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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]

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

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

The main concept of a smart city is to join manual items with electronics, software, sensors, and network connectivity for data contact via Internet of Things (IoT) gadgets. IoT improves production and efficiency by cleverly utilizing remote management, but security as well as privacy risk, rises. Cyber threats are progressing every day, affecting inadequate actions of safety and privacy. In a smart city, the exposed action of an individual or business can keep the whole city in danger. Owing to the trust of numerous modules of smart cities on data and communication techniques, cybersecurity tasks like information leakage and mischievous cyber-attacks. Malicious Activity Detection in the area of cybersecurity for smart cities is dominant in protecting the consistent organization of urban atmospheres. Leveraging innovative machine learning (ML) and deep learning (DL) approaches, mainly anomaly detection methods permits the identification of abnormal patterns and probable threats within the massive streams of data produced by smart city methods. So, this paper designs a Fusion of Optimization Algorithms and DL for Enhanced Malicious Activity Recognition (FOADL-EMAR) technique for cybersecurity in smart cities. The purpose of the FOADL-EMAR technique is to recognize the presence of malicious activities using optimal DL techniques in the environment of a smart city. To accomplish this, the FOADL-EMAR technique uses a Z-score data normalization model to measure the input dataset into a useful format. In addition, the FOADL-EMAR technique uses an improved sand cat swarm optimization (ISCSO) algorithm that can be applied to select an optimal subset of features. An ensemble of three DL techniques is used namely long short-term memory (LSTM), deep neural networks (DNNs), and extreme learning machine (ELM) for malicious activity detection. The Harris Hawks Optimization (HHO) approach has been applied to improve the detection results of the ensemble models. The simulation analysis of the FOADL-EMAR methodology occurs utilizing a benchmark database. The experimentation values demonstrate the improved detection outcomes of the FOADL-EMAR algorithm over other existing methods in terms of different measures. [ABSTRACT FROM AUTHOR]

Information and communication technologies (ICT) have a recent impact on governance and public administration. Electronic government (e-government) services were created to streamline administrative processes and enhance citizen engagement on the one hand, and to build new governance models that would empower individuals, involve them in the decision-making process, and increase transparency on the other. Many individuals are doubtful about smart city projects because of the security issues that arise in such environments. In essence, internet of things gadgets are security flaws. Concerns about the proliferation of IoT sensors and the tighter coupling of infrastructure silos in cities are well-founded. The major objective of the study is to identify the influencing factors of smart cities on e-government in Saudi Arabia. Additionally, this research explores the definition of e-government, and its supporting technologies like smart cities, IoT, big data, cloud computing and other digital government platforms. After exploring the detailed definitions, the study identifies the challenging scenarios of e-government in Saudi Arabia, and discusses the different opportunities. This study also concentrates on the good practices to be followed to overcome the challenges. Furthermore, this study can be used in future research for solving real time challenges of e-government. [ABSTRACT FROM AUTHOR]

Over the last years, a huge number of things has been connected to the Internet to support applications for smart cities, smart industries, smart agriculture and smart environment, etc. In order to fix bugs, update features or perform dynamic adaptations on these things, Efficient Firmware Update Over the Air (FUOTA) methods are required. In this paper, we propose a modular-based approach for the development of firmware of MCU-based IoT devices, and a new FUOTA method for devices communicating using LoRaWAN. This method enables partial and dynamic updates of modules forming the device firmware, and does not require a system reboot to apply these updates. This approach and this method have been implemented on STM32 MCUs. The experimental results show good performance in terms of update size and network traffic compared to the traditional FUOTA method and monolithic-based firmware development approach, which both impose full firmware updates. [ABSTRACT FROM AUTHOR]

The article proposes a concept of organizing access to the guaranteed resource of the 5 G cluster information environment, structured in the form of a hierarchy of logical network segments. This framework presents an interrupt access control scheme that exhibits survivability characteristics important to mission-critical applications. Analytically, the author's concept is represented by an irreversible Markov process, for which a system of equilibrium equations and stationary probabilities are defined. Based on the latter, a qualitative metric was formulated, which included such indicators as the average number of requests accepted for service in the 5 G cluster, the probability of losing an incoming request, and the average volume of network resource units used in the 5 G cluster. For the single input data that characterizes a real NS 5 G cluster, a comparison of the author's and known competing access control schemes in terms of formulated qualitative metrics is implemented. The obtained results showed that the author's concept prevails over the competitor in the range of low loads. Note that in this range, the advantage of the author's scheme over the competitor significantly increases with the increase in the number of network segments into which the information environment of the 5 G cluster is divided. [ABSTRACT FROM AUTHOR]

Security in smart cities is a challenging issue in urban environments as they depend upon interconnected technologies and data for effective services. To address security challenges, smart cities implement robust cybersecurity measures, including network monitoring, encryption, and intrusion detection systems. Detecting and mitigating possible security risks in drone network B5G is a crucial aspect of ensuring reliable and safe drone operation. It is necessary to establish sophisticated and robust attack detection techniques to defend against security threats as the use of drones becomes increasingly widespread and their applications diversify. This is due to the lack of privacy and security consideration in the drone's system, including an inadequate computation capability and unsecured wireless channels to perform advanced cryptographic algorithms. Intrusion detection systems (IDS) and anomaly detection systems can identify suspicious activities and monitor network traffic, such as anomalous communication patterns or unauthorized access attempts. Therefore, the study presents an enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection (EDMOA-DLAD) in Networks B5G for the purpose of Drones technique. The presented EDMOA-DLAD technique aims to recognize the attacks and classifies them on the drone network B5G. Primarily, the EDMOA-DLAD technique designs a feature selection (FS) approach using EDMOA. To detect attacks, the EDMOA-DLAD technique uses a deep variational autoencoder (DVAE) classifier. Finally, the EDMOA-DLAD technique applies the beetle antenna search (BAS) technique for the optimum hyperparameter part of DVAE model. The outcome of EDMOA-DLAD approach can be verified on benchmark datasets. A wide range of simulations inferred that the EDMOA-DLAD method obtains enhanced performance of 99.79% over other classification techniques. [ABSTRACT FROM AUTHOR]

Using and connecting different technologies to improve the quality of urban life is a significant challenge for smart cities. The interconnection of transport, energy, water, public lighting, and other urban infrastructure systems is critical to providing quality services to citizens and reducing environmental impact. However, data interoperability from these systems is a significant challenge, as data is mainly stored in different formats and locations from various sources. This paper examines the challenges of integrating existing datasets in intelligent cities and proposes solutions to create secure and interoperable data-sharing environments. To this end, an approach called data federation is discussed by implementing a federated platform for integrating such data. However, it is essential to note that there may be challenges in terms of cybersecurity and interoperability. This is where context brokers play a crucial role, regulating access to data by enforcing rules and providing security standards. In this way, these challenges can be managed appropriately, ensuring data protection and the system's proper functioning. Finally, it is essential to mention that this work is linked to a smart cities project, which aims to promote innovation in smart cities by implementing several innovative solutions. The study presented in this research contributes to understanding the challenges in smart cities. It proposes solutions for creating secure and interoperable data-sharing environments, which are essential both for the project's success and for the development of smart cities worldwide. [ABSTRACT FROM AUTHOR]

