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Population growth in urban cities has created transport problems such as traffic congestion, pollution, accidents, and infrastructure failures. These problems often lead to disruption of mobility, especially in emerging economies. The goal of any city is to have a resilient transport system that is free from frequent and severe disruptions. Resilience can be incorporated into smart mobility systems to create a robust, integrated and intelligent transport system. Therefore, this study sought to determine resilience factors required for a resilient smart mobility system in emerging economies. The study used interpretive structural modelling (ISM) to establish the causal interrelationships among the factors drawing from data from experts in smart cities, city transport and academia. It was found that the most critical factor to drive resilience is foresight – the ability to anticipate disruptions. Three other resilience factor categories identified were dynamism, certainty and optimisation, which all point to the importance of building dynamic capabilities. The key implication for practice highlights the role that human and artificial intelligence can play in anticipating natural, social and infrastructure disruptions. ISM offered a mechanism to determine the critical causal resilience factors to be prioritised amongst the competing demands on the limited resources of emerging economies. Apart from foresight, which was found to be a driving factor, the other resilience factors are equally strong linkage factors (influence each other), thus further revealing the complexities that decision-makers in urban cities of emerging economies constantly face. A research agenda for resilience in smart mobility in emerging economies was developed. [ABSTRACT FROM AUTHOR]

This article presents an approach for predicting Lockout/Tagout (LOTO) procedure sheets, which are commonly used in the manufacturing industry to prevent premature equipment restart during maintenance. The prediction problem of energetic devices to lock from machine names is regarded as a multi-task classification problem. The dataset was obtained by processing LOTO sheets in Portable Document Format (PDF). The K-Nearest Neighbours (KNN), Random Forest (RF), and Deep Neural Network (DNN) algorithms were compared for this problem. The best prediction performance was achieved with the DNN method, with top-1 accuracies exceeding 63% and top-2 accuracies exceeding 90% for all devices. The sensitivity analysis conducted on the results indicates that the approach is robust and reliable, regardless of the industrial sector considered. In other words, the approach is not significantly affected by variations in the industry or its specific characteristics. These results suggest that the proposed approach can be used to assist workers in drafting LOTO sheets, and offers strong potential for concrete applications in safety management in the era of smart manufacturing. [ABSTRACT FROM AUTHOR]

Sidewalk Autonomous Delivery Robots (SADRs) present a promising alternative for mitigating excessive delivery traffic in smart cities. These bots operate at pedestrian speed and work in conjunction with vans to offer efficient delivery services. Existing research emphasises the development of coordinated service schedules for vans and bots to optimise customer service. In contrast, this study examines the influence of bot return policies on their travel back to designated stations after task completion. We assess three distinct return policies that dictate the station selection for bot returns and explore the relocation of bots between stations using vans. Specifically, we present a reformulation of the fleet sizing problem as a minimum cost matching problem in a bipartite graph. This reformulation allows for the efficient calculation of optimal solutions for bot fleet sizing under different return policies within polynomial time. Notably, this computational efficiency enables the analysis of large-scale cases without sacrificing the evaluation of policies with heuristic gaps. Our findings highlight the importance of carefully selecting the appropriate return policy, as the best policies have the potential to decrease the bot fleet size by up to 70%. [ABSTRACT FROM AUTHOR]

Product service system (PSS) is an effective approach to achieve win–win situation between service providers and consumers, and the design of PSS is the first step of its application. However, on the one hand, PSS is relatively an abstract concept, and what exactly should be considered when designing a PSS still require further exploration; on the other hand, the fast development of information and web technologies bring both opportunities and challenges for PSS design. For example, how to efficiently design the smart & connected products that support remote monitoring and control of service operation, how to build the dynamic service activity flow model that can be intuitively and visually read by human engineers and also can be conveniently deployed on computers, and how to realise edge–cloud collaboration between service providers and consumers. In this regard, a service requirement-oriented four-step generic PSS design method is established, including service mode selection and structured service order generation → smart & connected service product configuration → dynamic event-state knowledge graph-based service activity flow and service resource network configuration → industrial internet-based edge–cloud collaborative service delivering. Finally, a real design case of carbon block grinding and polishing PSS is used for verification. ABBREVIATIONS: AS: assembly service; C: controlling targets or objectives; I: input; M: enabling methods or mechanisms; MRO: maintenance, repair, and operation; MS: maintenance service; O: output; OEE: overall equipment effectiveness; PSS: product service system; PV: processing volume; RxEy-Sz: the relation from Event y to the State z of Service activity x; RxSz-Ey: the relation from the State z of Service activity x to Event y; StateAiCurrent: the current state of Service activity i; StateAiFuturek: the kth possible future state of Service activity i; StateAiHistoryj: the jth historical state of service activity i; StateEy: the state of Event y; Tq: the qth time point in the time line. [ABSTRACT FROM AUTHOR]

The article discusses the changes made by the Academy of Marketing to better meet the needs of the academic community and shape the direction of the Academy. The annual conference introduced capacity building research workshops in 2018, which became even more important when the 2020 conference was canceled due to the COVID-19 pandemic. The article highlights 18 workshops that focused on reframing marketing priorities and explored various topics such as consumer research during the pandemic, digital assessments, sustainability in marketing, and virtual engagement. The article also mentions six papers in a special issue that examine the role of technology in marketing practices, teaching, and future imagining. The papers cover topics such as assessment strategies for work-ready graduates, online consumption of the arts, virtual community building, online religious engagement, entrepreneurial decision-making, and stakeholder co-creation of smart cities. The article concludes by encouraging readers to embrace the opportunities to advance marketing priorities through technology. [Extracted from the article]

