Building Smart Cities Research Articles

TOE Configuration Analysis of Smart City Construction in China Under the Concept of Sustainable Development

The construction of smart cities is an effective path for sustainable urban development. Determining how to build smart cities and choose effective development models has attracted attention from all walks of life. Consequently, determining how to promote the development of smart cities and how to choose effective development models has also attracted attention. This paper takes 77 prefecture-level cities in China as case studies to explore the impact of technology, organization, and environment on the high-level development of smart cities. In this study, the fsQCA (fuzzy set-based qualitative comparative analysis) method was used to explore the configuration effects of three types of factors. The main findings are as follows: Firstly, no single factor among the three categories is a necessary condition for the high development level of smart cities, indicating that relying solely on technological conditions cannot build high-level smart cities. Secondly, the path of development of a smart city can be summarized into three modes: Both organizational and industrial environment type, both governance and cultural environment type, and balanced development type. Finally, among the three modes, the government’s digital governance capability and industrial environment play a crucial role in achieving a high level of development in smart cities. This article examines the development of smart cities from a systems perspective and provides useful, practical insights for the construction of smart cities.

Smart Visegrad - How modern and dynamic are the local governments of the V4 regions

The megatrend of building Smart Cities in the context of sustainable development of local governments is highly topical. The movement and intensity of Smart City solutions are proportional to the need to eliminate serious global and regional problems. The most pressing of these relates to socio-economic impact. Therefore, the most attractive areas of interest include infrastructure, environment, management, and governance. The geopolitical context and the vision of a sophisticated, efficient, high-quality, and beneficial municipality leads to the production of economic, social, and environmental benefits also in postcommunist European countries. The effects of synergies of high technology and reflection on the virtuous behaviour of people and society are prerequisites for the desired socioeconomic development and sustainability of countries. Promoting the paradigm of multisubject and multi-sectoral involvement of actors contributes to the performance of regions and enterprises at the same time. The primary impetus for writing the article was the ambition to identify the significant attributes of the Smart City concept and to assess the propensity of V4 countries to address this issue. Within the framework of the study, the analytical methods used were the investigation of the current state of the art, the search of available relevant literature and case studies, the summarisation of results on the basis of induction, deduction, mathematical-statistical calculations and other methods allowing us to draw correct conclusions. The most significant of these conclusions is that there are no significant differences in the propensity of Visegrad Four (V4) countries to be smart. On the contrary, the interest in improving and developing the country, the unit, and the society can be considered a global phenomenon.

A Survey of Machine Learning for Urban Decision Making: Applications in Planning, Transportation, and Healthcare

Developing smart cities is vital for ensuring sustainable development and improving human well-being. One critical aspect of building smart cities is designing intelligent methods to address various decision-making problems that arise in urban areas. As machine learning techniques continue to advance rapidly, a growing body of research has been focused on utilizing these methods to achieve intelligent urban decision making. In this survey, we conduct a systematic literature review on the application of machine learning methods in urban decision making, with a focus on planning, transportation, and healthcare. First, we provide a taxonomy based on typical applications of machine learning methods for urban decision making. We then present background knowledge on these tasks and the machine learning techniques that have been adopted to solve them. Next, we examine the challenges and advantages of applying machine learning in urban decision making, including issues related to urban complexity, urban heterogeneity and computational cost. Afterward and primarily, we elaborate on the existing machine learning methods that aim to solve urban decision making tasks in planning, transportation, and healthcare, highlighting their strengths and limitations. Finally, we discuss open problems and the future directions of applying machine learning to enable intelligent urban decision making, such as developing foundation models and combining reinforcement learning algorithms with human feedback. We hope this survey can help researchers in related fields understand the recent progress made in existing works, and inspire novel applications of machine learning in smart cities.

Blockchain-Based Responsibility Management Framework for Smart City Building Information Modeling Projects Using Non-Fungible Tokens

In the context of digital construction, responsibility management in smart city building information modeling (BIM) projects spans the entire building lifecycle. The involvement of numerous BIM designers in project management and frequent data exchanges pose significant challenges for the traceability, immutability, and responsibility attribution of BIM models. To address these issues, this study proposes a blockchain-based responsibility management and collaboration framework for BIM projects using non-fungible tokens (NFTs), aiming to enhance the management of responsibilities and accountability in BIM projects. This research adopts a design science methodology, strictly adhering to scientific research procedures to ensure rigor. First, NFTs based on blockchain technology were developed to generate corresponding digital signatures for BIM model files. This approach ensures that each BIM model file has a unique digital identity, enhancing transparency and traceability in responsibility management. Next, the interplanetary file system (IPFS) was used to generate digital fingerprints, with the content identifier generated by IPFS uploaded to the blockchain to ensure the immutability of BIM model files. This method guarantees the integrity and security of BIM model files throughout their lifecycle. Finally, the proposed methods were validated through a blockchain network. The experimental results indicate that the proposed framework is theoretically highly feasible and demonstrates good applicability and efficiency in practical production. The constructed blockchain network meets the actual needs of responsibility management in smart city BIM projects, enhancing the transparency and reliability of project management.

Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks

Sixth-generation (6G) communication advancements target massive connectivity, ultra-reliable low-latency communication (URLLC), and high data rates, essential for IoT applications. Yet, in natural disasters, particularly in dense urban areas, 6G quality of service (QoS) can falter when terrestrial networks—such as base stations—become unavailable, unstable, or strained by high user density and dynamic environments. Additionally, high-rise buildings in smart cities contribute to signal blockages. To ensure reliable, high-quality connectivity, integrating low-Earth Orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RIS) into a multilayer (ML) network offers a solution: LEO satellites provide broad coverage, UAVs reduce congestion with flexible positioning, and RIS enhances signal quality. Despite these benefits, this integration brings challenges in resource allocation, requiring path loss models that account for both line-of-sight (LOS) and non-line-of-sight (NLOS) links. To address these, a joint optimization problem is formulated focusing on resource distribution fairness. Given its complexity, a framework is proposed to decouple the problem into subproblems using the block coordinate descent (BCD) method. These subproblems include UAV placement optimization, user association, subcarrier allocation via orthogonal frequency division multiple access (OFDMA), power allocation, and RIS phase shift control. OFDMA efficiently manages shared resources and mitigates interference. This iterative approach optimizes each subproblem, ensuring convergence to a locally optimal solution. Additionally, we propose a low-complexity solution for RIS phase shift control, proving its feasibility and efficiency mathematically. The numerical results demonstrate that the proposed scheme achieves up to 43.5% higher sum rates and 80% lower outage probabilities compared to the schemes without RIS. The low complexity solution for RIS optimization achieves performance within 1.8% of the SDP approach in terms of the sum rate. This model significantly improves network performance and reliability, achieving a 16.3% higher sum rate and a 44.4% reduction in outage probability compared to joint optimization of SAT-UAV resources. These findings highlight the robustness and efficiency of the ML network model, making it ideal for next-generation communication systems in high-density urban environments.

Energy-Saving Design of Smart City Buildings Based on Deep Learning Algorithms and Remote Sensing Image Scenes

Energy-Saving Design of Smart City Buildings Based on Deep Learning Algorithms and Remote Sensing Image Scenes

SUSTAINABLE URBAN TRANSFORMATION:

The integration of electric mobility with Smart Grids represents a transformative strategy for the development of sustainable urban systems, characterized by a reduced carbon footprint and high energy efficiency. This article carries out a systematic review with a bibliometric approach on the intersection between these two concepts, exploring not only the operational and environmental advantages, but also the social and economic implications of this synergy in building smart cities. The results indicate that the combination of electric mobility and Smart Grids is a rapidly growing area of study, with a significant increase in publications in high-impact scientific journals. Authors from globally renowned institutions have contributed to the consolidation of this field of research, reinforcing its relevance in the context of sustainable urban policies. Furthermore, an analysis of the literature reveals that Smart Grids, by incorporating new communication and information technologies, not only enable the large-scale adoption of electric mobility, but also promote the integrated and efficient management of urban infrastructure. This interconnectivity between systems allows for better optimization of the use of resources, as well as supporting the transition to more sustainable and resilient urban mobility.

Smart city governance: assessing modes of active citizen engagement

ABSTRACT This paper discusses how to govern smart cities that nurture and promote citizen engagement. By reviewing the emergent literature on the topic and rooting in a pluralistic – a descriptive and normative orientation, we identify three novel governance models: embedded, blended and networked. Our review indicates that the depth of citizen engagement varies between models based on the roles played by closely aligned resourceful actors. A collaborative environment driven by, for and with citizens seeks to build smart cities embedded in local realities. Drawing on examples from smart cities across the globe, we offer policy suggestions for building futuristic citizen-engaged smart cities.

Low-complexity short-time acoustic scene classification based on causal convolutions

Acoustic scene classification is vital for building smart cities. Most acoustic scene classification studies are performed by training convolutional neural networks. The networks constructed based on the general convolutional approach need to consider the temporal-frequency connections in the audio spectrogram. This work proposes a bottleneck structure neural network model for acoustic scene classification based on causal convolution and Sub-spectral normalization to overcome the abovementioned problem. To obtain more sample data, a new mix-spec augmentation data enhancement method is proposed by drawing on the existing classical mixup and spec augmentation methods, and the three are combined for sample augmentation. It was experimentally found that the proposed bottleneck structure helps improve the recognition accuracy and robustness of the model. In addition, the ablation experiment showed that the data augmentation training model combined with the mixup, SpecAugment, and the newly proposed mix-SpecAugment method had the highest classification accuracy. Finally, the near-square audio spectrogram has better training results by employing different shapes of audio spectrograms for the experiments. Compared with the baseline results, it is found that our proposed model significantly improved the accuracy of the baseline and reduced loss.