To address the limitations of 5 G, 6 G wireless networks are envisaged to provide sub-millisecond latency, ultra-high connection density, extremely high data rates, better coverage, reliability, availability, etc., for cloud computing, Internet-of-Everything, and cyber-physical systems. However, avoiding several trust-related issues in the design of the wireless networks may delay the goal. In recent years, Blockchain has become a promising technology that can transform various fields by providing innovative and profitable solutions. Blockchain's intrinsic features, such as immutability, decentralization, anonymity, and transparency, can corroborate trust among isolated networks to secure access control, authentication, better integrity, secrecy, and effectual resource sharing. This paper paints a holistic picture of 6 G wireless networks and Blockchain, including architecture, Blockchain-assisted 6 G services, deployment, 6 G-assisted Blockchain service models, consensus mechanisms for Blockchain-as-a-Service, 6 G-assisted mining and consensus, and others. It also sheds light on the integration possibilities of Blockchain and 6 G, its potential benefits, and the effects of consensus mechanisms on 6 G concerning security, scalability, and energy consumption, to name a few. The existing 6 G projects and applications have also been summarized, along with the discussion on research and projects to standardize 6 G. Various research directions for Blockchain, 6 G, and their integration have also been explored and reported. [ABSTRACT FROM AUTHOR]

This paper proposes a Distributed Software Architecture (DSA) for Smart Building (SB) based on the Reactive Manifesto (RM) principles. To follow the RM principles, we analyze the usage of different deployment approaches, particularly the impact of using a container orchestrator on the application layer. After running performance tests on the different configurations, the container orchestrator usage led to enhanced distributed processing, lowering the latency, increasing flexibility, enhancing security, and providing cost-effectiveness and scalability. We introduce the implementation of a modern DSA, developed following state-of-the-art cloud patterns and compliant with the RM for the SB context. Furthermore, we have ensured the reproducibility of this implementation by making the initial tests and overall architecture code available in public repositories. The research follows the Design Science Research (DSR) methodology for elaborating each phase until we get the artifact (DSA) and, with this, contribute to the Knowledge Base. The architecture was properly tested, considering the performance as the principal test layer. This solution is tailored for application in domains of the Internet of Things (IoT), focusing on the SB and a case study involving the Laboratory of Distributed Systems and Concurrent Programming (LaSDPC) at São Paulo University. Moreover, its applicability extends to IoT domains like smart home, smart campus, smart city, and health-related applications. [ABSTRACT FROM AUTHOR]

The EU Mission on Climate Neutral and Smart Cities is an ambitious initiative aiming to involve a wide range of stakeholders and deliver 100 climate-neutral and smart cities by 2030. We analysed the information submitted in the expressions of interest by 362 candidate cities. The majority of the cities' strategies for climate neutrality include urban transport as a main sector and combine the introduction of new technologies with the promotion of public transport and active mobility. We combined the information from the EU Mission candidate cities with data from the CORDIS and TRIMIS databases, and applied a clustering algorithm to measure proximity to foci of H2020 funding. Our results suggest that preparedness for the EU Mission is correlated with research and innovation activities on transport and mobility. Horizon 2020 activities specific to transport and mobility significantly increased the likelihood of a city to be a candidate. Among the various transport technology research pathways, smart mobility appears to have a major role in the development of solutions for climate neutrality. • Analysis of 362 candidate cities to the EU Mission on Climate Neutral and Smart Cities. • Urban transport is a main sector for climate neutrality. • Strategies combine new technologies, public transport and active mobility. • Horizon 2020 activities significantly increased the likelihood of a city to be a candidate. • Smart mobility has a major role in the development of solutions for climate neutrality. [ABSTRACT FROM AUTHOR]

Using unmanned aerial vehicles (drones) in security has become common in the Czech Republic. This paper focuses on using drones as an example of the application of smart technologies in conjunction with the Internet of Things (IoT) and state security forces, especially in cooperation with the police, fire and medical emergency services in the context of the new Smart Cities concept. Drones are one of the most suitable examples of use along with IoT within the security sector, which play a key role in the daily protection of citizens' safety, public order security and crisis management. It is related to developing and implementing 5G networks, which allow real-time transmission of data obtained by drones directly to information systems, and is also gaining importance. This technology enables fast and efficient communication between the different actors involved in an intervention and creates a platform for fast and effective emergency response. Among other things, paying attention to cybersecurity, such as securing data transmissions, protecting against cyber threats, complying with security standards and legislation, and introducing special procedures and methodologies applicable in crisis, is essential. [ABSTRACT FROM AUTHOR]

In the context of a 'smart campus', which serves as a specialized version of a smart city designed for educational institutions, there lies the potential to employ cutting-edge technologies like the Internet of Things (IoT), artificial intelligence (AI), and big data analytics[1]. These tools are directed towards establishing a more efficient, sustainable, and comfortable environment for the entire campus community. This study takes a step forward by concentrating on creating an optimal indoor environment within the campus, specifically tailored to individuals with specific environmental sensitivities. In our endeavor, we give precedence to two crucial environmental parameters: the heat index[2] and the air quality index[3], both known to have a significant impact on individual comfort and well-being. The goal is to optimize these factors, fostering a secure and favorable environment. To accomplish this, we propose the development of predictive models capable of forecasting heat index and air quality index values. By employing three prominent models - Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), and one-dimensional Convolutional Neural Networks (Conv1D) - we seek to determine the suitability of an environment, ultimately enhancing the well-being of those within the campus. [ABSTRACT FROM AUTHOR]

Smart and sustainable dermatology takes on a new dimension within Green Smart Cities with the integration of artificial intelligence (AI) into dermatological diagnosis. This study explores the success of deep learning models in accurately recognizing skin lesions, focusing on the use of the HAM10000 dataset. Our comparative analysis highlights the crucial impact of network architecture choices, data augmentation, and preprocessing on model performance. The results reveal that models leveraging transfer learning and fine-tuning on pre-trained networks excel in precision, underscoring their relevance in the context of smart green health. We also address opportunities for improvement in model generalization across diverse datasets and skin types. These findings provide a foundation for the development of more accurate skin lesion recognition models aligned with the principles of Green Smart Health, contributing to faster diagnostics, improved patient care, and ultimately, healthier Green Smart Cities. This work opens avenues for future research, such as exploring of the effectiveness of deep learning techniques in diverse health contexts and the integration of clinical data for more personalized dermatological diagnostics within Green Smart Cities. [ABSTRACT FROM AUTHOR]

