Search

An experimental demonstration of directivities exceeding the fundamental Kildal limit, a phenomenon called superdirectivity, is provided for spherical high-index dielectric antennas with an electric dipole excitation. A directivity factor of about 10 with a total efficiency of more than 80% for an antenna having a size of a third of the wavelength was measured. High directivities are shown to be associated with constructive interference of particular electric and magnetic modes of an open spherical resonator. Both analytic solutions for a point dipole and a full-wave rigorous simulation for a realistic dipole antenna were employed for optimization and analysis, yielding an excellent agreement between experimentally measured and numerically predicted directivities. The use of high-index low-loss ceramics can significantly reduce the physical size of such antennas while maintaining their overall high radiation efficiency. Such antennas can be attractive for various high-frequency applications, such as antennas for the Internet of Things, smart city systems, 5G network systems, and others. The demonstrated concept can be scaled in frequency. [ABSTRACT FROM AUTHOR]

The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of distributed intelligence are promising research paradigms in the design of smart cities. IoT and distributed intelligence are conjoined. On the one hand, IoT provides a digital connection to everyday physical devices, and on the other hand, distributed intelligence allows these connected devices to work together in a distributed environment to solve complex problems through learning and reasoning. In this article, we investigate and report the recent premier advances in distributed intelligence for IoT-based smart cities. We use bibliometric techniques which provide essential insights into many studies to evaluate the literature including publication year, type, and citation status on distributed intelligence and its variations published between 2013 and 2023. We categorize and classify literature by devising a taxonomy based on essential parameters, technologies, approaches, applications, and implications with respect to distributed intelligence. Moreover, we provide recent ongoing advancements in distributed intelligence and IoT such as sustainability, blockchain, 5G technologies, AI, edge computing expansion and IoT expansion. We also highlight some of the use cases which include traffic management, energy management, waste management, public safety, and healthcare. We provide core layers and services for implementing distributed intelligence in IoT-based smart cities and present several research challenges and issues. [ABSTRACT FROM AUTHOR]

Currently, over 60% of the world's population lives in cities. Urban living has many advantages but there are also challenges regarding the need for smart urbanization. The next generation of tunable 2D nanomaterials, called MXenes, is the critical enabling technology that can bring the current urban thinking to the next level, called a smart city. The smart city is a novel concept based on a framework of self‐sufficient technologies that are interactive and responsive to citizens’ needs. In this perspective, MXene‐enabled technologies for sustainable urban development are discussed. They can advance self‐sufficient, adaptive, and responsive buildings that can minimize resource consumption, solving the deficiency of essential resources such as clean energy, water, and air. MXenes are at the cutting edge of technological limitations associated with the Internet of Things (IoT) and telemedicine, combining diverse properties and offering multitasking. It is foreseen that MXenes can have a bright future in contributing to the smart city concept. Therefore, the roadmap is presented for demonstrating the practical feasibility of MXenes in the smart city. Altogether, this study promotes the role of MXenes in advancing the well‐being of citizens by raising the quality of urban living to the next level. [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]

Cybercriminals have become an imperative threat because they target the most valuable resource on earth, data. Organizations prepare against cyber attacks by creating Cyber Security Incident Response Teams (CSIRTs) that use various technologies to monitor and detect threats and to help perform forensics on machines and networks. Testing the limits of defense technologies and the skill of a CSIRT can be performed through adversary emulation performed by so-called "red teams". The red team's work is primarily manual and requires high skill. We propose SpecRep, a system to ease the testing of the detection capabilities of defenses in complex, heterogeneous infrastructures. SpecRep uses previously known attack specifications to construct attack scenarios based on attacker objectives instead of the traditional attack graphs or a list of actions. We create a metalanguage to describe objectives to be achieved in an attack together with a compiler that can build multiple attack scenarios that achieve the objectives. We use text processing tools aided by large language models to extract information from freely available white papers and convert them to plausible attack specifications that can then be emulated by SpecRep. We show how our system can emulate attacks against a smart home, a large enterprise, and an industrial control system. [ABSTRACT FROM AUTHOR]

Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM1, PM2.5, and PM10 particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data. [ABSTRACT FROM AUTHOR]

This investigation is part of a topical situation where wireless equipment is gradually being used for energy transfer, particularly for autonomous systems and the use of decarbonized energies. A characteristic example of decarbonized autonomous use is linked to the substitution of thermal engine vehicles for electric vehicles (EVs) equipped with energy storage batteries. This response was considered in an ecological context of reducing air pollution and defending planetary biodiversity, which are currently vital. These EVs ultimately operate thanks to the wireless charging of their batteries when stationary or running. By changing long-established means of transport that have become a threat to biodiversity, it is necessary to ensure that innovative replacement solutions protect this biodiversity. In addition, the construction of wireless power transfer (WPT) battery chargers for these EVs must offer an optimal ecology of clean energy saving. In such a context, the two concepts of One Health (OH) and Responsible Attitude (RA) will find their place in the design and control of WPT tools in EVs. This contribution aims to illustrate and analyze the roles of the green and non-wasteful OH and RA approaches in the design and control of WPT embedded in EVs for the smart city (SC) environment. In the paper, WPT tools are first introduced. The design and control of EV battery charging tools are then examined. The biological effects on living tissues due to the electromagnetic field (EMF) radiation of WPT are analyzed. The phenomena and equations governing the design of WPT and the effects of EMF radiation are then exposed. The OH and RA approaches in the SC context are afterward analyzed. The protection against the unsafe effects of WPT tools in the SC environment is consequently explored. The analyses followed in the paper are supported by examples from the literature. The explorations proposed in this contribution have made it possible to highlight certain notions, allowing a more in-depth understanding of the use of EVs with WPT rechargeable batteries for SCs. Thus, the analysis and fusion of these topics are at the heart of this contribution. [ABSTRACT FROM AUTHOR]

