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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]

Smart cities and information and communications technology is a rapidly growing field in both research and real-world implementation, but it is one that is still new and with many different ideas. Unfortunately, there is less cooperation and knowledge sharing across the field, and research often fails to move into real-world applications, which holds it back from becoming fully realized. This paper aims to provide an overview of the current state of smart cities, its definitions, technologies, and technical dimensions, from architectural design to standards and data handling, and how they are handled in the real world and its impact on society. Additionally, it examines important smart city projects, their applications, and ranking systems. This text aims to forecast the future of the field, its impact, the challenges it faces, and what should be addressed to help it reach its full potential. [ABSTRACT FROM AUTHOR]

Urban configurations have substantial impacts on lifestyles, behaviors, and people's daily lives. Elaborating urban plans based on smart and sustainable initiatives is a challenging task as it encompasses numerous multidisciplinary premises due to the dynamics of the urban context. This research aims to conduct a systematic review of the literature in three axes: sustainable urban development, city master plans, and smart cities. From the portfolio of papers built, it was possible to map projects designed for sustainable urban development in order to verify the integration of these projects with city master plans, specifically for smart cities. The content analysis also sought to identify global regions that use robust models, their frameworks, and technologies for this purpose as a strategy to identify which research or frameworks can be replicated in the future, helping to develop city master plans for smart cities. Although some results were found, it was observed that there is a lack of studies integrating the topics of smart cities, sustainability, and city master plans in the same discussion. Few types of research involve the three themes or their full application, being mostly described in isolation. Thus, the originality of this study is filling this gap in the literature. [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]

This article investigates the innovative role of machine learning (ML) in the development of smart cities, emphasizing the critical interrelationship between ML and urban environments. While existing studies address ML and urban settings separately, this work uniquely examines their intersection, highlighting the transformative potential of ML in urban development. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a systematic and reproducible approach was employed to review 42 relevant studies. The analysis reveals four key themes: transportation and traffic optimization, people and event flow tracking, sustainability applications, and security use cases. These findings underscore ML's ability to revolutionize smart city initiatives by enhancing efficiency, sustainability, and security. This review identifies significant research gaps and proposes future directions, positioning ML as a cornerstone in the evolution of intelligent urban environments. [ABSTRACT FROM AUTHOR]

The deployment of fifth-generation (5G) wireless networks has already laid the ground-work for futuristic smart cities but along with this, it has also triggered the rapid growth of a wide range of applications, for example, the Internet of Everything (IoE), online gaming, extended/virtual reality (XR/VR), telemedicine, cloud computing, and others, which require ultra-low latency, ubiquitous coverage, higher data rates, extreme device density, ultra-high capacity, energy efficiency, and better reliability. Moreover, the predicted explosive surge in mobile traffic until 2030 along with envisioned potential use-cases/scenarios in a smart city context will far exceed the capabilities for which 5G was designed. Therefore, there is a need to harness the 6th Generation (6G) capabilities, which will not only meet the stringent requirements of smart megacities but can also open up a new range of potential applications. Other crucial concerns that need to be addressed are related to network security, data privacy, interoperability, the digital divide, and other integration issues. In this article, we examine current and emerging trends for the implementation of 6G in the smart city arena. Firstly, we give an inclusive and comprehensive review of potential 6th Generation (6G) mobile communication technologies that can find potential use in smart cities. The discussion of each technology also covers its potential benefits, challenges and future research direction. Secondly, we also explore promising smart city applications that will use these 6G technologies, such as, smart grids, smart healthcare, smart waste management, etc. In the conclusion part, we have also highlighted challenges and suggestions for possible future research directions. So, in a single paper, we have attempted to provide a wider perspective on 6G-enabled smart cities by including both the potential 6G technologies and their smart city applications. This paper will help readers gain a holistic view to ascertain the benefits, opportunities and applications that 6G technology can bring to meet the diverse, massive and futuristic requirements of smart cities. [ABSTRACT FROM AUTHOR]

The swift advancement of communication and information technologies has transformed urban infrastructures into smart cities. Traditional assessment methods face challenges in capturing the complex interdependencies and temporal dynamics inherent in these systems, risking urban resilience. This study aims to enhance the criticality assessment of geographic zones within smart cities by introducing a novel deep learning architecture. Utilizing Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependency modeling, the proposed framework processes inputs such as total electricity use, flooding levels, population, poverty rates, and energy consumption. The CNN component constructs hierarchical feature maps through successive convolution and pooling operations, while the LSTM captures sequence-based patterns. Fully connected layers integrate these features to generate final predictions. Implemented in Python using TensorFlow and Keras on an Intel Core i7 system with 32 GB RAM and an NVIDIA GTX 1080 Ti GPU, the model demonstrated a superior performance. It achieved a mean absolute error of 0.042, root mean square error of 0.067, and an R-squared value of 0.935, outperforming existing methodologies in real-time adaptability and resource efficiency. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) envisions the interconnection of all electronic devices, ushering in a new technological era. IoT and 5G technology are linked, complementing each other in a manner that significantly enhances their impact. As sensors become increasingly embedded in our daily lives, they transform everyday objects into "smart" devices. This synergy between IoT sensor networks and 5G creates a dynamic ecosystem where the infrastructure provided by 5G's high-speed, low-latency communication enables IoT devices to function more efficiently and effectively, paving the way for innovative applications and services that enhance our awareness and interactions with the world. Moreover, application-oriented and multifunctional antennas need to be developed to meet these high demands. In this review, a comprehensive analysis of IoT antennas is conducted based on their application characteristics. It is important to note that, to the best of our knowledge, this is the first time that this categorization has been performed in the literature. Indeed, comparing IoT antennas across different applications without considering their specific operational contexts is not practical. This review focuses on four primary operational fields: smart homes, smart cities, and biomedical and implantable devices. [ABSTRACT FROM AUTHOR]