The innovations in smart cities are crucial to integrating green technologies, including in the tourism sector, which can signif-cantly impact the sustainability of destinations. Conversational agents (CAs) are considered green technologies because they ofer solutions to reduce the negative impact of human activity on the environment while enhancing the customer experience through automated interactions. By combining these advances, sustainable tourism destinations can effectively leverage green technologies such as CAs to ofer quality attractions while preserving the natural environment. In this work, we present an analysis study and a comprehensive comparison of CA development tools. As a case study, we explore the power of Bot Framework Composer, a visual development tool from Microsoft, to create a CA that displays attractions in the Deraa Taflalet region as part of smart tourism. However, our goal goes beyond the creation of a simple CA. We also introduce a metamodel for the CA, identifying the fundamental concepts of Bot Framework Composer. This metamodel aims to establish a unified basis for creating a Domain-Specific Language (DSL) for designing CA in a consistent and portable way. [ABSTRACT FROM AUTHOR]

Numerous smart city frameworks have arisen in recent years as a response to the global urbanization problem. The emergence of frameworks with their distinct dimensions causes confusion in the selection of a framework and the implementation of a smart city. Therefore, more thorough research on the smart city dimension is necessary to build a more comprehensive smart city framework. This study conducted a systematic literature review to find the most prevalent dimension in the subject by examining a collection of 30 papers from five reputable sources that discuss smart city frameworks. A total of 38 smart city dimensions were retrieved from 12 smart city frameworks and 9 smart city maturity models. The 38 dimensions are then categorized according to their similarity. This study results in six main categories of smart city dimensions, including economy, people, environment, government, living, and branding. [ABSTRACT FROM AUTHOR]

The text that follows gives an overview of the numerous uses of artificial intelligence (AI) and data science in a variety of fields, focusing on their potential advantages and drawbacks. Healthcare, finance, education, smart cities, and the development of disease therapies have all showed promise for AI. The study examines several AI models, such as ontological, statistical, hybrid, and biological models, in addition to the restrictions placed by deep learning techniques. The study on brain architecture and function is also included in the article, along with applications of multivariate pattern analysis, bioinspired systems, and machine learning to the analysis and processing of enormous datasets. The literature study emphasizes how critical it is to address societal and ethical issues with AI. [ABSTRACT FROM AUTHOR]

• Introduced SWM 4.0 by considering Industry 4.0 technologies in waste management. • Reviewed I4.0 role in waste types, processes, and strategies of waste management. • Developed SWM 4.0 framework through smart factories, cities, businesses, and people. Smart Waste Management (SWM) discusses the waste management process for different types of waste while introducing an intelligent approach to controlling the amount of waste. This paper introduces SWM4.0, which applies Industry4.0 (I4.0) technologies in various related events. First, the paper presents a systematic literature review on the role of I4.0 technologies in SWM activities regarding waste types, waste management processes, and 5R strategies. Then, existing solutions supporting SWM4.0 are extracted to develop a framework for exploring the use of I4.0 technologies. This framework includes sharing the four main pillars that contribute to the success of SWM4.0, namely smart people, smart cities, smart enterprises, and smart factories. Furthermore, this review suggests the possibility of unifying and extending existing solutions and identifying the necessary links and interfaces for researchers. For managerial implications, the framework identifies future strategies to fulfill specific SWM tasks and to foster new technological solutions for future research. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has emerged as a transformative technology with applications spanning diverse domains, from healthcare to smart cities. Efficient utilization of IoT data is essential for extracting meaningful insights and supporting decision-making processes. This research paper focuses on the critical task of feature extraction from IoT data, with a particular emphasis on the IoT 2023 dataset—a comprehensive and widely-used benchmark dataset in the IoT domain. In this study, exploration and comparison of various feature extraction methods are undertaken to enhance understanding of IoT 2023 dataset characteristics and its potential applications. Traditional statistical techniques, as well as machine learning-based approaches for feature extraction, are investigated. Evaluation of these methods is based on their ability to capture relevant information, reduce dimensionality, and enhance the performance of downstream IoT analytics tasks. Extensive experiments are conducted, and a comprehensive performance analysis is presented to guide the selection of the most suitable feature extraction approach for specific IoT applications. The outcomes of this research contribute to ongoing efforts to harness the potential of IoT data by offering a comprehensive understanding of the impact of feature extraction methods on data quality and predictive accuracy. This aids in paving the way for more informed and effective IoT solutions in 2023 and beyond. [ABSTRACT FROM AUTHOR]

Among the contemporary concepts fostering active innovation and the development of information support for management systems at the level of individual cities, the Smart City stands prominent. For port operations management at the port level, the notion of Smart Port is instrumental. Additionally, the fusion of these two aforementioned concepts in cities where the port assumes the role of a city-forming enterprise gives rise to the concept of Smart Port-City. This article aims to ascertain the role of the Smart Port-City concept as foundational for initiating projects that facilitate the balanced development of transportation networks and the corresponding infrastructure of both the city and the port, employing modern methodologies and technologies. Principal outcomes. The system of joint city and port development objectives has been examined. The structure of functional domains within the Smart Port-City concept has been proposed, amalgamating the two foundational components. The sequence of processes for realizing Smart Port-City objectives through relevant projects has been delineated. Conclusions. The system of joint city and port development objectives within the transportation domain serves as the cornerstone for launching transportation-centric projects benefitting both the city and the port. [ABSTRACT FROM AUTHOR]