Data center storage systems have different workloads and characteristics. In a centralized managed storage paradigm (software defined data center), providing a fault-tolerant system requires the best possible placement, and the least possible numbers of software defined storage (SDS) controllers. The separation of storage intelligence from storage resources raises the SDS controller's placement problem, which in turn, raises the reliability's issue (the main controller's failure leads to unavailability). The unavailability—down time—cost too much to an organization. To protect against the single point of failure, and at the same time, facilitate a fault-tolerant storage network, center of gravity technique is presented for optimal location selection. The method analyzes different work-loads, distance, and link bandwidth among controllers and OpenFlow enabled switches, and proposes multiple optimal locations for the controllers' deployment. We show the best optimal controllers' placement for various scenarios, e.g., single optimal placement for a big enterprise, and multiple proposed optimal placements for multiple SDS controllers containing various openFlow switches. These controllers can be the part of a large distributed Internet of Things (IoT) architecture. Experiments show that the work-load, distance, and the bandwidth are three important factors that can affect the whole network's reliability (the controllers' placement). [ABSTRACT FROM AUTHOR]

A common feature of numerous definitions of "smart city" is the skilfull use of digitalization tools aimed at achieving the goals of urban policy based on the principles of sustainable development. Digitalization is, therefore, the foundation of a smart city, however, there are no uniform standards for digital solutions that city authorities could implement. This article aims to analyze digitalization solutions understood as processes implemented in the cities ranked in the first five positions of the latest Smart City Index report for 2021—Singapore, Zurich, Oslo, Taipei City, and Lausanne. The authors show whether—and to what extent—digitalization of smart cities is a defining condition for their status and to what extent it is optional. The study showed that various digitalization solutions and paths were implemented, however, their scope and goals differ depending on the specificity of each city and the municipal strategy implemented. [ABSTRACT FROM AUTHOR]

The major advances in intelligent transportation systems are pushing societal services toward autonomy where road management is to be more agile in order to cope with changes and continue to yield optimal performance. However, the pedestrian experience is not sufficiently considered. Particularly, signalized intersections are expected to be popular if not dominant in urban settings where pedestrian density is high. This paper presents the design of a novel environment for simulating human motion on signalized crosswalks at a fine-grained level. Such a simulation not only captures typical behavior, but also handles cases where large pedestrian groups cross from both directions. The proposed simulator is instrumental for optimized road configuration management where the pedestrians' quality of experience, for example, waiting time, is factored in. The validation results using field data show that an accuracy of 98.37% can be obtained for the estimated crossing time. Other results using synthetic data show that our simulator enables optimized traffic light scheduling that diminishes pedestrians' waiting time without sacrificing vehicular throughput. [ABSTRACT FROM AUTHOR]

Creating digital twins of scenes has been widely studied in smart city applications. The development of 3D virtual games attracts many researchers. However, most products have high costs, neglect 3D recovery of the moving people, and lack structured analysis. In this paper, we present a mirror world based on PTZ cameras, which creates digital twins of the space and persons from video streamings, including real-time video registration for PTZ cameras and 3D recovery of the human pose and shape. Our goal is to quickly build a digital space to represent and visualize the physical world, leveraging the two tasks to improve the performance of structured scene understanding. We use the scene images from PTZ cameras to create the 3D scene model and propose an image edge alignment method to optimize the texture mismatching during real-time video registration. Then we propose a human analysis network for 3D recovery of the human pose and shape and add refinement to improve performance on two datasets. We place the 3D human bodies in the right position in the mirror world and utilize joint data from specific scenarios to drive the optimization of the system. Consequently, the mirror world provides 3D visualization and structured analysis of the scene with low cost, thereby enhancing users' spatial understanding. [ABSTRACT FROM AUTHOR]

Mobile Crowdsensing (MCS) is considered to be a key emerging example of a smart city, which combines the wisdom of dynamic people with mobile devices to provide distributed, ubiquitous services and applications. In MCS, each worker tends to complete as many tasks as possible within the limited idle time to obtain higher income, while completing a task may require the worker to move to the specific location of the task and perform continuous sensing. Thus the time and location information of each worker is necessary for an efficient task allocation mechanism. However, submitting the time and location information of the workers to the system raises several privacy concerns, making it significant to protect both the temporal and spatial privacy of workers in MCS. In this article, we propose the Task Allocation with Temporal‐Spatial Privacy Protection (TASP) problem, aiming to maximize the total worker income to further improve the workers' motivation in executing tasks and the platform's utility, which is proved to be NP‐hard. We adopt differential privacy technology to introduce Laplace noise into the location and time information of workers, after which we propose the Improved Genetic Algorithm (SPGA) and the Clone‐Enhanced Genetic Algorithm (SPCGA), to solve the TASP problem. Experimental results on two real‐world datasets verify the effectiveness of the proposed SPGA and SPCGA with the required personalized privacy protection. [ABSTRACT FROM AUTHOR]

This review comprehensively evaluates the integration of solar-powered electrolytic hydrogen (H 2) production and captured carbon dioxide (CO 2) management for clean fuel production, considering all potential steps from H 2 production methods to CO 2 capture and separation processes. It is expected that the near future will cover CO 2 -capturing technologies integrated with solar-based H 2 production at a commercially viable level, and over 5 billion tons of CO 2 are expected to be utilized potentially for clean fuel production worldwide in 2050 to achieve carbon-neutral levels. The H 2 production out of hydrocarbon-based processes using fossil fuels emits greenhouse gas emissions of 17-38 kg CO 2 /kg H 2. On the other hand,. renewable energy based green hydrogen production emits less than 2 kg CO 2 /kg H 2 which makes it really clean and appealing for implementation. In addition, capturing CO2 and using for synthesizing alternative fuels with green hydrogen will help generate clean fuels for smart cities. In this regard, the most sustainable and promising CO 2 capturing method is post-combustion with an adsorption-separation-desorption processes using monoethanolamine adsorbent with high CO 2 removal efficiencies from flue gases. Consequently, this review article provides perspectives on the potential of integrating CO 2 -capturing technologies and renewable energy-based H 2 production systems for clean production to create sustainable cities and communities. [Display omitted] • Carbon capture-storage and H 2 production technologies are comprehensively reviewed. • Clean fuel production technologies from captured CO 2 and produced H 2 are evaluated. • Renewable-powered integrated systems for clean fuel production are proposed. • Solar-powered H 2 production from water is a promising solution in integrated systems. • Promising carbon management and green energy support sustainable cities and communities. [ABSTRACT FROM AUTHOR]

