Smart City Technologies Research Articles

Spatial organization of post-industrial society

The article examines the features of the current stage of urbanization, in particular, the increasing complexity of spatial organization associated with cities overcoming their administrative boundaries, which led to the emergence of agglomerations and urban regions. New urban forms are considered as a result of the formation of a post-industrial society, where the labor market demonstrates greater flexibility than in the industrial one. In addition, the modern transport system makes it possible to easily cover significant distances in a short time and thereby changes the idea of space. Digital technologies have made it possible to transfer a number of interactions (both personal and business) to a remote format, which also contributed to the expansion of urban entities and complicated their management. The author also points out the ambiguous nature of the social consequences of digitalization and algorithmization of urban space. It is noted that smart city technologies, which are positioned as an effective opportunity to counteract uncertainty and manage it, create new vulnerabilities and threats. Special attention is paid to new challenges that modern cities face due to climate change. Thus, the author demonstrates a wide range of social problems relevant to modern urban entities that are becoming in creasingly complex. The article emphasizes that today urban studies is an interdisciplinary field for which sociology is of great importance, allowing us to get an answer to the question of how to improve the quality of life of the population of all spatial forms existing today.

Collaboration, Organizational Capacity, and Sustainability: The Cross-Sector Governance of Smart Cities

Interactions with other organizations likely influence how local governments confront complex problems. Drawing on theories of collaborative governance, we investigate the extent to which collaboration affects U.S. local governments’ prioritization of the sustainability benefits of Smart Cities Technologies (SCT). Using data from the International City/County Management Association (ICMA) 2016 Smart Cities Survey and the U.S. Census Bureau, we find that collaboration with local, regional, and private sector partners influences the extent to which local governments prioritize the sustainability benefits of SCT. Organizational capacity is also an important predictor of perceived benefits of SCT. Our findings suggest a hidden side of boundary spanning, suggesting that collaboration can enhance opportunities for sustainability technologies.

Is It Just Dumb Luck? The Challenge of Getting Access to Public Records Related to Smart City Technology

Many state and local governments have begun to rely heavily on Smart City technologies, including license plate readers, gun-shot detection equipment, wastewater monitoring tools, and more. But the public often has little oversight with regard to their purchase, installation, and use. We analyze public records laws and relevant case law to examine whether the laws provide access to records about, and produced by, Smart City technologies. Even in states that broadly define “public record,” officials regularly rely on exemptions—often citing law enforcement or privacy concerns—to deny access. Further, many Smart City technologies have not been addressed by the legislatures and courts, but legislation and litigation involving two technologies (police body-worn cameras and police dash cameras) have advanced—offering some optimism.

Assessment of Cybersecurity Risks in Digital Twin Deployments in Smart Cities

Digital Twin (DT) technology has become a cornerstone of modern smart city infrastructure, providing real-time insights and operational efficiencies by creating a virtual replica of physical systems such as traffic networks, energy grids, and public services. While these advancements enable optimized urban management and improved decision-making, they also present new cybersecurity challenges that can potentially jeopardize the safety and reliability of critical infrastructures. This study addresses the cybersecurity risks associated with Digital Twin deployments in smart cities, focusing on threats such as unauthorized access, data manipulation, and hijacking of the DT models, which could result in service disruptions and compromise public safety. The research employs a comprehensive risk assessment methodology based on the NIST Cybersecurity Framework, where potential risks are identified, evaluated, and prioritized according to their severity and likelihood of occurrence. To mitigate these risks, a multi-layered security framework was developed, incorporating encryption mechanisms, robust access control, and an Artificial Immune System (AIS)-based anomaly detection model. The framework was tested through a simulated case study on a smart transportation system within a smart city environment, demonstrating its effectiveness in preventing data tampering and detecting unauthorized access. The results indicate that the proposed security model reduced data manipulation incidents by 35%, decreased response times for threat detection by 25%, and improved overall system resilience by 40%. These findings underscore the critical need for proactive cybersecurity strategies in ensuring the secure and resilient deployment of Digital Twin technologies in smart cities. The study concludes by emphasizing the importance of continuous security monitoring and adaptive threat management to safeguard smart city ecosystems from evolving cyber threats

Advanced concept for noise monitoring in smart cities through wireless sensor units with AI classification technologies