Connecting to the internet will increase our computing challenges as it becomes an integral part of our daily lives. Therefore, it is necessary to advance the service qualities of Internet of Things (IoT) applications because the data produced by all these devices will need to be processed quickly and sustainably. Previously, cloud data centers with large capacity interface IoT devices with support servers. While IoT devices proliferate and generate massive amounts of data, communicating between devices and the Cloud is becoming more complex and harder, resulting in high costs and inefficiencies. Fog computing emerges as an approach to address the growing demand for IoT solutions. In this article, an IoT-fog-cloud application's general framework is developed, followed by an algorithm for Energy efficiency through an integrated approach computation model. Fog-Enabled Smart Cities (FESC) are proposed to minimize service delay and response time by using a fog offloading policy for the fog-enabled IoTs. Also, we developed an analytical model evaluating the proposed framework's effectiveness in reducing the delay of IoT services. Comparing the proposed model and the Alternating Direction Method of Multipliers (ADMM-VS) algorithm, the proposed model performs significantly better. Thus, by optimizing response and processing times, fog-enabled smart grids determine whether computation will be performed autonomously or semi-autonomously on fog nodes or in the Cloud. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology involves a network of interconnected devices and sensors that gather and exchange information. In smart cities, IoT devices were utilized in several fields including energy, transportation, waste management, healthcare, etc., to improve the overall quality of life and sustainability of the populace. But, as the usage of IoT increases, the cybersecurity and data privacy become a concern of safety. An anomaly detection system helps to identify possible data breaches or cyber-attacks by identifying abnormal data patterns. Deep learning (DL) driven anomaly detection has emerged as an effective and powerful method for identifying abnormal behaviours or patterns in the data domain. This technique leverages the abilities of a deep neural network for automated learning of complex patterns and representations from data, which make it better for anomaly detection task where irregularities cannot be easily defined by handcrafted rules. This paper establishes a new Billiard Based Optimization with Deep Learning Driven Anomaly Detection and Classification (BBODL-ADC) technique in IoT-assisted Sustainable Smart Cities. The goal of the BBODL-ADC technique lies in the proper recognition and classification of anomalies in the IoT-assisted smart city. To obtain that, the BBODL-ADC system applies a binary pigeon optimization algorithm (BPEO) algorithm for the effectual selection of features. Besides, the BBODL-ADC technique utilizes Elman recurrent neural network (ERNN) approach for the recognition and classification of anomalies. Moreover, the BBO system can be used for better parameters chosen by the ERNN algorithm. The stimulation value of the BBODL-ADC algorithm can be executed benchmark database. The achieved outcomes demonstrate the remarkable outcome of the BBODL-ADC methodology of 95.69% and 99.21% compared to existing models under dataset-1 and dataset-2. [ABSTRACT FROM AUTHOR]

Car manufacturers around the globe are in a race to design and build driverless cars. The concept of driverless is also being applied to any moving vehicle such as wheelchairs, golf cars, tourism carts in recreational parks, etc. To achieve this ambition, vehicles must be able to drive safely on streets stay within required lanes, sense moving objects, sense obstacles, and be able to read traffic signs that are permanent and even temporary signs. It will be a completely integrated system of the Internet of Things (IoT), Global Positioning System (GPS), Machine Learning (ML)/Deep Learning (DL), and Smart Technologies. A lot of work has been done on traffic sign recognition in the English language, but little has been done for Arabic traffic sign recognition. The concepts used for traffic sign recognition can also be applied to indoor signage, smart cities, supermarket labels, and others. In this paper, we propose two optimized Residual Network (ResNet) models (ResNet V1 and ResNet V2) for automatic traffic sign recognition using the Arabic Traffic Signs (ArTS) dataset. Additionally, the authors developed a new dataset specifically for Arabic Traffic Sign recognition consisting of 2,718 images taken from random places in the Eastern province of Saudi Arabia. The optimized proposed ResNet V1 model achieved the highest training and validation accuracies of 99.18% and 96.14%, respectively. It should be noted here that the authors accounted for both overfitting and underfitting in the proposed models. It is also important to note that the results achieved using the proposed models outperform similar methods proposed in the extant literature for the same dataset or similar-size dataset. [ABSTRACT FROM AUTHOR]

The Kingdom of Saudi Arabia has experienced a notable impact as a result of population growth. Consequently, there is a heightened concern regarding urban air pollution and its effects on both individuals and society. This issue is of great importance in the development and expansion of cities within the region. The presence of air pollutants has been linked to various negative health outcomes, including the development of diseases, allergic reactions, and even fatalities in humans. Additionally, air pollution can have detrimental effects on other living organisms, such as animals and food crops, and can also cause harm to the natural environment, including contributing to climate change, ozone depletion, and land degradation in urban areas. The present study employs statistical analysis to regulate spatial theory to examine air pollution in the urban areas of Bisha and Abha in the Kingdom of Saudi Arabia. The results showed that carbon emissions are lower in Abha than Bisha. The spatial theory shows that there are no carbon emissions from the factories in Abha and Bisha as the factories are located outside of the urban reach of both cities. The research findings indicate significant differences at a significance level of 0.05 in terms of the mean responses of the study participants from Abha and Bisha cities. The results favored Abha, with a study concerning carbon emissions was 0.005. In addition to the aforementioned results, various conclusions and recommendations were derived from the present investigation. [ABSTRACT FROM AUTHOR]

A considerable amount of non-dispatchable photovoltaic and wind power have always been planned in smart cities, however, the problem of massive energy storage has not yet been solved which limits the use of green energy on larger scale. At present the only battery energy storage is available, and it is effective only for storing modest quantities of energy for short periods of time. The other storage technology options are not often commercially available items; rather, they are just good concepts that need to be tested for viability. Currently, the only alternative options for turning an urban development into one that exclusively uses green energy is to use that energy to generate hydrogen through electrolyzers, then use this fuel to generate the required electricity in order to stabilize the grid. Even more appealing is the idea of using wind and photovoltaic energy to transform smart communities into a centre for producing hydrogen in addition to a city that solely uses renewable energy. The most likely solution, absent an urgent debate inside the science establishment, will be to import electricity from the burning of hydrocarbons while continuing to pay carbon offsets, which is incompatible with the goal of using only renewables. The smart city has not officially accepted this issue, just like the science establishment. • Hydrogen fuel is a high-density energy storage solution for future smart cities. • Hydrogen has a variety of potential applications in smart cities as storing energy. • Hydrogen fuel is being a key component for smart cities of energy storage needs. • Hydrogen plays increasingly important role in energy needs of future smart cities. [ABSTRACT FROM AUTHOR]

Urban development and growth have a significant impact on the environment, contributing to ongoing climate change and affecting the resilience of urban communities. However, cities also have the potential to become innovation centers and drive the urgent green transition. Smart cities, in particular, rely on digital services to enhance citizens' quality of life while minimizing their environmental footprint. Therefore, public authorities and policymakers must leverage technological innovations and smart sensing to gather and process relevant data in real-time, supporting citizens' well-being through dedicated services. This work aims to provide a perspective toward healthier and more resilient smart cities by integrating personal, contextual, and environmental data to enhance Early Warning Systems and resiliency planning. Existing studies on the usage of smart sensing technologies in healthcare and environmental assessment services are reviewed to present existing opportunities in both fields. A critical analysis of the background is provided, highlighting the potentials and challenges of the proposed smart city management protocol, leveraging data directly collected by citizens - viewed in this sense as data prosumers. • Smart sensing for healthcare and environmental quality mapping is explored. • Environmental-driven Early Warning Systems can support e-health in smart cities. • Inclusive governance should engage citizens as data prosumers for their well-being. • Smart city management protocol should be based on human-centered Real Time Data. • Interoperable platforms combining medical and environmental data serve stakeholders. [ABSTRACT FROM AUTHOR]