In March 2019, Zhejiang province introduced a new approach to China’s neighbourhood governance, placing digital technology at the centre of communities. Utilizing a case study design, we examine this approach and the role of technology in cultivating community autonomy, public participation, and local social interaction. The findings show how sociospatial dynamics guide the orchestration and reception of smart innovations for community engagement and empowerment, indicating what works and for whom. The empirical work provides a basis for future recommendations, signalling the limitations of digital innovation within communal life and reinforcing a human-centred approach to developing smart systems of community development. [ABSTRACT FROM AUTHOR]

In-cabin occupancy detection has become increasingly important due to incidents involving children left in vehicles under extreme temperature conditions. Frequency modulated continuous wave (FMCW) radars are widely used for non-contact monitoring and sensing applications, particularly for occupancy detection. However, the confined and metallic environment inside vehicle cabins presents significant challenges due to multipath reflections. This paper introduces a novel approach that detects the occupied space in each seat to determine occupancy, using the variance of detected points as an indicator of volume occupancy. In an experimental study involving 70 different scenarios with single and multiple subjects, we classify occupants in each seat into one of three categories: adult, baby, or empty. The proposed method achieves an overall accuracy of 96.7% using an Adaboost classifier and a miss-detection rate of 1.8% for detecting babies. This approach demonstrates superior robustness to multipath interference compared to traditional energy-based methods, offering a significant advancement in in-cabin occupancy detection technology. [ABSTRACT FROM AUTHOR]

Most cities across the globe are embarking on various ‘smart’-based initiatives that seek to use technological developments to enhance people’s lives and make society more productive. Within such smart cities, citizens are often expected to become active ‘collaborators’ in producing smartness. Such technocratic forms of collaboration maintain entrenched forms of hierarchical power, with citizens usually figured as passive ‘consumers’ or ‘users’ of smart technologies rather than as real transformative participants. In this paper, I explore how ‘collaboration’ in the smart city might be thought of differently in ways that challenge the more utilitarian senses that dominate much political and academic discussion. I do this through an engagement with Gilbert Simondon’s philosophy of individuation and technology. In particular, I explore the distinction he draws between ‘interindividual’ and ‘transindividual’ modes of relation and how they entail different political senses of what collaboration between humans and technologies might entail. I flesh this out by drawing on interview research with stakeholders and citizens in Colombo, Sri Lanka. I highlight how stakeholders and citizens imagine collaboration and how these imaginaries explore alternate ways of living in and relating to smart urbanism and smart mobilities today. [ABSTRACT FROM AUTHOR]

Understanding how smart city investment influences the environment has valuable implications for increasing the environmental performance of smart cities. This study adopted spatial econometric models to explore the environmental impacts of smart city investments using data from 100 smart cities in China for the period 2014–2019. Smart city investment includes investment in information and communication technologies (ICT) and human capital. Environmental performance was measured in terms of air quality, green space and energy consumption. The results reveal that smart city investment can produce spatial spillover effects and that the environmental quality of cities is spatially correlated. ICT reduces air quality but increases green space and electricity consumption. In addition, human capital contributes to electricity conservation but does not influence air quality and green space. The impacts of smart city investments on the environment vary according to economic development level. Moreover, policy implications for the implementation of smart city investments that increase environmental performance are proposed. [ABSTRACT FROM AUTHOR]

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]

Integrating the Internet of Things (IoT) in smart cities can solve rapid urbanization. It will provide better urban services, addressing urban problems that develop because of enhancing urbanization without establishing policies focusing on well-being. The present investigation explores the growing trend of the dynamic publications associated with the advanced technologies integrated into smart city publication productivity using bibliometric analysis. This approach enables the detection of the usage history, the usage scheme, and the research landscape topic in scientific papers on IoT in smart cities. This study included 976 articles from 2013 to 2023. The area of research in smart cities is categorized into six important clusters: (1) IoT application areas in smart cities, (2) IoT infrastructure for smart cities, (3) IoT security and privacy for smart cities, (4) IoT architecture for smart cities, (5) IoT technologies and communication protocol for smart cities, (6) IoT data analysis for smart cities. Moreover, this study reveals that the issues related to new technologies, security, and privacy of IoT to integrate into smart cities would be an interesting topic for future research. [ABSTRACT FROM AUTHOR]

The study investigated how cutting‐edge smart city technologies (SCT) contribute to developmental strategies and foster sustainable, efficient, and technologically advanced urban ecosystems. The study is based on multi‐qualitative methods. In the first phase, comprehensive literature was reviewed using the systematic literature review (SLR). Based on the literature recommendations in the second phase, structured interviews were conducted with the professionals based on snowball and purposive sampling techniques. The data were collected from NEOM's working professionals for the interviews. In the third phase, the conclusions of the case studies were also added to generalize the findings comprehensively. The study proclaims that SCT plays a significant role in developing the smart ecosystem in smart cities. The study also investigated the NEOM's potential to elevate urban sustainability. Moreover, the study found that implementing the SCT enhances operational efficiencies, creates new job opportunities, and improves mobility and quality of life. The study also contributed to the equitable theory and presented the shared resources concept. The integration of SCT enhances the quality of life and attracts investment. The study also emphasized the availability of robust data infrastructure and stakeholder engagement to integrate SCT in urban planning and development successfully. [ABSTRACT FROM AUTHOR]