The rapid urbanization in the cities has indicated the importance of sustainable development for creating smart cities with high quality of life. Environmental noise as one of the main concerns has to be addressed according to International Directives and Legislation. Traditional noise estimation methods are costly and time-consuming, but the emerging technologies like low-cost wireless sensor units (WSUs) offer more granular data collection and analysis. Requirements for more accurate assessment of the noise pollution by detecting the dominant noise sources impose the need to apply novel approaches in the technical systems for noise monitoring. To this aim, this paper investigates the advanced methods for noise monitoring through WSUs with AI classification technologies. The proposed concept of the device not only quantitively describes the noise pollution, but also tends to recognize the class of the disturbing sound events. The accuracy of the WSUs are configured by comparing the results with professional hand-held analyzer, showing powerful and affordable low-cost units able to publish results through cloud and fog computing based on the IoT and smart city technologies. The paper discusses the challenges for merging noise measurement technology and the AI algorithms to classify and quantify the different classes of sound events.

Usage of Technologies in Smart Cities

Usage of Technologies in Smart Cities

Fuzzy based Decision-Making Algorithm for Solving Big Data Issues in Smart Cities

To better provide urban services and build an increasingly sustainable architecture, big data can be used to make more efficient use of current assets while enhancing the caliber of services offered to local inhabitants. However, there are several challenges to incorporating big data into existing infrastructure. Therefore, this research aims to determine the problems associated with Big Data’s effectiveness in developing intelligent towns and to investigate the connections between those difficulties. The 14 issues with Big Data were found through a literature study, and the precision was checked by feedback from professionals. Next, we employ a combined approach based on fuzzy interpretation, Structured simulation, and the Fuzzy Making Decisions Trial and Assessment Laboratories to decipher the connections between our identified problems incorporating Big Data into the development of smart cities is hampered, as shown by the analysis of links between challenges, primarily by the heterogeneous inhabitants in developed cities and the lack of connectivity. The findings of this study will provide creative city practitioners and policy planners with the information they need to successfully tackle these obstacles, clearing the way for the widespread adoption of smart city technologies. This research is a first step towards creating an interpretive structural model of the difficulties brought on by Big Data in cutting-edge urban planning. The study attempts, in part, to use this paradigm to better understand the relationship among the highlighted issues.

Integrating Smart City Technologies for Enhanced Urban Sustainability

The concept of smart cities has emerged as a transformative approach to addressing urban challenges while promoting sustainability and enhancing quality of life. This paper explores the integration of smart city technologies as a strategic framework for achieving urban sustainability goals. Key components include the deployment of Internet of Things (IoT) devices, data analytics, and digital infrastructure to optimize resource management, improve efficiency in public services, and mitigate environmental impact. By analyzing case studies and current trends, this study underscores the potential of smart city technologies to foster innovation, resilience, and inclusive growth in urban environments. Ultimately, it advocates for a holistic approach where technological integration is leveraged to create sustainable, livable cities for present and future generations.

The impact of smart city construction on resident well-being: the mediating role of technology acceptance

This study investigates the relationships between smart city construction, technology acceptance, and resident well-being, integrating the Technology Acceptance Model with smart city research. Using structural equation modeling and data from 2,187 residents across five smart cities, we examine how smart city initiatives influence technology acceptance and, consequently, resident well-being. Results indicate that smart city construction positively affects perceived usefulness and ease of use of smart technologies, which in turn drive technology acceptance. Technology acceptance significantly mediates the relationship between smart city initiatives and resident well-being, highlighting its crucial role in translating urban innovations into quality of life improvements. The indirect effect of smart city construction on resident well-being through technology acceptance was significant (β = 0.183, p < 0.001), accounting for 37.1% of the total effect. Additionally, a direct positive effect of smart city construction on well-being was observed (β = 0.31, p < 0.001), suggesting benefits beyond active technology engagement. The study contributes to smart city literature by providing empirical evidence for the often-assumed link between smart city development and resident well-being, while also extending the application of the Technology Acceptance Model to urban contexts. These findings have important implications for urban planners and policymakers, emphasizing the need for user-centered design and inclusive development strategies in smart city projects to maximize positive impacts on urban populations.