Federated learning (FL) and bioinspired computing (BIC), two distinct, yet complementary fields, have gained significant attention in the machine learning community due to their unique characteristics. FL enables decentralized machine learning by allowing models to be trained on data residing across multiple devices or servers without exchanging raw data, thus enhancing privacy and reducing communication overhead. Conversely, BIC draws inspiration from nature to develop robust and adaptive computational solutions for complex problems. This paper explores the state of the art in the integration of FL and BIC, introducing BIC techniques and discussing the motivations for their integration with FL. The convergence of these fields can lead to improved model accuracy, enhanced privacy, energy efficiency, and reduced communication overhead. This synergy addresses inherent challenges in FL, such as data heterogeneity and limited computational resources, and opens up new avenues for developing more efficient and autonomous learning systems. The integration of FL and BIC holds promise for various application domains, including healthcare, finance, and smart cities, where privacy-preserving and efficient computation is paramount. This survey provides a systematic review of the current research landscape, identifies key challenges and opportunities, and suggests future directions for the successful integration of FL and BIC. [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]

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]

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]

Although walking methodologies (WMs) and machine learning (ML) have been objects of interest for urban scholars, it is difficult to find research that integrates both. We propose a 'cyborg walk' method and apply it to studying litter in public spaces. Walking routes are created based on an unsupervised learning algorithm (k-means) to classify public spaces. Then, a deep learning model (YOLOv5) is used to collect data from geotagged photos taken by an automatic Insta360 X3 camera worn by human walkers. Results from image recognition have an accuracy between 83.7% and 95%, which is similar to what is validated by the literature. The data collected by the machine are automatically georeferenced thanks to the metadata generated by a GPS attached to the camera. WMs could benefit from the introduction of ML for informative route optimisation and georeferenced visual data quantification. The links between these findings and the existing WM literature are discussed, reflecting on the parallels between this 'cyborg walk' experiment and the seminal cyborg metaphor proposed by Donna Haraway. [ABSTRACT FROM AUTHOR]

The rise of the Internet of Things (IoT) has enabled the development of smart cities, intelligent buildings, and advanced industrial ecosystems. When the IoT is matched with machine learning (ML), the advantages of the resulting enhanced environments can span, for example, from energy optimization to security improvement and comfort enhancement. Together, IoT and ML technologies are widely used in smart buildings, in particular, to reduce energy consumption and create Intelligent Energy-Efficient Buildings (IEEBs). In IEEBs, ML models are typically used to analyze and predict various factors such as temperature, humidity, light, occupancy, and human behavior with the aim of optimizing building systems. In the literature, many review papers have been presented so far in the field of IEEBs. Such papers mostly focus on specific subfields of ML or on a limited number of papers. This paper presents a systematic meta-survey, i.e., a review of review articles, that compares the state of the art in the field of IEEBs using the Prisma approach. In more detail, our meta-survey aims to give a broader view, with respect to the already published surveys, of the state-of-the-art in the IEEB field, investigating the use of supervised, unsupervised, semi-supervised, and self-supervised models in a variety of IEEB-based scenarios. Moreover, our paper aims to compare the already published surveys by answering five important research questions about IEEB definitions, architectures, methods/models used, datasets and real implementations utilized, and main challenges/research directions defined. This meta-survey provides insights that are useful both for newcomers to the field and for researchers who want to learn more about the methodologies and technologies used for IEEBs' design and implementation. [ABSTRACT FROM AUTHOR]

In recent decades, the dynamics of road vehicle traffic have significantly evolved, compelling traffic engineers to develop innovative traffic monitoring solutions, especially for dense road networks. Traditional methods for measuring traffic volume along road sections may no longer suffice for modern traffic control systems. This is particularly true for induction loops, a widely used method since the last century. In contrast, measuring techniques using microwaves or visible light offer better accuracy but are often hindered by the high cost of sensors. This paper presents new techniques for measuring traffic flow and other parameters that adapt to changing traffic dynamics using low-cost optical distance sensors. Our study demonstrates that the integration of multiple monitoring approaches enhances measurement accuracy, contingent on the dynamics and specific characteristics of the traffic. The results indicate that cheap optical distance sensors are particularly well suited for use in smart city road networks. [ABSTRACT FROM AUTHOR]

