city are being pursued in many developing countries like India and so concepts like IoT are mandatory for these major projects. Also, the solar panel efficiency may be increased and maintenance expenses decreased with the help of the Internet of Things in monitoring and optimizing the panels. As this technology may aid in managing energy usage in real time, solar power can be more consistent and adaptable to fluctuating demand. This article provides a state-of-the-art review of the application of IoT in effective solar energy utilization. The use of IoT in solar energy tracking, power point tracking, energy harvesting, smart lighting system, PV panels, smart irrigation system, solar inverters, etc., is reviewed. Hence, by merging solar power with the Internet of Things, we can provide companies and households with long-term, affordable energy solutions that help encourage responsible expansion and a better future. The outcome of this study reveals that IoT is very much successful in providing smart and efficient solar energy output from countless devices. A vast scope of work and research on IoT applications for smart solar energy utilization still exists in the future. [ABSTRACT FROM AUTHOR]

Smart cities are hubs of innovation and rapid development, where technology plays a significant role in shaping the urban environment. In such settings, the ability to adapt and think flexibly is crucial for individuals to thrive. The study's goal is to understand which factors influence how well individuals are equipped with 21st‐century skills and how they apply them in the dynamic context of modern urban living. Employing a quantitative research approach, data were initially collected from 600 Thai respondents through convenience sampling. A subsequent data‐cleansing process refined the focus to 568 respondents, selected based on their high‐score attainment. The study utilized percentages, means, and binary logistic regression in a comprehensive data analysis. Its key findings illuminate the influence of various factors, including score, age, career trajectory, and engagement with social media platforms like Instagram, LINE, and X, on skill development within the smart city context. This research offers a nuanced perspective on the myriad elements that foster individual and collective success in technologically advanced urban environments. It moves beyond mere acknowledgment of innate abilities or age‐related wisdom, highlighting the critical roles of career advancement and digital engagement in skill enhancement. The implications of these findings are far reaching, especially for policymakers, educators, and industry leaders. They underscore the need for a comprehensive approach to shaping the future of urban living and working spaces, ensuring that individuals are not just equipped with the necessary skills but are also adept at applying them effectively in the smart cities of tomorrow. This study serves as a guide for these stakeholders, emphasizing the importance of fostering environments that support continuous learning, innovation, and adaptability in the face of technological advancements. [ABSTRACT FROM AUTHOR]

With the increasing reliance and demand for artificial intelligence in the construction of smart cities, AI has been embedded in various applications and infrastructures. However, uncertainties, concerns, and even fears about AI are constantly emerging. This study based on actor‐network theory (ANT) showcases the application practices of artificial intelligence in smart city, including smart transportation, smart water management, smart healthcare, smart grids, and smart city evaluation standards, and analyzes the process of constructing an actor‐network involving governments, technology development companies, and urban residents. It demonstrates the interactive network processes such as problem statement, interest alignment, recruitment, and mobilization. It explores the relationships among humans, humans and technology, and the self‐challenges faced by technology, such as digital ethics, machine ethics, and relational alienation. In the future, it is necessary to establish explainable, transparent, safe, and responsible AI in the development of smart cities, grant certain rights, and respect to nonhuman actors such as data, AI, and machines, and formulate a series of laws and ethical guidelines to ensure the sustainable development of smart city. [ABSTRACT FROM AUTHOR]