China plays a significant role in the global product supply chain through its exports. To remain competitive, Chinese firms need to manufacture products cost-effectively. Revamping innovative cities can contribute to this goal, but empirical evidence in this area is limited. Therefore, this paper scrutinizes the innovative city pilot policy's consequences on firms' export performance and product quality. We employ the time-varying Difference in Difference (DID) model and harness a comprehensive firm-by-product level database to estimate the co-movement between the pilot intervention and the performance of the export product. Based on the findings, we unveil that a smart intervened city can significantly boost firms' export product quality, which still holds after multiple robustness checks. Furthermore, innovative cities heighten the quality of the manufactured products when we consider firm-level (i.e., private, foreign, and ordinary trade enterprises), industry-level (i.e., low-polluting, capital-intensive, and technology-intensive industries), and regional (i.e., Middle, and Western regions) heterogeneities. In addition, the intervened smart city strategy impacts the efficacy of export product quality through policy and innovation-driven effects. Over and above, policymakers and experts should support state-of-the-art ideas while considering local conditions, providing guidance for decision-making in China's innovative development strategy. [ABSTRACT FROM AUTHOR]

Photorealistic 3D models are important data sources for digital twin cities and smart city applications. These models are usually generated from data collected by aerial or ground-based platforms (e.g., mobile mapping systems (MMSs) and backpack systems) separately. Aerial and ground-based platforms capture data from overhead and ground surfaces, respectively, offering complementary information for better 3D mapping in urban areas. Particularly, backpack mapping systems have gained popularity for 3D mapping in urban areas in recent years, as they offer more flexibility to reach regions (e.g., narrow alleys and pedestrian routes) inaccessible by vehicle-based MMSs. However, integration of aerial and ground data for 3D mapping suffers from difficulties such as tie-point matching among images from different platforms with large differences in perspective, coverage, and scale. Optimal fusion of the results from different platforms is also challenging. Therefore, this paper presents a novel method for the fusion of aerial, MMS, and backpack images and point clouds for optimized 3D mapping in urban areas. A geometric-aware model for feature matching is developed based on the SuperGlue algorithm to obtain sufficient tie-points between aerial and ground images, which facilitates the integrated bundle adjustment of images to reduce their geometric inconsistencies and the subsequent dense image matching to generate 3D point clouds from different image sources. After that, a graph-based method considering both geometric and texture traits is developed for the optimal fusion of point clouds from different sources to generate 3D mesh models of better quality. Experiments conducted on a challenging dataset in Hong Kong demonstrated that the geometric-aware model could obtain sufficient accurately matched tie-points among the aerial, MMS, and backpack images, which enabled the integrated bundle adjustment of the three image datasets to generate properly aligned point clouds. Compared with the results obtained from state-of-the-art commercial software, the 3D mesh models generated from the proposed point cloud fusion method exhibited better quality in terms of completeness, consistency, and level of detail. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) contains many smart devices that collect, store, communicate, and process data. IoT implementation has performed novel opportunities in industries, environments, businesses, and homes. Anomaly detection (AD) is helpful in IoT platforms that can recognize and prevent potential system failures, decrease downtime, enhance the quality of products and services, and improve overall operational efficacy. AD systems for IoT data contain statistical modelling, deep learning (DL), and machine learning (ML) approaches which detect patterns and anomalies in the data. This article introduces an Improved Bacterial Foraging Optimization with optimum deep learning for Anomaly Detection (IBFO-ODLAD) in the IoT network. The presented IBFO-ODLAD technique performs data normalization using Z-score normalization approach. For the feature selection process, the IBFO-ODLAD technique designs the IBFO algorithm to choose an optimal subset of features. In addition, the IBFO-ODLAD technique uses multiplicative long short term memory (MLSTM) model for intrusion detection and classification process. Furthermore, the Bayesian optimization algorithm (BOA) was executed for the optimum hyperparameter selection of the MLSTM model. The experimental outcome of the IBFO-ODLAD method was validated on the UNSW NB-15 dataset and UCI SECOM dataset. The experimental outcomes signified the improved performance of the IBFO-ODLAD algorithm with maximum accuracy of 98.89 % and 98.66 % validated on the UNSW NB-15 dataset and UCI SECOM dataset respectively. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) has developed significantly with the progress of the Internet of Things (IoT) which is allowed the cheap and fast acquisition of data in millions and billions of interrelated devices utilized throughout the whole world. RS scene classifier that purposes for classifying scene types for RS images has wide applications in several domains like urban planning, national defence security, environmental monitoring, and natural hazard detection. State-of-the-art deep learning (DL) successes are performed in a novel wave of RS scene classification applications, but it is the absence of explainability and trustworthiness. An intrusion detection system (IDS) plays a vital role to ensure security in the RS-based IoT environment. In this aspect, this study presents an ebola optimization algorithm with deep learning-based scene classification and intrusion detection (EOADL-SCID) technique on IoT-enabled remote sensing images. The aim of the EOADL-SCID system lies in the effectual scene classification of remote sensing images and intrusion detection. It involves a two-stage procedure. In the initial stage, the EOADL-SCID algorithm involves a modified DarkNet-53 feature extractor, EOA-based hyperparameter tuning, and graph convolution network (GCN) based classification. Next, in the second stage, the intrusion detection process takes place via two subprocesses namely variational autoencoder (VAE) based intrusion detection and skill optimization algorithm (SOA) based parameter tuning. The simulation outcomes of the EOADL-SCID approach are tested utilizing two benchmark databases and the experimental outcomes highlighted the improved performance of the EOADL-SCID algorithm on scene classification and intrusion classification processes. [ABSTRACT FROM AUTHOR]

Rapid urbanization is affecting the harmonious development of the environment and economy. Governments are developing new urban planning policies to achieve sustainable urban development. Using the Chinese smart city pilot policy as a natural experimental group, we use a difference-differences model to explore the relationship between smart cities and clean energy development. There are three findings in this paper. First, we find that smart city pilots drive clean energy development. Second, we find that digital technologies can reinforce the positive impact of smart city pilots and that innovation and financial inclusion have a positive mediating role. Third, we find that the impact of smart city pilots has a heterogeneous impact. Namely, pilots in coastal, large, and resource-based cities have a more positive impact on clean energy development. Existing smart city pilots significantly impact energy security and sustainable development. Our study provides a new perspective on urban planning and clean energy development in emerging countries such as China. • A difference-differences model to explore the effect of smart city pilot. • Smart city pilot drives clean energy development. • Innovation and financial inclusion have positive mediating roles. • Digital technology has a positive moderating effect. • Clean energy development in smart cities is distinctly heterogeneous. [ABSTRACT FROM AUTHOR]