The in-depth participation and application of new-generation information and communication technologies, such as big data, Internet of Things, artificial intelligence, etc., in the field of smart cities have promoted their abilities in urban fine governance, public services, ecological livability, scientific and technological innovation, etc. Smart cities are gradually becoming recognized as the best solution to "urban problems". Smart city construction drives urban innovative development, accumulates kinetic energy for economic growth, strengthens social support functions, enhances the effectiveness of the ecological environment, and promotes the convergence and integration of urban green development and high-quality development. This paper constructs a difference-in-differences model based on propensity score matching. Additionally, fiscal science and technology investment is introduced as mediating variables to further explain the mechanism through which smart city pilot policy impacts urban green and high-quality development. This research uses panel data from 156 prefecture-level cities in China from 2006 to 2019 to empirically test that the construction of smart cities has a significant positive effect on urban green and high-quality development. The mediation effect model shows that an increase in the level of local government's fiscal science and technology investment enhances the positive effect of smart city construction on urban green and high-quality development. This research concludes with policy recommendations: the government should seize the development opportunity presented by smart city pilot policy, providing necessary policy support and financial incentive for the construction of smart cities. This will optimize the local economic structure, transform the driving forces of urban development, and assist cities in achieving green and high-quality development. [ABSTRACT FROM AUTHOR]

The construction industry is constantly evolving through government policies, technologies, and innovative processes. BIM and modular construction are innovative concepts aimed at achieving sustainable smart cities by enhancing cost performance, efficiency, and sustainability. Despite growing global interest in their integration, there is a notable knowledge gap in sub-Saharan Africa. As a result, this research aims to explore the barriers to integrating BIM into modular construction in sub-Saharan Africa. The study adopted a non-experimental design, using a four-stage methodological framework. Initially, a literature review was carried out to conceptualize the study. Stage two involves a pilot survey to create an adequate data collection instrument. In the third stage, 81 registered companies were purposely selected, and data was collected through an online survey. Finally, the fourth stage uses descriptive and inferential techniques to make logical and informed conclusions. The top-ranked barriers are high initial costs, insufficient cross-field expertise, stakeholder collaboration problems, limited software interoperability, and skills shortages. Recommendations include early stakeholder collaboration, BIM execution plan development by modular companies, improved staff training, and increasing financial support from the government. Future research should explore country-specific barriers and case studies to aid the integration of the two innovative solutions in the region. [ABSTRACT FROM AUTHOR]

Crosswalks play a fundamental role in road safety. However, over time, many suffer wear and tear that makes them difficult to see. This project presents a solution based on the use of computer vision techniques for identifying and classifying the level of wear on crosswalks. The proposed system uses a convolutional neural network (CNN) to analyze images of crosswalks, determining their wear status. The design includes a prototype system mounted on a vehicle, equipped with cameras and processing units to collect and analyze data in real time as the vehicle traverses traffic routes. The collected data are then transmitted to a web application for further analysis and reporting. The prototype was validated through extensive tests in a real urban environment, comparing its assessments with manual inspections conducted by experts. Results from these tests showed that the system could accurately classify crosswalk wear with a high degree of accuracy, demonstrating its potential for aiding maintenance authorities in efficiently prioritizing interventions. [ABSTRACT FROM AUTHOR]

In the development of Smart Cities, efficient waste collection networks are crucial, especially those that consider recycling. To plan for the future, routing and depot location techniques must handle heterogeneous cargo for proper waste separation. This paper introduces a Mixed-Integer Linear Programming (MILP) model and a three-level metaheuristic to address the Periodic Location Routing Problem (PLRP) for urban waste collection. The PLRP involves creating routes that ensure each customer is visited according to their waste demand frequency, aiming to minimize logistical costs such as transportation and depot opening. Unlike previous approaches, this approach characterizes each type of customer considering different needs for waste collection. A total of 25 customer types were created based on mixed waste demands and visit frequencies. The proposed algorithm uses Variable Neighborhood Search (VNS) and Local Search heuristics, comprising three neighborhood generation structures. Computational experiments demonstrate that the VNS algorithm delivers solutions seven times better than exact methods in a fraction of the time. For larger instances, VNS achieves feasible solutions where the MILP model fails within the same time frame. [ABSTRACT FROM AUTHOR]

Smart cities are urban areas that leverage technological solutions to enhance traditional network management and efficiency to benefit residents and businesses. Based on the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol, this study presents a systematic literature review aimed at analyzing the existing literature on smart cities research. The literature review specifically focuses on the impact of blockchain technology on the urban environment and its potential to contribute to the development of inclusive and sustainable communities, including financial systems and infrastructures with similar characteristics to serve these societies. The findings reveal a lack of studies on the practical applications of distributed ledger technologies (DLTs), particularly blockchain, that specifically focus on the urban context capable of developing the (financial) ecosystem of smart cities. To address this gap, a future research agenda is proposed, highlighting several research questions that could guide academics and practitioners interested in exploring the development of smart city systems, with particular attention on the financial framework. [ABSTRACT FROM AUTHOR]