Today, smart city areas and initiatives are found throughout the world, yet little research has been completed into the processes whereby decisions concerning the initiation, marketing, and branding of smart city projects have been taken. The present study examined these processes via interviews with 18 smart city managers, followed by an online World Café which was convened to discuss emerging issues. Interviewees were asked to relate stories of how decisions had been reached, which stakeholders had been prioritised, the extent of citizen co-creation in project initiation and branding, and the main difficulties involved. An argumentative narrative discourse methodology was employed to analyse the interview transcripts, which revealed a number of disparities between the suggestions of extant place branding literature and current practice where smart city projects were concerned. [ABSTRACT FROM AUTHOR]

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

In the last few years, the rapid advancement of the Internet of Things (IoT) and the widespread adoption of smart cities have posed new challenges to computing services. Traditional cloud computing models fail to fulfil the rapid response requirement of latency‐sensitive applications, while mobile edge computing (MEC) improves service efficiency and customer experience by transferring computing tasks to servers located at the network edge. However, designing an effective computing offloading strategy in complex scenarios involving multiple computing tasks, nodes, and services remains a pressing issue. In this paper, a computing offloading approach based on Deep Reinforcement Learning (DRL) is proposed for large‐scale heterogeneous computing tasks. First, Markov Decision Processes (MDPs) is used to formulate computing offloading decision and resource allocation problems in large‐scale heterogeneous MEC systems. Subsequently, a comprehensive framework comprising the "end‐edge‐cloud" along with the corresponding time‐overhead and resource allocation models is constructed. Finally, through extensive experiments on real datasets, the proposed approach is demonstrated to outperform existing methods in enhancing service response speed, reducing latency, balancing server loads, and saving energy. [ABSTRACT FROM AUTHOR]

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

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

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

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

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]

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

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

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

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

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

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]

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]

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

This 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]

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

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

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

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

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

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

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

The 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 spatially concentrated architecture of the cloud environment causes excessive latency and network congestion in traditional smart healthcare systems designed for smart cities. Fog computing underpins IoT-enabled smart city solutions for latency sensitivity by putting computing power closer to the network boundary. However, resource management issues degrade service quality and accelerate energy depletion in real-time smart healthcare systems, as the fog node workload has increased exponentially. This paper offers a fog-driven patient-centric smart healthcare system for an e-healthcare environment to maintain Quality of Service (QoS) during severe traffic load on a fog platform. The multi-objective EQLS (Energyefficient QoS-aware Load balancing Strategy), is proposed to stabilize workload among processing nodes to increase real-time sensitivity of critical tasks within optimal response time and energy usage. Using the iFogSim simulator to present the significance of research work, the proposed technique is compared to existing load-balancing policies (Round Robin (RR) and Fog Node Placement Algorithm (FNPA)) regarding energy usage, response time, and cost. The simulation results reveal that EQLS saves 8.7% and 14.9% more energy and 6.2% and 13.4% greater response time over FNPA and RR, respectively. The results signify that the proposed approach can efficiently support real-time applications of smart cities. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has expanded rapidly, allowing for a network of sensors and gadgets to collect and share information to make people's lives easier and more convenient. As the Internet of Things (IoT) grows, however, energy efficiency becomes a major issue, especially for portable and wireless gadgets. Low-power, long-range communication capabilities are needed, and Long-Range Wide Area Network (LoRaWAN) has emerged as a viable solution to meet this need. This study provides an in-depth analysis of the LoRaWAN-based, low-power Internet of Things. The suggested network architecture is optimized for low power consumption and high connectivity for numerous Internet of Things (IoT) use cases. This low-power Internet of Things network relies on LoRaWAN gateways, end devices, and a server to function. LoRaWAN is a technology that enables the low-power, long-range transmission of data packets. The results show that the optimized case and nonoptimized case have a delivery ratio of 0.85 to 0.73 from node 100 to 500. LoRaWAN significantly reduces energy usage compared to conventional IoT connectivity alternatives, making it a fantastic option for battery-powered devices in far-flung or limited-resource locations. Finally, the adoption of LoRaWAN provides a viable solution to address the energy efficiency concerns in IoT networks, hence allowing for sustainable, long-lasting IoT installations and enabling a wide variety of new applications within the IoT ecosystem. Furthermore, addresses the potential applications of this technology in the future, including upgrades and integration with other technologies like edge computing and 5G networks. [ABSTRACT FROM AUTHOR]

Smart city construction is an essential practice in the effort to promote the digital transformation of society as a whole. Taking China's 'National Smart City' pilot project as a quasi‐natural experiment, this paper studies the income distribution effect of smart city construction by building a differences‐in‐differences model and using the data of China's prefecture‐level cities from 2008 to 2018. The results show that implementing China's 'National Smart City' pilot project has significantly reduced regional income inequality. However, this effect has heterogeneity. In regions with a higher level of economic development, human capital accumulation and R&D investment, the impact of smart city construction on reducing income inequality is stronger. In areas with a higher level of human capital accumulation, smart city construction can reduce income inequality. However, this effect is insignificant in areas with a lower level of human capital accumulation. Mechanisms tests show that smart city construction can reduce regional income inequality by creating more jobs, narrowing the digital divide and improving public service delivery. As stated above, this paper implies that accelerating the construction of smart cities and improving the digital and intelligence levels of the wider society is of great significance to reducing income inequality and building a more reasonable income distribution structure. [ABSTRACT FROM AUTHOR]

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

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