Electrical energy saving was evaluated by taking advantage of PV and ESS in a community unit. An artificial neural network (ANN) and long short-term memory (LSTM) were employed to create a predictive model for PV generation. Annual demand data for residential buildings were estimated using EnergyPlus, while data for other buildings were collected from measurements in J Energy Town, Republic of Korea. Pearson correlation coefficients identified six crucial variables for the model. Comparative analysis of 310 cases revealed that the best-performing model was an ANN with three hidden layers and nodes of 14, 13 and 11. The model satisfied ASHRAE guidelines with a CV(RMSE) of 29.1 % and NMBE of −7.14 %. Evaluating electricity consumption in the community, case B (PV generation) showed a significant 46.3 % reduction compared to case A, while case D achieved a 5 % energy savings relative to case E over the year. [ABSTRACT FROM AUTHOR]

This comprehensive study explores the influence of aerosols and clouds on solar radiation in the urban environments of nine of Greece's largest cities over the decade from 2014 to 2023. Utilizing a combination of Earth Observation data, radiative transfer models, and geographic information systems, the research undertook digital surface modeling and photovoltaic simulations. The study meticulously calculated the optimal rooftop areas for photovoltaic installation in these cities, contributing significantly to their energy adequacy and achieving a balance between daily electricity production and demand. Moreover, the research provides an in-depth analysis of energy and economic losses, while also highlighting the environmental benefits. These include a reduction in pollutant emissions and a decrease in the carbon footprint, aligning with the global shift towards local energy security and the transformation of urban areas into green, smart cities. The innovative methodology of this study, which leverages open access data, sets a strong foundation for future research in this field. It opens up possibilities for similar studies and has the potential to contribute to the creation of an updated, comprehensive solar potential map for continental Greece. This could be instrumental in climate change mitigation and adaptation strategies, thereby promoting sustainable urban development and environmental preservation. [ABSTRACT FROM AUTHOR]

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are essential for a wide range of applications, including environmental monitoring and smart city developments, thanks to their ability to collect and transmit diverse physical and environmental data. The nature of WSNs, coupled with the variability and noise sensitivity of cost-effective sensors, presents significant challenges in achieving accurate data analysis and anomaly detection. To address these issues, this paper presents a new framework, called Online Adaptive Kalman Filtering (OAKF), specifically designed for real-time anomaly detection within WSNs. This framework stands out by dynamically adjusting the filtering parameters and anomaly detection threshold in response to live data, ensuring accurate and reliable anomaly identification amidst sensor noise and environmental changes. By highlighting computational efficiency and scalability, the OAKF framework is optimized for use in resource-constrained sensor nodes. Validation on different WSN dataset sizes confirmed its effectiveness, showing 95.4% accuracy in reducing false positives and negatives as well as achieving a processing time of 0.008 s per sample. [ABSTRACT FROM AUTHOR]

Wireless sensing is a crucial technology for building smart cities, playing a vital role in applications such as human monitoring, route planning, and traffic management. Analyzing the data provided by wireless sensing enables the formulation of more scientific decisions. The wireless sensing of dynamic events is a significant branch of wireless sensing. Sensing the specific times and durations of dynamic events is a challenging problem due to the dynamic event information is concealed within static environments. To effectively sense the relevant information of event occurrence, we propose a wireless sensing method for dynamic events based on RSSI, named RSSI-WSDE. RSSI-WSDE utilizes variable-length sliding windows and statistical methods to process original RSSI time series, amplifying the differences between dynamic events and static environments. Subsequently, z-score normalization is employed to enhance the comparability of the sensing effects for different dynamic events. Furthermore, by setting the adaptive threshold, the occurrence of dynamic event is sensed and the relevant information is marked on the original RSSI time series. In this study, the sensing performance of RSSI-WSDE was tested in indoor corridors and outdoor urban road environments. The wireless sensing of dynamic events, including walking, running, cycling, and driving, was conducted. The experimental results demonstrate that RSSI-WSDE can accurately sense the occurrence of dynamic events, marking the specific time and duration with millisecond-level precision. Moreover, RSSI-WSDE exhibits robust performance in wireless sensing of dynamic events in both indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

Computer Vision (CV) has become increasingly important for Single-Board Computers (SBCs) due to their widespread deployment in addressing real-world problems. Specifically, in the context of smart cities, there is an emerging trend of developing end-to-end video analytics solutions designed to address urban challenges such as traffic management, disaster response, and waste management. However, deploying CV solutions on SBCs presents several pressing challenges (e.g., limited computation power, inefficient energy management, and real-time processing needs) hindering their use at scale. Graphical Processing Units (GPUs) and software-level developments have emerged recently in addressing these challenges to enable the elevated performance of SBCs; however, it is still an active area of research. There is a gap in the literature for a comprehensive review of such recent and rapidly evolving advancements on both software and hardware fronts. The presented review provides a detailed overview of the existing GPU-accelerated edge-computing SBCs and software advancements including algorithm optimization techniques, packages, development frameworks, and hardware deployment specific packages. This review provides a subjective comparative analysis based on critical factors to help applied Artificial Intelligence (AI) researchers in demonstrating the existing state of the art and selecting the best suited combinations for their specific use-case. At the end, the paper also discusses potential limitations of the existing SBCs and highlights the future research directions in this domain. [ABSTRACT FROM AUTHOR]

