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

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

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

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

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]

Internet of Things (IoT) sensor networks are connected systems of physical devices set with actuators, sensors, and communication abilities, allowing them to gather, spread, and exchange information with centralized methods. These networks are essential in numerous businesses, such as healthcare, manufacturing, agriculture, and smart cities, as they deliver real-time observation, data-driven insights, and automation. Threat recognition in IoT sensor networks is a vital feature of safeguarding the protection and consistency of interconnected systems in the IoT. As IoT sensor networks endure to increase across various industries, the vulnerability to malicious actions and cyber-attacks increases. Threat recognition utilizing deep learning (DL) leverages neural networks to examine complex patterns and anomalies in data, permitting the identification of potential safety threats. DL techniques like convolutional neural networks (CNNs) or recurrent neural networks (RNNs) excel at learning complex representations of data and feature extraction, making them suitable for identifying sophisticated attacks in different fields, including cybersecurity. This research develops a Hybrid Sine-Cosine Chimp Optimization Feature Selection with a Deep Learning (HSCCOFS-DL) approach for Threat Recognition in IoT Sensor Networks. The foremost aim of the HSCCOFS-DL system lies in the automated detection of threats using DL models. To accomplish this, the HSCCOFS-DL approach undergoes a data normalization process. Besides, the selection of features can be performed using the HSCCO algorithm. Meanwhile, the symmetrical autoencoder (SAE) technique effectively classifies threats. Finally, the sparrow search algorithm (SSA) can be applied to the selection of the hyperparameter of the SAE system. The experimental assessment of the HSCCOFS-DL technique takes place on a benchmark dataset. The simulation results indicated that the HSCCOFS-DL approach attains enhanced performance over other methods. [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]

Communication networks are crucial components of the underlying digital infrastructure in any smart city setup. The increasing usage of computer networks brings additional cyber security concerns, and every organization has to implement preventive measures to protect valuable data and business processes. Due to the inherent distributed nature of the city infrastructures as well as the critical nature of its resources and data, any solution to the attack detection calls for distributed, efficient and privacy preserving solutions. In this paper, we extend the evaluation of our federated learning framework for network attacks detection and classification based on random forest. Previously the framework was evaluated only for attack detection using four well-known intrusion detection datasets (KDD, NSL-KDD, UNSW-NB15, and CIC-IDS-2017). In this paper, we extend the evaluation for attack classification. We also evaluate how adding differential privacy into random forest, as an additional protective mechanism, affects the framework performances. The results show that the framework outperforms the average performance of independent random forests on clients for both attack detection and classification. Adding differential privacy penalizes the performance of random forest, as expected, but the use of the proposed framework still brings benefits in comparison to the use of independent local models. The code used in this paper is publicly available, to enable transparency and facilitate reproducibility within the research community. [ABSTRACT FROM AUTHOR]

Projects to integrate digital technologies into the fabric of city life depend upon specific visions of politics and technology. In the process of their realization, they re-constitute the identities, agencies, and relations of human inhabitants, re-defining what it means to be a citizen. This article draws on the idiom of co-production and framework of constitutionalism from Science and Technology Studies (STS) to analyze the coming into being of a form of citizenship with smartphone technologies in Boston in the 2010s. When the Boston Mayor’s Office of New Urban Mechanics (MONUM) used newly available smartphone apps to reconfigure the connection among city residents and government, they brought into being a specific mode of citizenship. I term this mode of citizenship “mechanic” to draw attention to the qualities of passivity, infrastructure, and connectedness that characterized MONUM’s early digital citizenship projects. I argue that recognizing that the constitution of the human, and specifically of citizens, is at stake in smart city projects entails re-thinking ethical analysis in contexts of smart cities. Instead of seeking to perform ethical assessments of technological consequences (“ethics of” approach), scholars might begin with a situated analysis of how humans as citizens are constituted through smart city projects (“ethics in” approach). By identifying the political affordances and commitments of the emergent digital citizenship models, scholars can make visible alternative forms of living and engaging politically in the city. [ABSTRACT FROM AUTHOR]