Prediction model establishment for residential community occupancy considering urban environment

The accurate prediction of occupancy rates within the residential sector is pivotal for urban and energy planning and management. This study aims at addressing the often-neglected influence of regional variations within urban settings on occupancy rates by employing multi-source urban data and explainable machine learning algorithms. Utilizing mobile signaling data from January and July 2022 in Guangzhou, we developed prediction models using the CatBoost algorithm with hyperparameters finely tuned via Bayesian optimization. The SHapley Additive exPlanation was applied to elucidate the influence of different urban configurations. Our findings reveal significant seasonal variations and distinct differences between weekdays and weekends, showing higher occupancy rates in winter than in summer and elevated rates during weekends. Additionally, significant fluctuations were observed among different age groups on weekdays. The prediction models exhibited robust capabilities, achieving R2 values exceeding 0.95 for weekdays and 0.91 for weekends. Key findings highlight that the density of points of interest related to shopping and food, along with variability in building heights, significantly impact occupancy rates. The influence of diverse urban configurations varies across different hours of the day. By the comprehensive analysis of regional variations and the application of advanced machine learning techniques to predict and explain residential occupancy rates, this research offers tools to optimize the management and sustainability of built environments, enhances the understanding of urban operation patterns and contributes to advancing smart city technologies, providing valuable insights for urban and energy planning.

Real Estate Economy in the Post-epidemic Era

The COVID-19 outbreak has had a significant impact on China's real estate market, with the sector's recovery lagging behind the overall economy. This study evaluates the pandemic's influence on consumer behavior, housing demand, and preferences, highlighting substantial declines in real estate development investment and commercial housing sales. Key factors contributing to reduced demand include a shift towards remote working models, heightened health and safety concerns, and evolving perceptions of urban living. To stimulate the market, government incentives such as tax breaks and housing subsidies are recommended. Additionally, developers are encouraged to embrace smart city technologies for optimizing energy consumption and providing convenient community services. Emphasizing health-conscious design is crucial for improving air quality and increasing green space while investors should diversify their portfolios to mitigate market volatility risks and enhance resilience. The objective of this study is to devise an appropriate real estate economic recovery plan that aligns with the changing urban to sustain economic growth.

Innovative Application of Intelligent Internet of Things in Smart Cities

The Internet of Things, as an important component of the new generation of information technology system, regards the Internet as the main carrier of information, enabling all ordinary physical objects that can be independently addressed to achieve interconnectivity. This is the key to promoting urban modernization and building smart cities. This paper analyzes and studies the application of Internet of Things technology in smart cities. Based on the understanding of relevant concepts, it aims to understand the relationship between IoT technologies and smart city construction, clarify the role of Internet of Things in smart city construction, and the challenges currently faced by IoT technologies in smart city application. Therefore, taking the smart community as the starting point, the application of the smart Internet of Things in the smart community is completed through eight steps: demand analysis, planning and design, sensor and device deployment, data collection, data storage, data analysis, control and scheduling, and user application development. The experiment results show that after establishing a smart community control platform based on smart IoT technologies, the failure rate of the comprehensive information sending end, receiving end, output system, and hardware system is low, the user experience satisfaction is high, and the platform performance is strong.

AI-Driven Urban Planning: Enhancing Efficiency and Sustainability in Smart Cities

Urban planning in smart cities is increasingly leveraging artificial intelligence (AI) to enhance efficiency and sustainability. This article explores the integration of AI-driven technologies to optimize various aspects of urban development and management. Smart cities are characterized by their use of advanced technologies to improve quality of life, resource management, and infrastructure efficiency. Traditional urban planning methods often face challenges in adapting to rapid urbanization and dynamic environmental changes. AI presents opportunities to address these challenges by providing data-driven insights and predictive capabilities. This research employs a case study approach, analyzing the implementation of AI in urban planning processes across several smart cities globally. Key methodologies include data analytics, machine learning algorithms, and predictive modeling techniques applied to diverse urban datasets. The study evaluates how AI-driven decision support systems aid in infrastructure planning, traffic management, energy consumption optimization, and environmental sustainability. The findings demonstrate that AI-enabled urban planning significantly enhances efficiency and sustainability in smart cities. AI algorithms optimize traffic flow, reduce energy consumption through predictive maintenance of infrastructure, and facilitate adaptive urban design based on real-time data analytics. Moreover, AI-driven approaches improve decision-making processes by providing stakeholders with actionable insights for informed policy formulation and resource allocation. This article contributes to the evolving field of smart city technologies by showcasing the transformative potential of AI in urban planning. By harnessing AI capabilities, cities can effectively address complex urban challenges and pave the way for more resilient and sustainable urban environments.