Vehicle classification is widely used in intelligent transportation and smart cities. However, the good performance of vehicle classification often depends on a large number of labeling data, which leads to the high cost of manual labeling. How to use a small number of labeled samples with rich features to achieve good classification performance is a challenge in vehicle classification. To solve these problems, we propose a deep active learning framework called Feature Fusion Spatial Pyramid Pooling and Re -parameterization Visual Geometry Group (FFSPP-RepVGG). The framework is mainly divided into two parts: query strategy and feature extraction module. Specifically, the query strategy uses Feature Fusion (FF) to calculate the loss, thus defining uncertainty and being able to select more valuable samples for labeling training. The feature extraction module uses Spatial Pyramid Pooling and Re -parameterization Visual Geometry Group (SPP-RepVGG) model, which can extract more reliable image features. It can reduce the cost of data labeling and have better performance at the same time. The experimental results on the BIT-Vehicle data set and the car-10 data set show that the FFSPP-RepVGG framework has superior performance than that of the comparison models. [ABSTRACT FROM AUTHOR]

To improve the data communication processing capability of smart city informatization construction and defend against malicious joint attacks of internal communication participants, we studied the use of function encryption, blockchain, differential privacy and other technologies to defend against weight disclosure, participation in collusion attacks, single point failures and other issues in the federal learning process, and introduced edge computing and asynchronous communication in the classic federal learning framework, Improve the communication efficiency of the model while ensuring the accuracy. The research results show that the accuracy of FE-BDP algorithm can maintain above 95 % for a long time, and the change is not significant with the increase of the number of users, indicating that the algorithm has strong stability. The loss value of FE-LDP model is significantly smaller than that of other models and can be stabilized below 0.05. The data aggregation time of blockchain encryption technology is less than 1.0s. The edge-asynchronous communication framework can be applied to multiple urban scenarios and achieve effective data communication, with the highest accuracy rate of 93.87 % and the lowest communication cost of 1315.29s. The research results show that the security encryption fusion technology can effectively protect user data privacy, and the edge-asynchronous communication framework has obvious effect on improving communication efficiency, which has important application value for promoting the construction of smart city informatization. [ABSTRACT FROM AUTHOR]

With the construction of smart cities, smart ports are developing rapidly, providing favorable conditions for enhancing port competitiveness, promoting industrial upgrading, and economic transformation. With the continuous progress of society and science and technology, the safety risks of intelligent ports are also increasing. In order to better address these risks, it is necessary to establish a stable and efficient management system. This paper applies data mining and fuzzy control theory technologies for large-volume, high-precision real-time information collection, based on the research of the BP neural network algorithm. By establishing and analyzing a safety risk management system model based on the BP multilayer neuron network, it provides a rationality evaluation method and design concept. After designing the safety risk management system, this paper conducts a performance test on the system. The test results show that through optimization, the accuracy, generalization ability, and reliability of the model can be improved. Its accuracy is as high as 92% or more, fully demonstrating the system's reliability and superiority! [ABSTRACT FROM AUTHOR]

Smart cities thrive on supporting digital technology innovation of applications and patent protection in the entrepreneurial ecosystem. This research aimed to explore the significant trends in patents related to smart city applications worldwide. The researcher conducted a patent landscape analysis using 22,655 patent materials from the Lens.org database. The study's findings indicate that the number of patents for applications related to smart cities is rising annually. There is an opportunity to use innovations in smart city applications without having to pay royalties by using 1,443 patents that have been discontinued. With seven companies, American corporations have most of the patents for applications in smart cities. The most productive individual investor was Kim Soenghun from Korea, with 946 patents. The electricity (H), physics (G), and new technological developments (Y) patent sections dominate patents related to smart city applications. There were 11,903 simple family patents for smart city applications in seven country jurisdictions; the United States occupied the highest region. [ABSTRACT FROM AUTHOR]

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale. [ABSTRACT FROM AUTHOR]

QoS is an arguable feature of Message Queuing Telemetry Transport Protocol (MQTT) that is employed under constrained environment for communication over an unreliable network in smart cities. It gives extra control to the client for matching its needs according to the application but with confiscated network performance. For a reliable end-to-end service assurance, guaranteed QoS and controlled scalability, MQTT based IoT must be examined for various network parameters. In this work, an improved TCP based transparent MQTT network is proposed for massively deployed connected devices in smart cities. The mathematical modeling is carried out by analyzing end-to-end quality assurance. Network latency, content delivery rate, number of subscribe/publish requests and services offered by the MQTT broker for various topic IDs are then verified on physical network. These parameters are examined over a transparent gateway-based network for verifying the agreement of devised probability based mathematical model with the actual content delivery, service rate and request hold time at virtual machine based MQTT broker, local network broker and remote server respectively. The Improved MQTT model surpasses Apollo, RabbitMQ and Mosquitto server by evidencing the message queueing delay of 3.5 ms for QoS-0 and 3.6 ms for QoS-1 service IoT. [ABSTRACT FROM AUTHOR]

Green innovation is an important driving force for sustainable development. However China often imposes a wide variety of government regulations on green innovation One important reason behind these government regulations is the confinement of the cultural market. However, does this confinement actually affect the green innovation in China? By employing a 278 Chinese cities' dataset, we examine the effect of cultural reform pilot project on green innovation. Through the spatial difference-in-difference approach with the time trend, our results show that cultural reform pilot project (CRPP) is a significant determinist affecting the green innovation in China. Specifically, implementing CRPP promote green innovation in pilot cities which resulting from labour productivity exaltation, marketization rate increasing. The CRPP also have a spatial ripple effect which resulting from economic density promotion. Furthermore, the green innovation promotion is greater in cities which participating into World Technopolis Association, being included in the National Historical and Cultural Cities List and having high political hierarchy. Our conclusions still robustness after adopting a series of tests and alternative analyses. This paper not only provide evidence for the further implementation of cultural reform pilot project nationwide, but also provide policy implications on sustainable development in the post Covid-19 era. • Based on the theory of economic geography and the perspective of deregulation. • Examine both the direct and indirect effects of CRPP on green innovation via SDID approach. • Implement CRPP promotes green innovation both in pilot cities and neighbouring cities. • CRPP exalt ate labour productivity, increase marketization rate and promote economic density. • Effect of CRPP on green innovation exhibits heterogeneity on cities. [ABSTRACT FROM AUTHOR]