The analysis of large volumes of data collected from heterogeneous sources is increasingly important for the development of megacities, the advancement of smart city technologies, and ensuring a high quality of life for citizens. This study aimed to develop algorithms for analyzing and interpreting social media data to assess citizens' opinions in real time and for verifying and examining data to analyze social tension and predict the development of situations during the implementation of urban projects. The developed algorithms were tested using an urban project in the field of transportation system development. The study's material included data from social networks, messenger channels and chats, video hosting platforms, blogs, microblogs, forums, and review sites. An interdisciplinary approach was utilized to analyze the data, employing tools such as Brand Analytics, TextAnalyst 2.32, GPT-3.5, GPT-4, GPT-4o, and Tableau. The results of the data analysis showed identical outcomes, indicating a neutral perception among users and the absence of social tension surrounding the project's implementation, allowing for the prediction of a calm development of the situation. Additionally, recommendations were developed to avert potential conflicts and eliminate sources of social tension for decision-making purposes. [ABSTRACT FROM AUTHOR]

This article examines the operational functionality of intelligent transport systems to enhance smart cities by reducing traffic congestion. Given the increasing populations of smart cities, there is a growing demand for public transit systems to address the issue of traffic congestion. Therefore, the suggested system is developed using a few parametric design models, which combine point-to-point protocol and mode control optimization. The multi-objective parametric design for a smart transportation system is conducted using min–max functions to minimize the waiting time period for end users. Furthermore, customers are given the option to utilize a line following mechanism that offers suitable connectivity, along with independent identification and revitalize functions. The predicted model effectively eliminates the delay produced by transportation devices when positioning units are involved, ensuring that individual messages are delivered without any interruptions. In order to evaluate the results of the proposed system model, four different scenarios were examined. A comparison analysis revealed that the suggested method achieves a suitable directional flow for 96% of smart transport units. Additionally, it reduces delays and waiting periods by 2% and 6% respectively, while increasing energy consumption by 29%. [ABSTRACT FROM AUTHOR]

As a research domain, the smart city keeps growing, despite the remaining contradictions and ambiguity related to its conceptual aspects. We propose to dig deeper into the complex socio-technical nature of the smart city and examine the concept through the lens of different public governance paradigms, therefore aligning it with the sustainability outcomes. Embracing interrelated dimensions of humans, technologies and organisations, the smart city can be viewed through the intersection of public governance paradigms (digital governance, collaborative governance and networks). The case of the smart city initiative of Tampere in Finland serves as an empirical illustration of how the proposed conceptual model might be applied in practice. Providing a novel approach to the smart city from a public management perspective, this model would allow policymakers to acquire a more comprehensive understanding of smart city governance and its multi-dimensional outcomes, in terms of social, environmental and economic sustainability. This approach enables the unlocking of the potential to generate multiple values for each group of actors and ensure more effective integration of smart initiatives, policies and projects, based on the public governance paradigms. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) promises to revolutionize transportation in smart cities, but its interconnectedness raises critical security and privacy concerns. Limited computational power, diverse network technologies, and many sensors and vehicles challenge data integrity and trust in data exchange. Existing solutions, often dependent on specific environments and protocols, struggle to address these issues across the entire IoV ecosystem. This paper explores the potential of blockchain technology to address these challenges. We argue that blockchain's immutability and decentralization offer a unique solution for trust management in various IoV environments. We review existing blockchain-based algorithms and models proposed for IoV integration and propose a novel taxonomy to categorize these approaches. This taxonomy will help us analyze effective parameters, implementation methods, and evaluation metrics in the reviewed literature. According to our research, the most critical evaluation parameter for blockchain-based methods is time, including system-level service-related time parameters and solution implementation time, and 38% of existing papers simulated the approach using Hyperledger. Additionally, we will identify key challenges from integrating blockchain into the IoV landscape. By providing a comprehensive review and analysis of blockchain-based trust management solutions for IoV, this paper aims to contribute to the ongoing development of secure and reliable intelligent transportation systems. [ABSTRACT FROM AUTHOR]

The advancement of technology allows for easy adaptability with IoT devices. Internet of Things (IoT) devices can interact without human intervention, which leads to the creation of smart cities. Nevertheless, security concerns persist within IoT networks. To address this, Software Defined Networking (SDN) has been introduced as a centrally controlled network that can solve security issues in IoT devices. Although there is a security concern with integrating SDN and IoT, it specifically targets Distributed Denial of Service (DDoS) attacks. These attacks focus on the network controller since it is centrally controlled. Real-time, high-performance, and precise solutions are necessary to tackle this issue effectively. In recent years, there has been a growing interest in using intelligent deep learning techniques in Network Intrusion Detection Systems (NIDS) through a Software-Defined IoT network (SDN-IoT). The concept of a Wireless Network Intrusion Detection System (WNIDS) aims to create an SDN controller that efficiently monitors and manages smart IoT devices. The proposed WNIDS method analyzes the CSE-CIC-IDS2018 and SDN-IoT datasets to detect and categorize intrusions or attacks in the SDN-IoT network. Implementing a deep learning method called Bidirectional LSTM (BiLSTM)--based WNIDS model effectively detects intrusions in the SDN-IoT network. This model has achieved impressive accuracy rates of 99.97% and 99.96% for binary and multi-class classification using the CSE-CIC-IDS2018 dataset. Similarly, with the SDN-IoT dataset, the model has achieved 95.13% accuracy for binary classification and 92.90% accuracy for multi-class classification, showing superior performance in both datasets. [ABSTRACT FROM AUTHOR]