The aim of the paper was to identify which European capitals are sustainable and smart, why, and what influences the ranking. The main research hypothesis was to indicate that cities in the 'old' E.U. countries (richer and with higher levels of economic development) are more sustainable and smart. Furthermore, sustainable smart cities, by definition, through the use of advanced and modern management tools and technological support, should contribute to community resilience. Sustainable energy plays a significant role in the measurement system. The study's results showed the differences that exist across countries, as well as the leaders in each smart category and area. This is interesting and new; from a research point of view, there has been no study based on OECD research and data confronting and correlating the range of data with indicators found in the literature. The study results show that the concept of a smart city is comprehensive and that it is necessary to analyze in depth the various sub-categories included in the measurement and assessment of smartness offered by different indicators. This is because it turns out that an overall score and ranking do not always mean that a city is smart in every area and every element included in smart. Statistical methods and literature analysis are used for the study. The results represent a novel development and contribution to the science discipline and can be the basis for further scientific exploration in this area. The research gap and challenge indicate whether there is a link and correlation between the use of sustainable energy in E.U. countries and the implementation of smart concepts in European capitals in the context of the division into 'new' and 'old' E.U. capitals. An important element is the verification of the thesis that 'old' capitals are more advanced in the implementation of smart cities and make greater use of sustainable energy to meet social and economic needs. The thesis has been partly falsified and confirmed negatively; the results are not obvious. It means that the 'new' E.U. countries are very skillful in using financial, organizational, and common development policy opportunities to make their cities modern, intelligent, and friendly to their inhabitants. [ABSTRACT FROM AUTHOR]

The success of developing and implementing Smart Cities (SC) projects depends on a varied set of factors, where the availability of a qualified technical workforce is a critical one. The combination of ICT requirements, like the effectiveness and quality of solutions merging IoT, cloud computing, sensors, and communications with the work from many varied disciplines (e.g., civil engineering, architecture, etc.), mixed with aspects of environmental and business sustainability, makes the management of these projects really challenging. Reports forecast a scarcity of qualified candidates, given this complexity and the growth of activity in SC projects. The European project SMACITE has addressed the requirements of the qualification of an ICT workforce with an analysis of multiples sources of information from the labor market, feedback from involved stakeholders, and the literature. The goal was the development of two occupational ICT profiles as a reference for training and for the availability of candidates for job vacancies. The result is two ICT role profiles for engineers and technicians, mapped with the European skills frameworks ESCO and EN16234. The profiles determined the whole set of requirements, including not only the technical areas and soft skills, but also additional technical areas and sustainability and managerial skills and the analysis of different sources of information. Our work has also determined which existing ESCO occupations are similar to the two reference profiles, so they are better adapted to SC projects. The training activities of SMACITE have also suggested the amount of training expected for a varied sample of candidates who want to be qualified for SC projects. [ABSTRACT FROM AUTHOR]

This paper addresses the growing importance of efficient street lighting management, driven by rising electricity costs and the need for municipalities to implement cost-effective solutions. Central to this study is the UNI 11248 Italian regulation, which extends the European EN 13201-1 standard introduced in 2016. These standards provide guidelines for designing, installing, operating, and maintaining lighting systems in pedestrian and vehicular traffic areas. Specifically, the UNI 11248 standard introduces the possibility to dynamically adjust light intensity through two alternative operating modes: (a) Traffic Adaptive Installation (TAI), which dims the light based solely on real-time traffic flow measurements; and (b) Full Adaptive Installation (FAI), which, in addition to traffic measurements, also requires evaluating road surface luminance and meteorological conditions. In this paper, we first present the general architecture and operation of an FAI-enabled lighting infrastructure, which relies on environmental sensors and a heterogeneous wireless communication network to connect intelligent, remotely controlled streetlights. Subsequently, we examine large-scale, in-field FAI infrastructures deployed in Vietnam and Italy as case studies, providing substantial measurement data. The paper offers insights into the measured energy consumption of these infrastructures, comparing them to that of conventional light-control strategies used in traditional installations. The measurements demonstrate the superiority of FAI as the most efficient solution. [ABSTRACT FROM AUTHOR]