Three-dimensional multi-object tracking (MOT) using lidar point cloud data is crucial for applications in autonomous driving, smart cities, and robotic navigation. It involves identifying objects in point cloud sequence data and consistently assigning unique identities to them throughout the sequence. Occlusions can lead to missed detections, resulting in incorrect data associations and ID switches. To address these challenges, we propose a novel point cloud multi-object tracker called GBRTracker. Our method integrates an intra-frame graph structure into the backbone to extract and aggregate spatial neighborhood node features, significantly reducing detection misses. We construct an inter-frame bipartite graph for data association and design a sophisticated cost matrix based on the center, box size, velocity, and heading angle. Using a minimum-cost flow algorithm to achieve globally optimal matching, thereby reducing ID switches. For unmatched detections, we design a motion-based re-identification (ReID) feature embedding module, which uses velocity and the heading angle to calculate similarity and association probability, reconnecting them with their corresponding trajectory IDs or initializing new tracks. Our method maintains high accuracy and reliability, significantly reducing ID switches and trajectory fragmentation, even in challenging scenarios. We validate the effectiveness of GBRTracker through comparative and ablation experiments on the NuScenes and Waymo Open Datasets, demonstrating its superiority over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This study proposes wide-band frequency selective surfaces (FSS) with polarization-independent characteristics that are tailored for IoT applications. The design consists of two different layers with band-stop characteristics that target key frequency bands in sub-6 GHz: 3.7 GHz (n77) and 4.5 GHz (n79), offering a 1.39 GHz bandwidth spanning from 3.61 GHz to 5.0 GHz. This study also presents a double-layer structure with a WB property with a fractional bandwidth of 32%. Simulations have been conducted to observe variations in insertion loss across incident and polarization angles ranging from 0 to 60 degrees for both TE and TM modes in the suggested FSS structures. These simulations demonstrate the design's polarization independence. Transparent polyvinyl chloride with a dielectric constant of 2.77 and a thickness of 1.48 mm has been utilized as the substrate material. The optical transmittance is calculated to be 96.7% for Layer 1, 95.7% for Layer 2, and 92.4% for the double-layer structure, and these calculated optical transmittance values were found to be higher compared to the studies in the literature. The proposed design is well-suited for sub-6 GHz IoT applications due to their high transparency, cost-effectiveness, robust high-performance capabilities in suppression, and polarization-independent features. The results of 3D full-wave simulations were compared with measurement and the equivalent circuit model outcomes, and a good agreement between the results was observed. [ABSTRACT FROM AUTHOR]

The field of the Internet of Things (IoT) is dominating various areas of technology. As the number of devices has increased, there is a need for efficient communication with low resource consumption and energy efficiency. Low Power Wide Area Networks (LPWANs) have emerged as a transformative technology for the IoT as they provide long-range communication capabilities with low power consumption. Among the various LPWAN technologies, Long Range Wide Area Networks (LoRaWAN) are widely adopted due to their open standard architecture, which supports secure, bi-directional communication and is particularly effective in outdoor and complex urban environments. This technology is helpful in enabling a variety of IoT applications that require wide coverage and long battery life, such as smart cities, industrial IoT, and environmental monitoring. The integration of Machine Leaning (ML) and Artificial Intelligence (AI) into LoRaWAN operations has further enhanced its capability and particularly optimized resource allocation and energy efficiency. This systematic literature review provides a comprehensive examination of the integration of ML and AI technologies in the optimization of LPWANs, with a specific focus on LoRaWAN. This review follows the PRISMA model and systematically synthesizes current research to highlight how ML and AI enhance operational efficiency, particularly in terms of energy consumption, resource management, and network stability. The SLR aims to review the key methods and techniques that are used in state-of-the-art LoRaWAN to enhance the overall network performance. We identified 25 relevant primary studies. The study provides an analysis of key findings based on research questions on how various LoRaWAN parameters are optimized through advanced ML, DL, and RL techniques to achieve optimized performance. [ABSTRACT FROM AUTHOR]

In the context of escalating concerns about environmental sustainability in smart cities, solar power and other renewable energy sources have emerged as pivotal players in the global effort to curtail greenhouse gas emissions and combat climate change. The precise prediction of solar power generation holds a critical role in the seamless integration and effective management of renewable energy systems within microgrids. This research delves into a comparative analysis of two machine learning models, specifically the Light Gradient Boosting Machine (LGBM) and K Nearest Neighbors (KNN), with the objective of forecasting solar power generation in microgrid applications. The study meticulously evaluates these models' accuracy, reliability, training times, and memory usage, providing detailed experimental insights into optimizing solar energy utilization and driving environmental sustainability forward. The comparison between the LGBM and KNN models reveals significant performance differences. The LGBM model demonstrates superior accuracy with an R-squared of 0.84 compared to KNN's 0.77, along with lower Root Mean Squared Error (RMSE: 5.77 vs. 6.93) and Mean Absolute Error (MAE: 3.93 vs. 4.34). However, the LGBM model requires longer training times (120 s vs. 90 s) and higher memory usage (500 MB vs. 300 MB). Despite these computational differences, the LGBM model exhibits stability across diverse time frames and seasons, showing robustness in handling outliers. These findings underscore its suitability for microgrid applications, offering enhanced energy management strategies crucial for advancing environmental sustainability. This research provides essential insights into sustainable practices and lays the foundation for a cleaner energy future, emphasizing the importance of accurate solar power forecasting in microgrid planning and operation. [ABSTRACT FROM AUTHOR]