The improvement of edge perception layer anomaly detection performance has an immeasurable driving effect on the development of smart cities. However, many existing anomaly detection methods often suffer from problems such as ignoring the correlation between multiple source temporal sequences and losing key features of a single temporal sequence. Therefore, a new anomaly detection method using BiLSTM and attention mechanism is proposed. First, a fusion algorithm TCDCD was formed by combining Data Correlation Detection (DCD) and Temporal Continuity Detection (TCD) to preprocess Edge Perception Data (EPD). Then, BiLSTM is employed to gather deep-level features of EPD, and the attention mechanism is utilized to enhance important features that contribute to anomaly detection. Ultimately, the SoftMax classifier is employed to categorize abnormal data. The experimental findings from the SWaT and WADI datasets demonstrate that the suggested method achieves better performance than other newer anomaly detection methods. Among them, the accuracy, precision, recall and F1 of the proposed method on the SWaT dataset were 96.62%, 94.32%, 96.02% and 94.30%, respectively. In terms of performance, it is superior to traditional EPD anomaly detection models, and has good representational and generalization capabilities. [ABSTRACT FROM AUTHOR]

3D urban building modeling is a vital foundational step for building Digital Twins and Smart Cities. In response to existing challenges, such as high time costs, complex production processes, and low consistency with real‐world textures in large‐scale 3D urban building modeling methods, this research proposes a reconstructing 3D urban building models (3DUBM) approach that integrates geospatial data and street view. The approach achieves an enhanced generation of large‐scale 3DUBMs. Based on open geospatial data and street‐view imagery (SVI), the approach was tested in modeling experiments conducted in Shanghai, Hongkong, and Nanjing. Furthermore, a dataset covering unique blocks of 30 cities in China was constructed to demonstrate the approach's characteristics of large coverage, high time efficiency, high model quality and low economic cost. The accuracy of texture mapping from SVI to 3DUBM reached 85%. This achievement has significant economic value in bridging the gap in the production of large‐scale and low‐cost 3DUBM data, promoting the construction of Digital Twins, Smart Cities, and Real‐world 3D modeling. [ABSTRACT FROM AUTHOR]

The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consumption was generated to evaluate various anomaly-detection techniques. Their effectiveness in identifying outliers was determined, and their performance was documented. A 30-day forecast of electrical consumption and an anomaly-detection model have been developed using isolation forest. Isolation forest successfully captured up to 75% of the anomalies. Finally, the Shapley values have been used to generate an explanation of the results of a model capable of detecting anomalous data for the Mexican context. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of information acquisition and task selection in Virtual Reality (VR) systems, enhance the interactive experience, and reduce cognitive load for users, it is crucial to effectively organize and leverage user cognitive psychology and design elements during the VR scene design phase. This paper focuses on analyzing the low cognitive load requirements of users and the need for a satisfactory user perceptual experience based on the cognitive resource theory. We propose a method for optimizing the design of VR system scenario tasks under low cognitive load requirements. By utilizing human-computer hybrid intelligent assistance for predicting user cognitive load and incorporating intelligent optimization genetic algorithms into the optimization of VR system design elements, we aim to minimize cognitive load as the objective function based on the principle of low cognitive load. Important knowledge granularity nodes are used as fitness functions in the optimization process of VR system design resource elements. An application study is conducted, combining the multi-channel cognition in a smart city VR system task information interface, to optimize the system resource features. The study validates and compares the solutions obtained through traditional design processes and the solutions optimized by the method proposed in this paper, using virtual reality eye-tracking experiments for the same design task requirements in VR systems. The results demonstrate that users experience lower cognitive load and better task operation experience when interacting with the optimized solutions proposed in this paper. Therefore, the optimization method studied in this paper can serve as a reference for the construction of virtual reality systems. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G mobile communication technology, the growing demand for media services in society is becoming a characteristic of smart cities. Technical study has exhaustively investigated the low-latency resource supply of edge computing systems for web hosting services. However, external variables (such as transmission and network delays) impact the transmission between edge servers and service requests, resulting in service request delays. In addition, resource service requests that are constantly updated and in dynamic distribution may overload some servers while others are idle, resulting in poor load balancing. Consequently, this research presents a Resource Pre-Allocation method (RPA) for cognitive analytics-based social media platforms, which aims to improve the load balancing of edge servers while serving requests with strict latency requirements, so as to obtain the optimal resource allocation strategy. First, the resource requirement prediction algorithm is developed based on temporal-spatial demand history. Then, we propose a multi-objective algorithm combined with the optimal solution selection techniques to obtain the ideal resource allocation decisions. Finally, the performance of RPA is tested and evaluated. The experimental results show that RPA can allocate resources better than other methods. [ABSTRACT FROM AUTHOR]

Advances in big data and Internet of Things devices have brought novel service modes, such as smart cities and intelligent transportation, to daily life. With the widespread deployment of smart terminals comes an exponentially increasing amount of data, which, causes conflict due to the intensive resource demand and limited computation capacity. To manage this conflict, edge computing has been introduced as an auxiliary technique to cloud computing. However, the emerging computation-intensive service chains bring high resource demands that may exceed the computation capability of a single edge server. Simply offloading them to cloud servers is hardly time saving and is challenging for typical edge-cloud schemes. In this paper, we address the challenge of coordinating the workflow scheduler from multiple users in a partially observable environment. We first partition the workflow by leveraging graph theory to split the component tasks into clusters based on their dependency constraints. We further model the possible contention on edge servers among multiple users as a Markov game and propose a multiagent reinforcement learning-based edge server coordination algorithm named partially observable multiagent workflow scheduler (POMAWS) as the solution. With fine-trained agents, the proposed scheme can intelligently activate nearby edge nodes to form a temporal workgroup and manage contention when it occurs. The numerical results validate the feasibility of our proposed scheme, as its performance exceeds typical cloud computing and traditional clustering schemes with an improved QoS in terms of processing delay. [ABSTRACT FROM AUTHOR]