Smart cities are rapidly gaining momentum around the world as new opportunities to shift towards a sustainable urban future. Despite their promising solutions, there is so far little evidence on how smart city initiatives can achieve urban sustainability. This study explores the impact of smartness on urban sustainability by introducing the concept of smart complementarity and investigating whether the systemic governance of smart complementarities has a positive impact on sustainability. To this end, we employ a panel vector autoregression model with panel data for 26 smart cities from 2009 to 2019 to test the complex dynamic relationships between smart city drivers and dimensions of sustainability. Such analysis seems crucial for smart city governance and whether the vision of a smart agenda is compatible with urban sustainability. The findings suggest that while smart city initiatives have improved economic sustainability, they have compromised environmental and social sustainability. This points to the need for a coordinated approach to address the complexity of urban systems and sustainability challenges. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the global urbanization process is further accelerating, the number of urban people is also steadily increasing. With the continuous growth of the urban population, the use of various functional facilities in the city is gradually becoming saturated, which seriously restricts the development of the city. Therefore, real-time perception and prediction of the operational status of various functional urban facilities, as well as environmental safety monitoring in the city, are of great significance for improving the functionality and livability of the city. In complex urban environments, multi-source data is interrelated, and the noise reduction of multi-source data and the integration of this correlation still face great challenges. At the same time, how to apply urban intelligent monitoring in the reconstruction of old cities is rarely mentioned. Based on the above problems, this paper proposes a data noise reduction model based on a wavelet algorithm and combines it with the Macroscopic Fundamental Diagram (MFD) multi-source data fusion structure proposed in this paper to provide a theoretical basis for the construction of an urban intelligent monitoring model. Then, the author constructed a data-driven urban safety environment monitoring model and a multi-source data-based urban congestion monitoring model, which have good experimental results and certain practical value. At the end of the paper, the author briefly discussed the application of the smart city monitoring model in the reconstruction of old cities, hoping to provide certain guidance for further integration in the future. [ABSTRACT FROM AUTHOR]

With the advancement of urbanization in China, there is a growing demand for intelligent landscape management in urban science and innovation parks. To improve landscape management efficiency in science and technology innovation parks, this study adjusted the Inception module and activation function of the GoogLeNet algorithm, designed an improved GoogLeNet algorithm, and based on this, constructed a multi clue science and technology innovation park landscape vegetation intelligent recognition system. This is also the main contribution of this study. The experimental results show that the average image recognition accuracy of the Improve GoogLe Net+MM model designed from the study is 92.95%, which is higher than all the comparison models. Additionally, the computation time is significantly lower than the comparison models when the computation data volume is larger. The model designed in this study has a certain application value to improve the performance of the landscape intelligent management system within smart cities' science and technology parks. [ABSTRACT FROM AUTHOR]

With the continuous development of the IoT technology, the concept of smart city has gradually become one of the key elements of urban planning and design. The purpose of this study is to explore the smart urban landscape design and planning based on the IoT, and to provide new perspectives and methods for the future urban development through in-depth research and analysis of related issues. First, this study reviews the relevance of smart city and landscape design, highlighting the importance of IoT technology in urban planning. Secondly, through case analysis and field trips, the successful experiences and challenges of smart cities that have implemented the IoT technology are analysed. In terms of design and planning, this study proposes a landscape design framework integrating IoT technology, emphasizing the interaction between urban landscape and information technology to promote sustainable urban development. Finally, this study summarizes the relevant findings, and prospects the future development trend of smart urban landscape design planning, to provide useful guidance and inspiration for urban planners and designers. Through this study, theoretical support and practical experience can be provided for building a more intelligent and liveable urban environment. [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]

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]