ICT tools based on abstract spaces can be considered as the "younger sibling" of GIS tools. While GIS tools based on real spaces, i.e. those that are easy for users to imagine, are developing more and more, tools based on abstract spaces are still in their infancy. The aim of this article is to show the possibilities and directions of application of ICT tools based on abstract spaces and to present the current state of their application. The article shows which elements of GIS technology can be used in tools based on abstract spaces and what differentiates the discussed types of spaces. The approach to the proposed tools was made from three points of view. First - the types of abstract spaces were presented. Second - the areas of their application were indicated along with the application of higher information functions (such as spatial data collection for abstract spaces, their single- and multi-area visualisation, environmental analysis and assessment, planning, inspiration and support of decision-making processes presented in abstract spaces). And thirdly, they were framed from the point of view of modern technologies such as AI (Artificial Intelligence), VR (Virtual Reality), AR (Augmented Reality) or hyperlinks. The reflections were concluded with an analysis of the current state of application of the technology in question in Polish public administration offices. The results of the surveys show that the proposed solutions are not currently used in city halls and are at the conceptual stage. The topic of AI is well known among the respondents. This is probably due to the popularity of the concept in popular science publications. Respondents show much less interest in the area of abstract spaces. Furthermore, they are of the opinion that their GIS solutions do not support this type of space, or if they do, they have no idea how to use it. [ABSTRACT FROM AUTHOR]

The article deals with the application of GIS in different types of cities. The development of GIS technology is opening up new applications. In addition to the collection and cartographic visualisation of data, higher-level information functions such as automatic analysis, control, spatial planning or decision support can be realised. GIS technology can also be used to visualise abstract spaces. The article deals with the application of GIS in different types of cities. The article is intended to be the result of an analysis of the direction of the use of GIS technology in public administration in Poland. For this purpose, a model was developed to examine the state of GIS technology application. Two criteria were used to determine the state: groups of factors influencing the use of GIS technology (organisational, functional, informational and technical) and the level of maturity of GIS tools. Four levels were distinguished (from zero, i.e. no application of GIS technology, to a third level indicating the strategic importance of the tools studied). To this end, an empirical study was developed to determine the level of maturity of the application of the tools in question. After analysing the results, it can be concluded that the systems studied are still evolving. From the perspective of the proposed model, it can be assumed that the development of GIS tools has reached the second level in most of the surveyed offices. However, it will still be difficult to develop the software if not all the currently available GIS software capabilities are used. [ABSTRACT FROM AUTHOR]

Sustainable city development is an approach to city planning and management that takes into account the balance between economic, social and environmental aspects in order to ensure sustainable social development, environmental protection and ensuring a high quality of life for residents. The aim of the article is to draw attention to the problems in the field of parking vehicles in a smart city, which should be solved as soon as possible. Problems related to parking in the city are a common challenge that can affect the comfort of living of residents, traffic, natural environment and transport efficiency. Many cities lack parking spaces in relation to the number of vehicles, leading to difficulties in finding a parking space. This can lead to traffic jams that increase the time it takes to find a parking space, increase traffic-related emissions and reduce the quality of life for residents. The research method to solve the problem related to the management of parking spaces is focused on the analysis of the city's problems and guidelines resulting from the sustainable development of the European Union. In the application part, open data resources were used to carry out the simulation in the proprietary application solution. The result of the article, which is a scientific research, is the presentation of the author's application in the Digital Twin concept for the simulation of parking space management. The simulation is the result of research conducted using open data published on the Internet. The conclusion of the research is that in the process of effective management of parking spaces, simulation in the Digital Twin concept can be used. There is great potential in this approach. [ABSTRACT FROM AUTHOR]

Today, slightly more than half of the people live in urban areas and this rate is increasing over the years. The enormously rising population in cities brings with it numerous problems that need to be deeply addressed. New approaches are required in solving many issues such as the management of scarce resources in the city, and the access of city residents to clean air and water. The smart city concept, which freshly emerged with the approach of adapting advanced technological facilities to city administrations, may be able to assist to cope with these problems. The levels of embracement and implementation of the smart city concept play a fundamental role in the well-being of people living in urban areas. Measuring smart city performances, especially in metropolitan cities, can guide the management of local or national investments to improve society's quality of life. In this study, the smart city performance assessment of 30 metropolitan municipalities in Türkiye using a two-stage methodology is included. The scores of smart city index indicators determined by the literature review for Türkiye were calculated using MEthod based on the Removal Effects of Criteria (MEREC), which is a recent multi-criteria decision-making (MCDM) approach. In the second part of the methodology, 30 metropolitan municipalities were ranked based on weighted criteria using another MCDM approach Measurement Alternatives and Ranking according to the COmpromise Solution (MARCOS). The methodology is compared with different MCDM approaches. As a result of the study, it has been revealed that the best city according to the methodology is İstanbul. Also, Antalya, İzmir and Konya have also been among the prominent cities with high smart city scores. The aim of this study is to contribute to the analysis of the issues where the investment should be focused on by measuring the smart city performances of metropolitan cities. [ABSTRACT FROM AUTHOR]

In this paper, we mean by digital health passport a digital passport or immunity passport that stores information about an individual's personal data, health status, travel history, health conditions, and medical and drug records. The proposed innovative scheme can ease travel, ensure free movement, and minimize economic damages. However, protecting personal and digital health passport information during a crisis is challenging. Therefore, this paper aims to propose an agent-based secure decentralized innovative digital health passport protocol that is interoperable, fault-tolerant, and compatible with privacy standards. The proposed solution is known as Secure Mobile Digital Passport Agent (SMDPA), uses: (1) Private Set Intersection (PSI) to (a) protect Digital Health Passport Information (DHPA) against surveillance, especially during the crisis, (b) generate Bit Passing Coin (BPC); (2) blockchain to store serialized SMDPAs copies (3) mobile agent to disseminate DHPA autonomously; (4) Location and time attributes for data minimization. We prototype the proposed solution using the JADE agent framework. The performance measurements show that the result is acceptable. [ABSTRACT FROM AUTHOR]

• A novel performance benchmarking methodology is proposed for large number of bus ACs. • Autoencoder-based similarity measurement method is proposed to identify comparable peer samples. • Statistical analysis-based trend and change detection algorithms are designed to identify anomalous operation. • Experiments are conducted on groups of field operating bus ACs for validation. Bus air conditioners (ACs) are responsible for providing a comfortable cabin environment for passengers. Identifying the bus ACs with degraded performance from a large number of city buses is a critical and challenging task in the development of smart cities. This study developed a data-driven benchmarking methodology to detect anomalous operations with degraded energy performance from a large number of bus ACs. For each target AC to be benchmarked, its similar operation data in other ACs, termed comparable peer samples, are first identified by a Long-Short-Term-Memory (LSTM) autoencoder-based similarity measurement method. The comparable peer samples are then used to develop a LSTM network-based reference model for predicting the power consumption of the target AC. A key energy performance indicator termed power consumption ratio (PCR) is defined for the target AC as the ratio of its measured power to the predicted power. Statistical analysis-based trend and change detection algorithms are designed to identify a trend or change of PCR over a few days for anomalous detection. To validate the benchmarking methodology, two fault experiments were conducted in field-operating bus ACs, and the results show encouraging potentials of the proposed methodology for health monitoring of a large number of ACs serving the city bus fleet. [ABSTRACT FROM AUTHOR]