The emergence of the intelligent city, in the late 2000s, was a worldwide success that has invited itself to the national political agendas of several countries in world association of the major metropolises as well as in the multinational strategy. The success of this global phenomenon is indisputable; however, the smart city remains enigmatic, without precise definition and vague terms. The smart cities projects are characterized by the ability to interconnect urban systems between themselves, including transport system, electricity system, waste system, water system, gas system, etc. and to make each system develop itself; For instance, the field of urban and interurban transport has experienced a spectacular boom over the last few decades mainly with the appearance of vehicular ad hoc networks (VANETs) having promoted the concept of smarts cities mentioned by the birth of intelligent transport systems (ITS). The notion of routing is important and necessary for ITS in order to establish an efficient transmission process in VANETs. A large number of routing protocols exist in VANETs that are classified by different methods and according to determined criteria. This paper proposes a new geographic routing solution based on an intersection entitled A Geographic Routing Based on Local Traffic Density and Multi-hop Intersections in VANETs (GRBLTD-MI) that is more adapted in the context of smart cities. This new approach will discuss the selection of the next intersection when taking into consideration the connectivity of the multi-hop intersection with the use of vehicle's local traffic density, speed, distance and direction. Simulation comparison of GRBLTD-MI against some strong routing protocols has shown the superiority of our approach in terms of packet delivery ratio, end-to-end delay and packet overhead. [ABSTRACT FROM AUTHOR]

VANET technology is an essential component of Intelligent Transportation Systems, which makes c communication between moving cars and stationary Road Side Units more accessible. It allows vehicle nodes to share crucial data among communication devices. VANET has significant potential to enhance traffic efficiency and road safety. This is accomplished by decreasing the chances of collisions between vehicles and reducing the number of accidents. Man-in-the-middle (MITM) attacks are a crucial issue in VANET which needs significant consideration from researchers. To solve the problem of man-in-the-middle attacks, this article presents a dynamic and optimized routing approach for VANET conversation in smart cities by utilizing a chaotic secure multi-verse optimization algorithm. The strategy that has been proposed seeks to achieve the goal of ensuring safe and effective interaction between vehicles participating in VANETs by dynamically determining the optimal path for the exchange of data. A chaotic protect multi-verse optimization approach is used to generate several random sequences from which the most secure route may be selected. This is done to enhance the security of the VANET transmission network during transmission. The results of the trials indicate that the suggested technique is more successful in avoiding MITM and improving the functioning of VANET connections in settings that are characterized by intelligent cities. [ABSTRACT FROM AUTHOR]

Effective quality management plays a pivotal role in ensuring the smooth operation of smart city systems, which have significant implications for safety, accessibility, affordability, and maintainability. Dependability of autonomous systems is of utmost importance, as achieving satisfactory levels of availability and reliability poses considerable challenges. Smart cities are characterized by interconnected sub-architectures, encompassing vehicle monitoring, sidewalk monitoring, and building monitoring, all of which need to function efficiently. Analytical models such as Petri nets, Markov chains, and fault trees are well-suited for evaluating complex scenarios in the context of smart cities. This paper presents analytical models that utilize fault tree and Markov chain techniques to assess the availability and reliability of smart city monitoring systems. The model is divided into shared and non-shared components, with non-shared components being specific to certain contextual applications, while shared components, such as data processing and electrical power, are essential for all smart city monitoring and management systems. The study underscores the ease with which the fault tree model can enhance availability by modifying failure requirements and resources. Case studies provide concrete examples of how availability improved from 95.3 to 99.8% by varying a configuration known as "KooN" in multiple components. This paper takes a comprehensive approach to evaluating the dependability of smart city architectures and contributes advancements, such as hierarchical modeling, sequential sensitivity analysis, and the "KooN" analytic method. These contributions expand the existing knowledge and methodologies in smart city dependability analysis. Moreover, this work aims to serve as a practical tool to assist smart city managers in optimizing their proposals. All modeling aspects and parameters are detailed thoroughly to enable effective implementation of the proposed approach by anyone using it. [ABSTRACT FROM AUTHOR]

The wide application of urban outdoor surveillance systems has greatly improved the efficiency of urban management and social security index. However, most of the existing urban outdoor surveillance cameras lack the records of important parameters such as geospatial coordinates, field of view angle and lens distortion, which brings difficulties to the unified management and layout optimization of the cameras, geospatial analysis of video data, and the computer vision applications such as the trajectory tracking of moving targets. To address this problem, this paper designs a marker with a chessboard pattern and a positioning device, makes the marker move in outdoor space through vehicles and other mobile carriers, and utilizes the marker image captured by the surveillance camera and the spatial position information obtained by the positioning device to batch calibrate the outdoor surveillance cameras and calculate its geospatial coordinates and field of view angle, which achieves the rapid acquisition of important parameters of the surveillance camera, and provides a new method for the rapid calibration of urban outdoor surveillance cameras, which contributes to the informationization management of urban surveillance resources and the spatial analysis and computation of surveillance video data, and make it play a greater role in the application of smart transportation and smart city. Taking the outdoor surveillance cameras within 2.5Km2 of a city as an example, calibration tests were performed on 295 surveillance cameras in the test area, and the geospatial coordinates, field of view angle and lens parameters of 269 surveillance cameras were obtained, and the average error of the spatial position was 0.527 m, and the maximum error was 1.573 m, and the average error of the field of view angle was 1.63°, and the maximum error was 3.4°, which verified the effectiveness and accuracy of the method in this paper. [ABSTRACT FROM AUTHOR]

The formation of congestion on an urban road network is a key issue for the development of sustainable mobility in future smart cities. In this work, we propose a reductionist approach by studying the stationary states of a simple transport model using a random process on a graph, where each node represents a location and the link weights give the transition rates to move from one node to another, representing the mobility demand. Each node has a maximum flow rate and a maximum load capacity, and we assume that the average incoming flow equals the outgoing flow. In the approximation of the single-step process, we are able to analytically characterize the traffic load distribution on the single nodes using a local maximum entropy principle. Our results explain how congested nodes emerge as the total traffic load increases, analogous to a percolation transition where the appearance of a congested node is an independent random event. However, using numerical simulations, we show that in the more realistic case of synchronous dynamics for the nodes, entropic forces introduce correlations among the node states and favor the clustering of empty and congested nodes. Our aim is to highlight the universal properties of congestion formation and, in particular, to understand the role of traffic load fluctuations as a possible precursor of congestion in a transport network. [ABSTRACT FROM AUTHOR]