In this study, we present a novel approach leveraging the segment anything model (SAM) for the efficient detection and tracking of vehicles in urban traffic surveillance systems by utilizing uncalibrated low-resolution highway cameras. This research addresses the critical need for accurate vehicle monitoring in intelligent transportation systems (ITS) and smart city infrastructure. Traditional methods often struggle with the variability and complexity of urban environments, leading to suboptimal performance. Our approach harnesses the power of SAM, an advanced deep learning-based image segmentation algorithm, to significantly enhance the detection accuracy and tracking robustness. Through extensive testing and evaluation on two datasets of 511 highway cameras from Quebec, Canada and NVIDIA AI City Challenge Track 1, our algorithm achieved exceptional performance metrics including a precision of 89.68%, a recall of 97.87%, and an F1-score of 93.60%. These results represent a substantial improvement over existing state-of-the-art methods such as the YOLO version 8 algorithm, single shot detector (SSD), region-based convolutional neural network (RCNN). This advancement not only highlights the potential of SAM in real-time vehicle detection and tracking applications, but also underscores its capability to handle the diverse and dynamic conditions of urban traffic scenes. The implementation of this technology can lead to improved traffic management, reduced congestion, and enhanced urban mobility, making it a valuable tool for modern smart cities. The outcomes of this research pave the way for future advancements in remote sensing and photogrammetry, particularly in the realm of urban traffic surveillance and management. [ABSTRACT FROM AUTHOR]

This study presents an innovative method to optimize convolutional neural networks (CNNs) tailored for the prevalent low-power platforms in IoT and embedded devices, which are integral to smart city infrastructures. Our approach introduces a novel "Rotating Convolutional Kernel" technique, designed to significantly reduce the computational load of CNNs while maintaining high accuracy, an essential feature for the constrained processing capabilities of devices produced by Hisilicon, MediaTek, and Novatek, among others. By leveraging the intelligent video engine (IVE) capabilities inherent in low-end CPUs, our optimized YOLOv3-Tiny model, with fewer than 100 K parameters and specifically fine-tuned for pedestrian and vehicle detection, demonstrates impressive processing speeds of 52 ms/frame on the HI3516EV200 chip and 66 ms/frame on the MSC313E chip, with minimal accuracy compromise. Despite a substantial reduction in parameter count compared to that in MobileNetV2, our model's top-1 accuracy only slightly decreases by 2.6%, showcasing the effectiveness of our optimization technique. Our findings highlight the potential and applicability of our method in enhancing the performance and utility of IoT and embedded devices within smart cities. By achieving an optimal balance between computational efficiency and detection accuracy, our approach offers a promising avenue for advancing the capabilities of low-power devices in urban surveillance, traffic management, and other smart city applications, thereby contributing to the development of more intelligent, efficient, and responsive urban environments. [ABSTRACT FROM AUTHOR]

The emergence of 6G communication technologies brings both opportunities and challenges for the Internet of Things (IoT) in smart cities. In this paper, we introduce an advanced network slicing framework designed to meet the complex demands of 6G smart cities' IoT deployments. The framework development follows a detailed methodology that encompasses requirement analysis, metric formulation, constraint specification, objective setting, mathematical modeling, configuration optimization, performance evaluation, parameter tuning, and validation of the final design. Our evaluations demonstrate the framework's high efficiency, evidenced by low round-trip time (RTT), minimal packet loss, increased availability, and enhanced throughput. Notably, the framework scales effectively, managing multiple connections simultaneously without compromising resource efficiency. Enhanced security is achieved through robust features such as 256-bit encryption and a high rate of authentication success. The discussion elaborates on these findings, underscoring the framework's impressive performance, scalability, and security capabilities. [ABSTRACT FROM AUTHOR]

Function as a Service (FaaS) is highly beneficial to smart city infrastructure due to its flexibility, efficiency, and adaptability, specifically for integration in the digital landscape. FaaS has serverless setup, which means that an organization no longer has to worry about specific infrastructure management tasks; the developers can focus on how to deploy and create code efficiently. Since FaaS aligns well with the IoT, it easily integrates with IoT devices, thereby making it possible to perform event-based actions and real-time computations. In our research, we offer an exclusive likelihood-based model of adaptive machine learning for identifying the right place of function. We employ the XGBoost regressor to estimate the execution time for each function and utilize the decision tree regressor to predict network latency. By encompassing factors like network delay, arrival computation, and emphasis on resources, the machine learning model eases the selection process of a placement. In replication, we use Docker containers, focusing on serverless node type, serverless node variety, function location, deadlines, and edge-cloud topology. Thus, the primary objectives are to address deadlines and enhance the use of any resource, and from this, we can see that effective utilization of resources leads to enhanced deadline compliance. [ABSTRACT FROM AUTHOR]