Decomposition and reconstruction algorithms for IoT reliability analysis utilizing 5G technology for smart cities

Internet of Things (IoT) and 5G communication technologies in smart cities deliver promising services for heterogeneous applications. The application reliability banks on uninterrupted and seamless services experienced by the users. However, the increasing smart city application demands influence the experience reliability through augmented wait times. This article therefore introduces a Coherent Reliability Service Broadcasting Technique (CRSBT) for sustaining constructive application services. This technique incorporates linear regressive and digressive learning for application service improvements and restrictions. Based on the demand, the regressive process verifies the wait time and with the reducing demands, the service broadcast ratio is verified. These two factors are verified post the demand and response through 5G resource allocations and IoT computations. Both the service-oriented features are validated for regressive service broadcast and either of the one is used for digressive response. The coherence between the computations (IoT) and resources (5G) is verified on-demand and linearly. Therefore, the proposed technique is reliable in sustaining service broadcast, less wait time, and maximum flexibility.

Data Analytics for Predicting Situational Developments in Smart Cities: Assessing User Perceptions.

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.

Role of big data and technological advancements in monitoring and development of smart cities

Driven by rapid advancements in technology and data science, a revolutionary transformation is sweeping across environmentally friendly cities worldwide. This surge stems from a pressing need to tackle the intricate complexities of urban sustainability, encompassing everything from infrastructure and governance to fragmented design and technological solutions. To effectively manage these complexities and accurately measure, assess, and optimize their sustainability performance, sustainable communities are increasingly tapping into the potential of smart city technologies, particularly big data and its fictionalized applications. This trend culminates in the emergence of smart cities. This article delves into the current state of research surrounding data-driven, environmentally conscious smart cities, aiming to assess the extent to which these two concepts are currently being integrated and identify potential gaps in this field. Through a strong emphasis on evidence-based research, the study underscores the potential of big data technologies to offer innovative approaches for monitoring, comprehending, evaluating, and ultimately managing sustainable urban development. It further highlights the crucial role of data-driven advancements in formulating strategic development policies and operational management procedures, ensuring that environmentally conscious cities can continue to contribute to sustainability goals even amidst rapid urbanization.

Improved YOLOX-based detection of condition of road manhole covers

Manhole cover damage poses significant threats to road safety and infrastructure integrity, necessitating timely detection and repair. To address this, we introduce an enhanced YOLOX model integrated with ECA (High Efficiency Channel Attention) modules for real-time monitoring using car recorder footage. Our method categorizes manhole cover conditions into three distinct states: normal, broken, and down. By in-corporating ECA-Net before the decoupling head of the YOLOX model, we significantly boost its channel feature extraction abilities, critical for distinguishing subtle changes in cover conditions. Experimental results reveal a substantial increase in mean Average Precision (mAP) to 93.91%, with a notable AP of 92.2% achieved in the detection of the ‘down’ state, historically the most challenging category. Despite the en-hancements, our model maintains a high detection speed, processing at an average rate only five images per second slower than the original YOLOX model. Comparative analyses against leading detection models, in-cluding Faster R-CNN, SSD, and CenterNet, underscore the superiority of our approach in terms of both accuracy and speed, particularly in accurately recognizing the ‘down’ condition of manhole covers. This in-novative model provides a reliable tool for swiftly identifying damaged manhole covers and their precise lo-cations, enabling prompt maintenance actions. By improving the monitoring efficiency of urban infrastruc-ture, our solution contributes to enhanced road safety and the advancement of smart city technologies.