The development of a digital social credit system in China has sparked debates around urban governance and citizenship performance. Although China’s social credit system is portrayed as a single technical system, the article offers a new perspective on digital social credit focused on geographical variegation. We analyze how variegation is manifested through use of multiple data streams and a diverse range of aims operationalized in various social credit systems at the municipal level. Social credit can be seen as part of algorithmic urban governance and an instrument of urban citizenship performance through scoring, ranking, incentives, and punishments. Analysis of social credit systems’ development in Chinese cities contributes to debates around the role of the urban Global East and evolution of digital and algorithmic governance in and beyond China. [ABSTRACT FROM AUTHOR]

Waste management poses a major challenge for cities worldwide, with significant environmental, economic, and social impacts. This paper proposes a novel waste management system leveraging recent advances in the Internet of Things (IoT), algorithms, and cloud analytics to enable more efficient, sustainable, and eco-friendly waste collection and processing in smart cities. An ultrasonic sensor prototype is tailored for reliable fill-level monitoring. A LoRaWAN and cellular network architecture provides city-wide connectivity. A cloud platform handles sensor data storage, processing, and analytics. Dynamic route optimization algorithms minimize time, distance, and fuel use based on real-time bin data. Extensive pilot studies in 10 different locations across Lahore, Pakistan, validated the system, processing over 200 million data points. The results showed a 32% improvement in route efficiency, a 29% decrease in fuel consumption and emissions, a 33% increase in waste processing throughput, and 18% vehicle maintenance savings versus conventional practices. This demonstrates quantifiable benefits across operational, economic, and sustainability dimensions. The proposed IoT-enabled waste management system represents a significant advancement towards sustainable and ecologically responsible waste practices in smart cities worldwide. This research provides a replicable model for holistic smart city solutions integrating sensing, algorithms, and analytics to transition civic operations towards data-driven, efficient paradigms. It represents a significant advancement in sustainable waste practices for smart cities worldwide. Further work could apply emerging technologies like automation and artificial intelligence to create waste management 3.0. [ABSTRACT FROM AUTHOR]

The infrared and visible fusion technology holds a pivotal position in smart city for cloud and fog computing, particularly in security system. By fusing infrared and visible image information, this technology enhances target identification, tracking and monitoring precision, bolstering overall system security. However, existing deep learning‐based methods rely heavily on convolutional operations, which excel at extracting local features but have limited receptive fields, hampering global information capture. To overcome this difficulty, we introduce GRDATFusion, a novel end‐to‐end network comprising three key modules: transformer, gradient residual dense and attention residual. The gradient residual dense module extracts local complementary features, leveraging a dense‐shaped network to retain potentially lost information. The attention residual module focuses on crucial input image details, while the transformer module captures global information and models long‐range dependencies. Experiments on public datasets show that GRDATFusion outperforms state‐of‐the‐art algorithms in qualitative and quantitative assessments. Ablation studies validate our approach's advantages, and efficiency comparisons demonstrate its computational efficiency. Therefore, our method makes the security systems in smart city with shorter delay and satisfies the real‐time requirement. [ABSTRACT FROM AUTHOR]

Smart grid is the primary stakeholder in smart cities integrated with modern technologies as the Internet of Things (IoT), smart healthcare systems, industrial IoT, renewable energy, energy communities, and the 6G network. Smart grids provide bidirectional power and information flow by integrating many IoT devices and software. These advanced IOTs and cyber layers introduced new types of vulnerabilities and could compromise the stability of smart grids. Some anomalous consumers leverage these vulnerabilities, launch theft attacks on the power system, and steal electricity to lower their electricity bills. The recent developments in numerous detection methods have been supported by cutting‐edge machine learning (ML) approaches. Even so, these recent developments are practically not robust enough because of the limitations of single ML approaches employed. This research introduced a stacked ensemble method for electricity theft detection (ETD) in a smart grid. The framework detects anomalous consumers in two stages; in the first stage, four powerful classifiers are stacked and detect suspicious activity, and the output of these consumers is fed to a single classifier for the second‐stage classification to get better results. Furthermore, we incorporate kernel principal component analysis (KPCA) and localized random affine shadow sampling (LoRAS) for feature engineering and data augmentation. We also perform comparative analysis using adaptive synthesis (ADASYN) and independent component analysis (ICA). The simulation findings reveal that the proposed model outperforms with 97% accuracy, 97% AUC score, and 98% precision. [ABSTRACT FROM AUTHOR]

Intelligent forecasting of economic indexes has been an important demand for sustainable management of smart cities. Existing methods for this purpose were mostly established upon the basis of economic mechanism. Econometric models are the most general technical means in this area. However, in era of digital economy, increasing amount of big data has brought great change to traditional production. It is becoming more difficult for conventional technological forecasting methods to deal with multi-dimensional economic indexes. To deal with such challenge, this paper introduces the artificial intelligence algorithms to implement automatic information processing, and proposes a deep neural network-based intelligent forecasting method for multi-dimensional economic indexes in smart cities. Specifically, a deep neural network with three-layer structure is developed as the backbone methodology. For empirical analysis, the real-world data from "Chengdu–Chongqing Economic Circle" in China from 2012 to 2022 are selected as the main simulation scenario. Four major indexes are selected as the main research object: gross product (GDP), per capita GDP, GDP growth rate and the proportion of tertiary industry in GDP. The experimental results show that the proposal can well deal with such forecasting problem from a data-driven perspective, with a proper forecasting effect on historical data. [ABSTRACT FROM AUTHOR]