An innovative decision matrix has been developed to guide the selection and implementation of Pneumatic Urban Solid Waste Collection Systems (PUSWCS) in smart city projects. This study comprehensively collects and analyzes data on the advantages and disadvantages of pneumatic collection systems from technical, economic, and social perspectives. A decision-making tool was created to address the complexities of evaluating the desirability of incorporating PUSWCS in municipalities or specific areas, using a holistic approach. The tool assesses the technical, economic, and social feasibility of implementing PUSWCS, aligning it with the United Nations' Sustainable Development Goals (SDGs). Specific variables are measured to assess compliance with the SDGs, distinguishing technical aspects from economic and social aspects. The methodology includes surveys of system users and technicians, expert assessments, and the development of a decision matrix that cross-references study variables and SDGs. The matrix assigns numerical values to the Magnitude (M) and Impact (I) of each variable, enabling quantitative interpretation. This holistic approach accommodates the complexities of waste management and diverse stakeholder perspectives. The results demonstrate the matrix's effectiveness in accurately assessing the desirability of implementing PUSWCS. This confirms the matrix's ability to optimally integrate with innovative smart city concepts and align with long-term sustainability goals. The study concludes that the design of the decision matrix allows the collection of information from experts, users, and stakeholders about economic, social, and environmental variables and relates them to the SDGs, to obtain a numerical result that allows to decide whether in a given urban environment it is advisable to implement a PUSWCS. [ABSTRACT FROM AUTHOR]

Featured Application: Potential applications of the work include novel ML-based systems for sustainable smart cities and smart territory control. The rapid development of modern information technology (IT), power supply, communication and traffic information systems and so on is resulting in progress in the area of distributed and energy-efficient (if possible, powered by renewable energy sources) smart grid components securely connected to entire smart city management systems. This enables a wide range of applications such as distributed energy management, system health forecasting and cybersecurity based on huge volumes of data that automate and improve the performance of the smart grid, but also require analysis, inference and prediction using artificial intelligence. Data management strategies, but also the sharing of data by consumers, institutions, organisations and industries, can be supported by edge clouds, thus protecting privacy and improving performance. This article presents and develops the authors' own concept in this area, which is planned for research in the coming years. The paper aims to develop and initially test a conceptual framework that takes into account the aspects discussed above, emphasising the practical aspects and use cases of the Social Internet of Things (SIoT) and artificial intelligence (AI) in the everyday lives of smart sustainable city (SSC) residents. We present an approach consisting of seven algorithms for the integration of large data sets for machine learning processing to be applied in optimisation in the context of smart cities. [ABSTRACT FROM AUTHOR]

Security is an important consideration when delivering information-aware messages to vehicles that are far away from the current location of the information-sending vehicle. This information helps the receiver to save fuel and time by making wise decisions to avoid damaged or blocked roads. To ensure the safety and security of this type of information using blockchain technology, we propose a new cognitive vehicular communication scheme to transfer messages from source to destination. Due to spectrum scarcity in vehicular networks, there needs to be a wireless medium available for every communication link since vehicles require it to communicate. The primary user (PU) makes a public announcement about a free channel to all secondary users nearby and only gives it to authentic vehicles. The authenticity of vehicles is guaranteed by a roadside unit (RSU) that offers secure keys to any vehicle that joins this blockchain network. Those who participate in this network must pay a certain amount and receive rewards for their honesty that exceed the amount spent. To test the performance of various parameters, the proposed scheme utilizes the Ethereum smart contract and compares them to blockchain and non-blockchain methods. Our results show a minimum delivery time of 0.16 s and a minimum overhead of 350 bytes in such a dynamic vehicle environment. [ABSTRACT FROM AUTHOR]

The caching mechanism of federated learning in smart cities is vital for improving data handling and communication in IoT environments. Because it facilitates learning among separately connected devices, federated learning makes it possible to quickly update caching strategies in response to data usage without invading users' privacy. Federated learning caching promotes improved dynamism, effectiveness, and data reachability for smart city services to function properly. In this paper, a new caching strategy for Named Data Networking (NDN) based on federated learning in smart cities' IoT contexts is proposed and described. The proposed strategy seeks to apply a federated learning technique to improve content caching more effectively based on its popularity, thereby improving its performance on the network. The proposed strategy was compared to the benchmark in terms of the cache hit ratio, delay in content retrieval, and energy utilization. These benchmarks evidence that the suggested caching strategy performs far better than its counterparts in terms of cache hit rates, the time taken to fetch the content, and energy consumption. These enhancements result in smarter and more efficient smart city networks, a clear indication of how federated learning can revolutionize content caching in NDN-based IoT. [ABSTRACT FROM AUTHOR]