By using advanced technologies and data analytics, smart cities can establish conditions that are both inclusive and accessible, addressing the distinctive needs of disabled people. This research aims to examine the benefits of smart city technologies and develop strategies for developing environments that serve the requirements of individuals with disabilities in Saudi Arabia. Using a sequential mixed method, the study uses the social disability model. The initial phase involves gathering quantitative data from 427 individuals with disabilities in Saudi Arabia. Further, qualitative data was obtained through semi-structured interviews with a sample of four professionals employed in Saudi smart city initiatives. Quantitative data is analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM), while qualitative data is analyzed using thematic analysis. Quantitative findings revealed the robustness of the measurement model, confirming the significant effects of Smart City Initiatives on Accessibility Enhancement, Inclusive Information, and Health and Wellbeing Improvement. The respondents indicated that they are satisfied with the initiatives and their effectiveness, providing them with equal services and opportunities without discrimination. The qualitative analysis further revealed themes, i.e., Technology Integration for Accessibility, Inclusive Design, Inclusive Planning for Health, and others. Participants indicated special consideration for implementing the designs and approaches to ensure inclusivity and availability of services to disabled people. Besides, implementing infrastructure and policies to ensure the health and wellbeing of disabled people also remained prevalent. Hence, it is concluded that smart city initiatives break obstacles and improve the wellbeing of individuals with disabilities. Improved healthcare services and inclusive urban planning highlight the transformative effect of these initiatives on health and wellbeing, promoting an equitable and sustainable services environment. Finally, research implications and limitations are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]

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]

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 urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas. This problem is an NP (nondeterministic polynomial time)-hard problem because of involving many objective functions, many constraints, and complex search space. Moreover, this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses. Different types ofMulti-Objective Optimization Algorithms (MOOAs) based on Artificial Intelligence (AI) have been frequently employed, but their ability and performance have not been evaluated and compared properly. This paper aims to employ and compare three commonly used MOOAs i.e. NSGA-II, MOPSO, and MOEA/D in urban land-use allocation problems. Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages. The objective functions of this study are compatibility, dependency, suitability, and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment. Evaluation of results is based on the dispersion of the solutions, diversity of the solutions' space, and comparing the number of dominant solutions in Pareto-Fronts. The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses. However, the run time of NSGA-II is the worst, related to the Diversity Metric (DM) which represents the regularity of the distance between solutions at the highest degree. Moreover, MOPSO provides the best Scattering Diversity Metric (SDM) which shows the diversity of solutions in the solution space. Furthermore, In terms of algorithm execution time, MOEA/D performed better than the other two. So, Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems. [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]

Managing e-waste involves collecting it, extracting valuable metals at low costs, and ensuring environmentally safe disposal. However, monitoring this process has become challenging due to e-waste expansion. With IoT technology like LoRa-LPWAN, pre-collection monitoring becomes more cost-effective. Our paper presents an e-waste collection and recovery system utilizing the LoRa-LPWAN standard, integrating intelligence at the edge and fog layers. The system incentivizes WEEE holders, encouraging participation in the innovative collection process. The city administration oversees this process using innovative trucks, GPS, LoRaWAN, RFID, and BLE technologies. Analysis of IoT performance factors and quantitative assessments (latency and collision probability on LoRa, Sigfox, and NB-IoT) demonstrate the effectiveness of our incentive-driven IoT solution, particularly with LoRa standard and Edge AI integration. Additionally, cost estimates show the advantage of LoRaWAN. Moreover, the proposed IoT-based e-waste management solution promises cost savings, stakeholder trust, and long-term effectiveness through streamlined processes and human resource training. Integration with government databases involves data standardization, API development, security measures, and functionality testing for efficient management. [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]

The integration of internet of things (IoT) and big data technologies is transforming the overall perspective of managing various sectors of modern life; with higher educational sectors being no exception of this transformation. This paper explores the idea of a "smart university" as an extension of the overarching "smart city" framework, emphasizing the blending of IoT and big data technologies within higher education institutions. The study investigates the incorporation of IoT technologies throughout university campuses, including intelligent classrooms, smart infrastructure, and device networking. Moreover, the paper delves into the substantial role played by big data analytics in processing and extracting meaningful insights from extensive data generated by IoT devices in a Smart University. The use of predictive analytics, machine learning algorithms, and data-driven decision-making contributes to personalized learning experiences, adaptive campus management, and proactive maintenance of university facilities. Furthermore, this paper not only emphasizes the potential benefits of deploying IoT and big data in a university setting but also addresses challenges related to security, privacy, and ethical considerations. By embracing a comprehensive approach to technology integration, universities can leverage the capabilities of IoT and big data to establish intelligent, interconnected, and flexible educational environments that align with the broader vision of a smart city. [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]