Secure and transparent traffic congestion control system for smart city using a federated learning approach

This study addresses the increasing problems of traffic congestion in smart cities by introducing a Secure and Transparent Traffic Congestion Control System using federated learning. Traffic congestion control systems face key issues such as data privacy, security vulnerabilities, and the necessity for joint decision-making. Federated learning, a type of distributed machine learning, is effective because it allows for training models on decentralized data while maintaining data privacy. Furthermore, incorporating blockchain technology improves the system’s security, integrity, and transparency. The proposed system uses federated learning to securely gather and analyze local traffic data from different sources within a smart city without moving sensitive data away from its original location. This method minimizes the risk of data breaches and privacy issues. Blockchain technology creates a permanent, transparent record for monitoring and confirming decisions related to traffic congestion control, thereby promoting trust and accountability. The combination of federated learning's decentralized nature and blockchain's secure, transparent features aids in building a strong traffic management system for smart cities. This research contributes to advancements in smart city technology, potentially improving traffic management and urban living standards. Moreover, tests of the new combined model show a high accuracy rate of 97.78% and a low miss rate of 2.22%, surpassing previous methods. The demonstrated efficiency and adaptability of the model to various smart city environments and its scalability in expanding urban areas are crucial for validating its practical use in real-world settings.

Challenges to Socio-Economic Balance in Istanbul’s Rapid Urbanization

Istanbul, a city of significant historical and geographical importance, has experienced rapid urbanization over the past few decades. This paper explores the socio-economic challenges that have arisen from this urban expansion, focusing on housing shortages, infrastructure strain, economic disparities, social cohesion issues, and environmental degradation. Using a mixed-methods approach, the study integrates qualitative insights from in-depth interviews and field observations with quantitative data from structured surveys and secondary sources. The findings reveal that while urban renewal projects and infrastructural investments have modernized parts of the city and attracted investment, they have also led to the displacement of long-term residents, exacerbated economic inequalities, and stressed existing infrastructure. Comparative analysis with other rapidly urbanizing cities like Mumbai, São Paulo, and Shanghai provides valuable lessons for addressing these challenges. The paper concludes with policy recommendations aimed at promoting inclusive and sustainable urban development in Istanbul, emphasizing the need for equitable housing policies, strategic infrastructural investments, community engagement, and the adoption of smart city technologies.

Navigating urban sustainability: balancing livability, smartness, services and environment through AHP analysis

PurposeSustainable urban development involves integrating intelligent technology into urban planning to promote economic growth, social services, and environmental preservation. It fosters inclusiveness, safety, resilience, and sustainability in urban environments. However, this strategy faces challenges balancing economic growth, social equality, and environmental preservation. Design experts must balance economic development with sustainability preservation to achieve sustainable urban development. They depend on technology for smarter communities. The paper aims to identify the criteria for sustainable urban design and design professionals' bias and their awareness of using the fourth industrial revolution technologies.Design/methodology/approachThe research methodology involves a document review to identify criteria and weights for sustainable urban design. A questionnaire was conducted with 30 design professionals to understand their preferences and biases. A hierarchical framework was created, and pairwise comparisons were computed using an AHP Online System. The weight of each criterion was calculated by aggregating normalized pairwise values within each row and dividing by the total number of cells. The Consistency Ratio (CR) was determined by aggregating the values in the row to produce the weighted aggregate value for each principle.FindingsThe document review emphasizes the significance of sustainable urban design, livability, intelligence, and environmental conservation in sustainable communities. However, participants often overlook these aspects, focusing on comfort, privacy, security, and exclusiveness. They also ignore the need for smart city technologies and environmental protection. This lack of understanding may hinder Egypt’s achievement of SDG11.Research limitations/implicationsThe paper’s main limitation is the small sample size and composition, as all participants were Egyptians working in Egypt or the Arabian Gulf region. The findings cannot be generalized, and future research could involve a team from different regions. Egypt needs to address the lack of knowledge and awareness of sustainable urban design principles among design professionals to achieve SDG 11 goals. This can be achieved through educational initiatives, readjusting priorities towards sustainability, harmonizing policy and practice with sustainable urban design principles, and interdisciplinary teamwork. Design professionals should customize their methods to suit local requirements and ecological circumstances.Originality/valueThe paper reveals that many design professionals lack an integrated and holistic understanding of sustainable urban design principles that the document review outlined. Their responses and practices reveal an incomplete understanding of sustainable urban development, requiring attention to organizing the profession, training practitioners, and reforming planning education. The paper shows that AHP has the potential to serve as a suitable instrument for decision-making; nonetheless, it is essential to exercise caution when interpreting its outcomes.