This study provides a detailed analysis of land use and land cover (LULC) changes at the district level within the Delhi–Mumbai Industrial Corridor (DMIC) from 2001 to 2021. Using the Indian Meteorological Department's sub-divisional framework and MODIS data across seven primary LULC classes, the analysis is instrumental in informing infrastructure planning for existing and future smart cities and industrial clusters within the DMIC. The key findings reveal a yearly increase of 3031.40 sq. km. per year in agricultural land, with decreases in shrubland, grassland, and bareland of −1774.72 sq. km. per year, −1119.62 sq. km. per year, and −203.76 sq. km. per year, respectively. On the other hand, forests grew by a modest 148.14 sq. km. per year, while waterbodies and built-up lands saw minor increases of 55.73 sq. km. and 21.48 sq. km. per year. Ecologically Sensitive Areas (ESAs) were evaluated for LULC changes. The smart cities of Pune and Thane serve as excellent examples of balanced urban development and natural growth management. However, the study also highlights the need for further research to investigate LULC impacts on climatic variables, advocating for a regional planning approach in the DMIC. [ABSTRACT FROM AUTHOR]

Smart city construction aims to promote the digital transformation of cities, support the modernization of urban governance, and stimulate urban innovation and development. In this paper, we analyze the role of smart city construction on economic growth and the impact mechanism driving this. In terms of theoretical analysis, we discuss the role of smart city construction in economic growth based on Schumpeterian innovation theory. In terms of empirical analysis, we evaluate the impact of smart city construction on economic growth with panel data of Chinese prefecture-level cities from 2011 to 2019, using time-varying difference-in-differences models and the event study method. The findings of the research indicate that the construction of smart cities has a positive effect on regional economic growth and the results can be confirmed through a series of robustness tests. Smart city construction generates mediating effects by enhancing urban innovation capability and entrepreneurship, thereby promoting urban economic growth. Furthermore, the impact of smart city construction on economic growth exhibits heterogeneous effects due to variations in the degree of marketization. In regions with a higher level of marketization, the promotional effect of smart city construction on economic growth is stronger. Consequently, it is imperative to intensify the implementation of smart city construction and persistently pursue market-oriented reforms. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has become ubiquitous. IoT devices are applied in a multitude of applications, e.g., in smart home scenarios, building automation, smart energy and smart cities, healthcare, and industrial environments. Fast and efficient implementation and roll-out of IoT devices is a critical factor for successs and acceptance of IoT devices. At the same time, the variety of hardware platforms that can be used for IoT applications, as well as the number of IoT orchestration platforms is increasing. Finding the right combination of tooling and hardware is not trivial, but essential for building applications that provide value. In this work, a Software-Bus-Toolchain (SBT) is introduced that encapsulates firmware design, data point definition, and communication protocol usage. Furthermore, an IoT control platform is provided to control and evaluate the IoT modules. Thus, using the SBT, solely the business logic has to be designed, while the hardware-design is automated to a high degree. Usage of the Zephyr framework allows the interchange of hardware modules, while interfaces provide easy adaption of data points and communication capabilities. The implementation of interfaces to the IoT-platform as well as to the communication layer provides a universal usage of logic and data elements. The SBT is evaluated in two application scenarios, where its flexible nature is shown. [ABSTRACT FROM AUTHOR]

Modern building automation systems implement plenty of advanced control and monitoring functions that consider various parameters like users' activity, lighting, temperature changes, etc. Moreover, novel solutions based on the Internet of Things and cloud services are also being developed for smart buildings to ensure comfort of use, user safety, energy efficiency improvements, and integration with smart grids and smart city platforms. Such a wide spectrum of technologies and functions requires a novel approach in building automation systems design to provide effective implementation and flexibility during operation. At the same time, in the building design and operation industries, tools based on building information modeling and digital twins are being developed. This paper discusses the development directions and application areas of these solutions, identifying new trends and possibilities of their use in smart homes and buildings. In particular, the focus is on procedures for selecting automation functions, effective integration, and interoperability of building management systems with the Internet of Things, considering the organization of prediction mechanisms and dynamic functional changes in buildings and smart networks. Chosen solutions and functions should consider the requirements set out in the EN ISO 52120 standard and the guidelines defined for the Smart Readiness Indicator. [ABSTRACT FROM AUTHOR]

An effective way to promote energy transition while tackling climate change involves redefining cities from being part of the problem to integral parts of the solution. Positive energy districts and climate positive circular communities are excellent examples of how this is feasible. But how do we understand which are the multiple benefits that these projects can bring to the local territory and relative community? This article aims to answer this question by developing a specific engagement and evaluation methodology. Our approach involves consulting with project partners to explore the multiple benefits of each case study. Subsequently, it plans to engage the stakeholders through the submission of a questionnaire to gather information regarding the relative importance of different benefits as perceived by each stakeholder. The questionnaire is based on the best–worst scaling method, which is a survey technique for determining people's priorities. The preliminary findings of the study conducted on project partners of two European projects, ARV and ProLight, indicate a strong alignment with current European policy priorities. The involvement of other stakeholders in the study will serve to assess whether bottom-up priorities coincide with broader perspectives or whether adaptations to project strategies and dissemination approaches are needed. [ABSTRACT FROM AUTHOR]