Secure and scalable group communication environments are essential for many IoT applications as they are the cornerstone for different IoT devices to work together securely to realize smart applications such as smart cities or smart health. Such applications are often implemented in Wireless Sensor Networks, posing additional challenges. Sensors usually have low capacity and limited network connectivity bandwidth. Over time, a variety of Secure Group Communication (SGC) schemes have emerged, all with their advantages and disadvantages. This variety makes it difficult for users to determine the best protocol for their specific application purpose. When selecting a Secure Group Communication scheme, it is crucial to know the model's performance under varying network conditions. Research focused so far only on performance in terms of server and client runtimes. To the best of our knowledge, we are the first to perform a network-based performance analysis of SGC schemes. Specifically, we analyze the network impact on the two centralized SGC schemes SKDC and LKH and one decentralized/contributory SGC scheme G-DH. To this end, we used the ComBench tool to simulate different network situations and then measured the times required for the following group operations: group creation, adding and removing members. The evaluation of our simulation results indicates that packet loss and delay influence the respective SGC schemes differently and that the execution time of the group operations depends more on the network situations than on the group sizes. [ABSTRACT FROM AUTHOR]

The advent of smart cities has revolutionized urban living by providing innovative solutions, such as smart homes, smart hospitals, and smart parking. These smart applications have made life easier for people by improving infrastructure and accessibility. However, the development of smart cities also poses significant challenges for cybersecurity. The smooth operation of smart applications is essential to ensure the well‐being of users, and any disruption caused by cyber‐attacks can lead to critical situations. Malware, malicious software that can cause harm to devices or systems, is the most common type of cyber‐attack. Smart applications may consist of various heterogeneous devices, each with different security requirements and specifications, making it difficult to present an efficient mechanism against malicious software for all devices within different smart applications. Hence, developing a flexible and efficient solution to overcome this challenge is vital. This research presents a framework termed as Secure Software Update for the Internet‐of‐Things (SSUIT), which is designed to protect IoT devices from malicious software updates. This framework includes three primary components: publishers hosted on the cloud platform, an intelligent broker implemented on edge devices, and IoT devices as the subscribers. The publishers send software updates to the intelligent broker, which detects whether the update is malicious or not. The intelligent broker includes a secure software engine that integrates a disassembler, a preprocessor, and predictive models to detect malicious software. The predictive models are designed by taking into account the resource‐constrained nature of IoT systems. The end‐to‐end time taken for complete execution of a software update is also reported. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITS) are globally installed in smart cities, which enable the next generation of ITS depending on the potential integration of autonomous and connected vehicles. Both technologies are being tested widely in various cities across the world. However, these two developing technologies are vital in allowing a fully automatic transportation system; it is necessary to automate other transportation and road components. Unmanned aerial vehicles (UAVs) or drones are utilized for many surveillance applications in the ITS. Detecting on-ground vehicles in drone images is significant for disaster rescue operations, traffic and parking management, and navigating uneven territories. This study presents a flying foxes optimization with deep learning-based vehicle detection and classification model on aerial images (FFODL-VDCAI) technique for ITS application. The main objective of the FFODL-VDCAI technique is to automate and accurately classify vehicles that exist in aerial images. Three primary processes are involved in the presented FFODL-VDCAI technique. Initially, the FFODL-VDCAI approach utilizes YOLO-GD (Ghost-Net and Depthwise convolution) for vehicle detection, where the YOLO-GD uses lightweight Ghost Net in place on the backbone network of YOLO-v4 and interchanges the conventional convolutional with depthwise separable convolutional and pointwise convolutional. Next, the FFO technique is used for hyperparameter tuning the Ghost Net technique. Finally, a deep Q-network (DQN) based reinforcement learning technique is used to classify detected vehicles effectively. A comprehensive simulation analysis of the FFODL-VDCAI methodology is conducted on the UAV image dataset. The performance validation of the FFODL-VDCAI methodology exhibited superior values of 96.15% and 92.03% under PSU and Stanford datasets concerning various aspects. [ABSTRACT FROM AUTHOR]