Urbanisation is leading to a concentration of growing city populations that contribute significantly to economic growth, while becoming epicentres of waste generation, greenhouse gas emissions, and food consumption. Nutrient smart city circular economy is currently an understudied intersection of growing city populations of food consumers, nutrient recovery technologies, Internet of Things (IoT), and agriculture. Meanwhile, machine learning has exploded with popularity over the years, with many circular economy literatures examining its usefulness in its predictive qualities to support management, optimisation, and recovery of useful resources from organic waste. This review paper examines advancements in machine learning for macronutrient recovery in city organic waste systems for a circular economy. The use of ML will greatly improve the scalability, transparency, productivity and accuracy of nutrient: recovery technologies, logistics, dissemination, and reuse. ML can also be combined with hardware to automate tedious waste separation, recovery and agricultural tasks using drones, hydroponics and satellites. Meanwhile, crop yields, nutrient demand-supply efficiencies, food security, environmental soil monitoring, and prosumer involvement could all increase. However, ML applications for urine, anaerobic digestion and prosumer economics are lacking. [Display omitted] • Lack of ML urine, membrane-AD, and prosumer studies for nutrient recovery. • IoT hardware, drones, democratised data sharing to address ML underfitting. • Lack of digital twins; CE BIM; good technologies and economics were barriers. • ML could increase transparency and food security for building consumer trust. • ML IoT to predict nutrients for soil, hydroponics, water treatment and farming. [ABSTRACT FROM AUTHOR]

With the development of social economy and smart technology, the explosive growth of vehicles has caused traffic forecasting to become a daunting challenge, especially for smart cities. Recent methods exploit graph spatial–temporal characteristics, including constructing the shared patterns of traffic data, and modeling the topological space of traffic data. However, existing methods fail to consider the spatial position information and only utilize little spatial neighborhood information. To tackle above limitation, we design a Graph Spatial–Temporal Position Recurrent Network (GSTPRN) architecture for traffic forecasting. We first construct a position graph convolution module based on self-attention and calculate the dependence strengths among the nodes to capture the spatial dependence relationship. Next, we develop approximate personalized propagation that extends the propagation range of spatial dimension information to obtain more spatial neighborhood information. Finally, we systematically integrate the position graph convolution, approximate personalized propagation and adaptive graph learning into a recurrent network (i.e. Gated Recurrent Units). Experimental evaluation on two benchmark traffic datasets demonstrates that GSTPRN is superior to the state-of-art methods. [ABSTRACT FROM AUTHOR]

Road traffic has become prominent in everyday living, impacting or disrupting services to people and daily routines. With the rise in automobile manufacturing and the frequency of vehicle crashes, human catastrophes, like fatalities, accidents, impairments, and destruction of property, are surging yearly. Vehicle collision detection has recently gained prominence in decreasing manually operated and autonomous vehicle fatalities. The concept of independent and self-driving cars relies on accurate object recognition, including pedestrians, vehicles, buildings, and other moving objects. Various object-detecting approaches have been proposed to help autonomous vehicles (AVs) achieve consistent, safe driving. Object prediction and detection have noticed numerous algorithmic changes that have improved speed and accuracy. In this study, I used a traffic dataset produced by a CARLA simulator to anticipate collisions using the Yolov7 model. I generated the dataset from a CARLA simulation bench in video sequences, manually annotated the frames, and used the deep learning algorithm Yolov7 to train them. The model predicts the collision a few seconds before it occurs in real-time. I implemented this framework to increase the safety of driving in self-driving vehicles. [ABSTRACT FROM AUTHOR]

Climate change and its consequences are currently a very pressing topic for cities. Digital solutions can help in this regard, for example, to inform citizens and provide digital services aimed at fostering climate-friendly behavior. In the EnStadt:Pfaff research project, we are developing a climate-friendly smart city district. Part of this is digitalization, with the main goal being the implementation of a smart city district digital ecosystem providing digital solutions to the people of the district, which is currently still under construction. So far, we have already developed some digital solutions with different technology readiness levels. One major question we have faced, however, was how to inform the public about these solutions. We decided to do this in various ways, such as a classical project webpage, events, and videos. In order to have one main entry point for the public to explore our solutions, we have developed a so-called ecosystem map. Four of the digital solutions are presented there to show what kind of digital solutions might be part of the future smart city district digital ecosystem. In this paper, we report on how we created the digital map, provide the four digital solution examples shown on the map, and present initial feedback we received regarding our solutions. [ABSTRACT FROM AUTHOR]

Traffic flow prediction (TFP) has attracted increasing attention with the development of smart city. In the past few years, neural network-based methods have shown impressive performance for TFP. However, most of previous studies fail to explicitly and effectively model the relationship between inflows and outflows. Consequently, these methods are usually uninterpretable and inaccurate. In this paper, we propose an interpretable local flow attention (LFA) mechanism for TFP, which yields three advantages. (1) LFA is flow-aware. Different from existing works, which blend inflows and outflows in the channel dimension, we explicitly exploit the correlations between flows with a novel attention mechanism. (2) LFA is interpretable. It is formulated by the truisms of traffic flow, and the learned attention weights can well explain the flow correlations. (3) LFA is efficient. Instead of using global spatial attention as in previous studies, LFA leverages the local mode. The attention query is only performed on the local related regions. This not only reduces computational cost but also avoids false attention. Based on LFA, we further develop a novel spatiotemporal cell, named LFA-ConvLSTM (LFA-based convolutional long short-term memory), to capture the complex dynamics in traffic data. Specifically, LFA-ConvLSTM consists of three parts. (1) A ConvLSTM module is utilized to learn flow-specific features. (2) An LFA module accounts for modeling the correlations between flows. (3) A feature aggregation module fuses the above two to obtain a comprehensive feature. Extensive experiments on two real-world datasets show that our method achieves a better prediction performance. We improve the RMSE metric by 3.2%–4.6%, and the MAPE metric by 6.2%–6.7%. Our LFA-ConvLSTM is also almost 32% faster than global self-attention ConvLSTM in terms of prediction time. Furthermore, we also present some visual results to analyze the learned flow correlations. [ABSTRACT FROM AUTHOR]