Accurate localization of devices within Internet of Things (IoT) networks is driven by the emergence of novel applications that require context awareness to improve operational efficiency, resource management, automation, and safety in industry and smart cities. With the Integrated Localization and Communication (ILAC) functionality, IoT devices can simultaneously exchange data and determine their position in space, resulting in maximized resource utilization with reduced deployment and operational costs. Localization capability in challenging scenarios, including harsh environments with complex geometry and obstacles, can be provided with robust, reliable, and energy-efficient communication protocols able to combat impairments caused by interference and multipath, such as the IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol. This paper presents an enhancement of the TSCH protocol that integrates localization functionality along with communication, improving the protocol's operational capabilities and setting a baseline for monitoring, automation, and interaction within IoT setups in physical environments. A novel approach is proposed to incorporate a hybrid localization by integrating Direction of Arrival (DoA) estimation and Multi-Carrier Phase Difference (MCPD) ranging methods for providing DoA and distance estimates with each transmitted packet. With the proposed enhancement, a single node can determine the location of its neighboring nodes without significantly affecting the reliability of communication and the efficiency of the network. The feasibility and effectiveness of the proposed approach are validated in a real scenario in an office building using low-cost proprietary devices, and the software incorporating the solution is provided. The experimental evaluation results show that a node positioned in the center of the room successfully estimates both the DoA and the distance to each neighboring node. The proposed hybrid localization algorithm demonstrates an accuracy of a few tens of centimeters in a two-dimensional space. [ABSTRACT FROM AUTHOR]

Process algebra is one of the most suitable formal methods to model smart IoT systems for smart cities. Each IoT in the systems can be modeled as a process in algebra. In addition, the nondeterministic behavior of the systems can be predicted by defining probabilities on the choice operations in some algebra, such as PALOMA and PACSR. However, there are no practical mechanisms in algebra either to measure or control uncertainty caused by the nondeterministic behavior in terms of satisfiability of the system requirements. In our previous research, to overcome the limitation, a new process algebra called dTP-Calculus was presented to verify probabilistically the safety and security requirements of smart IoT systems: the nondeterministic behavior of the systems was defined and controlled by the static and dynamic probabilities. However, the approach required a strong assumption to handle the unsatisfied probabilistic requirements: enforcing an optimally arbitrary level of high-performance probability from the continuous range of the probability domain. In the paper, the assumption from the previous research is eliminated by defining the levels of probability from the discrete domain based on the notion of Permissible Process and System Equivalences so that satisfiability is incrementally enforced by both Permissible Process Enhancement in the process level and Permissible System Enhancement in the system level. In this way, the unsatisfied probabilistic requirements can be incrementally enforced with better-performing probabilities in the discrete steps until the final decision for satisfiability can be made. The SAVE tool suite has been developed on the ADOxx meta-modeling platform to demonstrate the effectiveness of the approach with a smart EMS (emergency medical service) system example, which is one of the most practical examples for smart cities. SAVE showed that the approach is very applicable to specify, analyze, verify, and especially, predict and control uncertainty or risks caused by the nondeterministic behavior of smart IoT systems. The approach based on dTP-Calculus and SAVE may be considered one of the most suitable formal methods and tools to model smart IoT systems for smart cities. [ABSTRACT FROM AUTHOR]

The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard's levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study's results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning. [ABSTRACT FROM AUTHOR]

Driving style and road traffic play pivotal roles in the development of smart cities, influencing traffic flow, safety, and environmental sustainability. This study presents an innovative approach for detecting road traffic conditions and driving styles using On-Board Diagnostics (OBD) data and smartphone sensors. This approach offers an inexpensive implementation of prediction, as it utilizes existing vehicle data without requiring additional setups. Two Artificial Neural Network (ANN) models were employed: the first utilizes a forward neural network architecture, while the second leverages bootstrapping or bagging neural networks to enhance detection accuracy for low-labeled classes. Support Vector Machine (SVM) is implemented to serve as a baseline for comparison. Experimental results demonstrate that ANNs exhibit significant improvements in detection accuracy compared to SVM. Moreover, the neural network with bagging model showcases enhanced recall values and a substantial improvement in accurately detecting instances belonging to low-labeled classes in both driving style road traffic. [ABSTRACT FROM AUTHOR]

The development of sustainable smart cities (SSCs) is pivotal for contemporary urban expansion, as highlighted by numerous international frameworks and scholarly studies. This study focused on Saudi Arabia to identify and assess the key challenges impeding the evolution of intelligent and sustainable urban environments. By categorizing and hierarchically analyzing these impediments, the research isolates the most significant barriers to SSC advancement. A total of 18 obstacles were identified, organized into four categories, and reviewed using existing scholarly literature. Semi-structured interviews were conducted with stakeholders engaged in executing SSC initiatives, enriching the research from a practical perspective. Additionally, a survey ranked these barriers, revealing that social and economic factors pose the most significant challenges, followed by governance and legal, technology, and environment. The findings of this study offer critical insights for policymakers and governments to mitigate the barriers to SSC development efforts. [ABSTRACT FROM AUTHOR]