The construction models of smart cities and low-carbon cities are crucial for advancing global urbanization, enhancing urban governance, and addressing major urban challenges. Despite significant advancements in smart and low-carbon city research, a consensus on their coupling coordination remains elusive. This study employs mixed-method research, combining qualitative and quantitative analyses, to investigate the coupling coordination between urban smart performance (SCP) and low-carbon level (LCL) across 52 typical smart and low-carbon pilot cities in China. Independent evaluation models for SCP and LCL qualitatively assess the current state of smart and low-carbon city construction. Additionally, an Entropy–TOPSIS–Pearson correlation–Coupling coordination degree (ETPC) analysis model quantitatively examines their relationship. The results reveal that smart city initiatives in China significantly outperform low-carbon city development, with notable disparities in SCP and LCL between eastern, non-resource-based, and central cities versus western, resource-dependent, and peripheral cities. A strong positive correlation exists between urban SCP and overall LCL, with significant correlations in management, society, and economy, and moderate to weak correlations in environmental quality and culture. As SCP levels improve, the coupling coordination degree between the urban SCP and LCL systems also increases, driven primarily by economic, management, and societal factors. Conversely, the subsystems of low-carbon culture and environmental quality show poorer integration. Based on these findings, this study proposes an evaluation system for smart and low-carbon coupling coordination development, outlining pathways for future development from the perspective of urban complex systems. [ABSTRACT FROM AUTHOR]

Due to the rapidly developing technologies, fast and practical solutions are offered to the problems encountered in daily life. Metropolitan cities are greatly affected by the ever-increasing population and migrations to big cities, the increase in production with the economy and job opportunities. At this point, with the introduction of smart transportation systems, fast and effortless solutions can be produced by saving time and space. City life can be facilitated by applying more efficient and rational solutions with smart transportation systems. In this study, it is aimed to investigate information about the Intelligent Transportation Systems and one of its applications, park and ride, which has created a significant agenda within the scope of transportation engineering in the recent past, and to provide information about the investments made by examining the application for Istanbul along with its various applications in the world. Some suggestions will be made by emphasizing the importance of the park and Ride smart city application for Istanbul. In conclusion, predictions of P + R application and energy consumption in periods of 1-24 months were made through machine learning. By obtaining energy consumption data thanks to machine learning, carbon gas emissions and its effects on greenhouse gases were also examined. It can be thought that by obtaining energy consumption data for the long term thanks to machine learning, it can make significant contributions to future investments, green environment-green world, and climate change studies. [ABSTRACT FROM AUTHOR]

This article aims to conduct a comparative analysis between the University of the Third Age (UNATI/UEM) and the Brazilian Charter for Smart Cities. To achieve this objective, we conducted qualitative research of a documentary nature, identifying convergent aspects between the two documents. The results reveal six key points of convergence: the emphasis on quality of life for the elderly, the planning of social actions to support this demographic, access to cultural and educational services, encouragement of technological advancements, the training of professionals to care for the elderly through teaching, research, and outreach, and healthcare provisions for the elderly. The study concludes that developing aging policies and training human resources for elderly care can reshape perceptions of aging, ultimately enhancing the quality of life for the elderly. [ABSTRACT FROM AUTHOR]

This research explores the efficacy of the YOLOR (You Only Learn One Representation) algorithm integrated with the Deep Sort algorithm for real-time vehicle detection, classification, and counting in Morelia, Mexico. The study aims to enhance traffic monitoring and management by leveraging advanced deep learning techniques. The methodology involves deploying the YOLOR model at six key monitoring stations, with varying confidence levels and pre-trained weights, to evaluate its performance across diverse traffic conditions. The results demonstrate that the model is effective compared to other approaches in classifying multiple vehicle types. The combination of YOLOR and Deep Sort proves effective in tracking vehicles and distinguishing between different types, providing valuable data for optimizing traffic flow and infrastructure planning. This innovative approach offers a scalable and precise solution for intelligent traffic management, setting new methodologies for urban traffic monitoring systems. [ABSTRACT FROM AUTHOR]