This article presents an innovative IoT-enabled single-camera speed sensor designed for smart city applications. The research encompasses the development of both hardware and software components, focusing on a computer vision and artificial neural network-based system. A novel aspect of this study is the implementation of a distance-measuring algorithm that eliminates the need for expensive LIDAR sensors traditionally used in speed cameras. Instead, the system relies on convolutional neural networks (CNN) and computer vision algorithms to estimate vehicle speed accurately. Field testing was conducted in real conditions over several months, generating sufficient data to assess the device's ability to function under various adverse conditions. The results demonstrate the system's capability to perform vehicle speed detection consistently across diverse conditions, showcasing its potential as a scalable solution for urban traffic management. This makes the proposed system not only more affordable but also simpler to deploy and maintain, thereby enhancing its suitability for widespread use in smart city environments. [ABSTRACT FROM AUTHOR]

Sustainable urban development in the era of energy and digital transformation is crucial from a societal perspective. Utilizing modern techniques for analyzing large datasets, including machine learning and artificial intelligence, enables a deeper understanding of historical data and the efficient prediction of future events based on data from IoT sensors. This study conducted a multidimensional historical analysis of air pollution to investigate the impacts of energy transformation and environmental policy and to determine the long-term environmental implications of certain actions. Additionally, machine learning (ML) techniques were employed for air pollution prediction, taking spatial factors into account. By utilizing multiple low-cost air sensors categorized as IoT devices, this study incorporated data from various locations and assessed the influence of neighboring sensors on predictions. Different ML approaches were analyzed, including regression models, deep neural networks, and ensemble learning. The possibility of implementing such predictions in publicly accessible IT mobile systems was explored. The research was conducted in Krakow, Poland, a UNESCO-listed city that has had long struggle with air pollution. Krakow is also at the forefront of implementing policies to prohibit the use of solid fuels for heating and establishing clean transport zones. The research showed that population growth within the city does not have a negative impact on PMx concentrations, and transitioning from coal-based to sustainable energy sources emerges as the primary factor in improving air quality, especially for PMx, while the impact of transportation remains less relevant. The best results for predicting rare smog events can be achieved using linear ML models. Implementing actions based on this research can significantly contribute to building a smart city that takes into account the impact of air pollution on quality of life. [ABSTRACT FROM AUTHOR]

Smart Cities have emerged as a promising approach for transforming urban living into more sustainable and resilient systems through technology-driven innovations and data-driven governance. Despite its growing implementation and diffusion around the globe, many questions surrounding this topic have emerged. Many critics have emerged since its first conceptualization in the first decade of the current century. Smart Cities have been criticized for their utopian objectives and the security, safety, people's freedom, and privacy within these systems. There are also capitalistic and neoliberalism-related critiques. Other critiques also highlight the current climate cost of Smart City initiatives. In the context of those critiques, bioclimatic and passive strategies might provide an interesting evolution of the concept but seem to be left in the background. This paper aims to contribute to the understanding of the linkages between environmental design approaches and the Smart City discourse. The contribution will explore to which extent bioclimatic and environmental design principles are present in the Smart City discourse and what the patterns are inside the current literature. The methodology of the research included a quali-quantitative analysis of the body of literature in Scopus and a bibliometric analysis using the VOS Viewer tool. [ABSTRACT FROM AUTHOR]

Background: Monitoring the lifestyles of older adults helps promote independent living and ensure their well-being. The common technologies for home monitoring include wearables, ambient sensors, and smart household meters. While wearables can be intrusive, ambient sensors require extra installation, and smart meters are becoming integral to smart city infrastructure. Research Gap: The previous studies primarily utilized high-resolution smart meter data by applying Non-Intrusive Appliance Load Monitoring (NIALM) techniques, leading to significant privacy concerns. Meanwhile, some Japanese power companies have successfully employed low-resolution data to monitor lifestyle patterns discreetly. Scope and Methodology: This study develops a lifestyle monitoring system for older adults using low-resolution smart meter data, mapping electricity consumption to appliance usage. The power consumption data are collected at 15-min intervals, and the background power threshold distinguishes between the active and inactive periods (0/1). The system quantifies activity through an active score and assesses daily routines by comparing these scores against the long-term norms. Key Outcomes/Contributions: The findings reveal that low-resolution data can effectively monitor lifestyle patterns without compromising privacy. The active scores and regularity assessments calculated using correlation coefficients offer a comprehensive view of residents' daily activities and any deviations from the established patterns. This study contributes to the literature by validating the efficacy of low-resolution data in lifestyle monitoring systems and underscores the potential of smart meters in enhancing elderly people's care. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments. [ABSTRACT FROM AUTHOR]