Public Food Trees’ Usage and Perception, and Their Potential for Participatory Edible Cities: A Case Study in Birjand, Iran

Public food trees are increasingly popular among researchers, urban planners, and citizens for their diversity of social, provisional, and environmental benefits. However, more research is needed to determine how to overcome their barriers. Here we used a qualitative approach to explore the usage and perception of public fruit trees and attitudes towards public usage and participation in two urban green spaces harboring fruit trees in Birjand, Iran: Tohid Park and Akbarieh Garden. Semi-structured interviews were conducted with twelve visitors to both spaces, four administrators, and eight workers. Almost all visitors had great personal experience and interest in picking fruit; nevertheless, usage in these green spaces was low, partly due to social norms. Almost all visitors appreciated the public fruit trees for the diverse pleasures that they provide (sensory, emotional, cognitive, experiential, social). Ten also had a high interest in participating in their management, especially in Tohid Park due to its greater social impact. Education and access limitation were suggested by some as important to reduce tree damage and maximize production. Our findings expand our understanding of how citizens relate to public urban fruit trees and can be involved in co-management schemes, thereby contributing to building smart and participatory edible cities.

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.

One Size Fits All: A Unified Traffic Predictor for Capturing the Essential Spatial-Temporal Dependency.

Traffic prediction is a keystone for building smart cities in the new era and has found wide applications in traffic scheduling and management, environment policy making, public safety, and so on. Instead of creating a traffic predictor for each city, this article focuses on designing a unified network model that could be directly applied for traffic prediction in any city, by learning the essential spatial-temporal dependencies, i.e., the mutual relationship between traffic and the corresponding fine-grained road network. To achieve this goal, this article proposes a joint knowledge- and data-driven mechanism that novelly divides dependencies into three kinds of correlations, i.e., road segment, intra-intersection, and inter-intersection correlation, which capture the microcosmic, middle, and macroscopic dependencies between traffic and the road network, respectively. Specifically, we first construct traffic datasets that could cover all road segments from real-world trajectory datasets, which makes it possible to model the whole road network as a graph, with the help of fine-grained road topology. Then, we propose meta road segment learner, connection-aware spatial-temporal graph convolutional network (GCN), and multiscale residual networks for capturing the microcosmic, middle, and macroscopic dependencies, respectively. Our experiments on three real-world datasets demonstrate that our proposed method could: 1) achieve better prediction accuracy compared with several approaches and 2) capture the mutual relationship between traffic and the fine-grained road network since our model trained only using data from the source city achieves good performance when it is directly applied for traffic prediction in the target city, without any fine-tuning. The codes will be made publicly available.

RSSI-WSDE: Wireless Sensing of Dynamic Events Based on RSSI.

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.

Smart governance for smart cities and nations

There is an increasing emphasis on utilizing ICT to drive global governmental transformation to enhance efficiency and cost-effective service delivery. Smart governance represents a novel and data-driven progressive approach, prioritizing intelligence in operations, upholding an exceptional standard of public administration, and contributing to the development of smart cities and nations. A smart city uses advanced technology and innovation to augment urban life and efficiency to ensure sustainability and a smart nation extends these principles across regions. Although smart governance is a priority in building smart cities and nations, its challenges and strategies are still not well-defined from the perspective of developing a smart nation and city. Smart Bangladesh is an inclusive digital transformation initiative and a grand vision of the government, advancing towards becoming a developed, prosperous, and smart nation by focusing on four key pillars: smart citizens, smart government, smart economy, and smart society. This study involved interviews with multi-level stakeholders and served as a preliminary step toward providing insights into and understanding the significant challenges and priorities in transforming a smart country and building smart cities. The research identifies fourteen prime challenges of smart governance that are pivotal for transforming Bangladesh into a smart nation and creating smart cities. Among these, stakeholders particularly emphasize the need for administrative reform, robust smart infrastructure, finance, uninterrupted electricity, strong data privacy and security, and effective big data management as crucial to the success of the country's vision. The analysis proposes a conceptual framework based on stakeholders' priorities that can serve as a practical guideline for practitioners to develop a strategic roadmap for effective preparedness to transition to a smart nation and build smart cities. The study fills a research gap in governance theory concerning the evolution of transformative technology-based governance, particularly emphasizing the significance of smart governance in the development of smart nations and cities.

Innovation Requirements for Good Governance of Local Government in Urban Government of Vietnam

Local governance is a quite popular term, however urban local governance is one of the new governance terms in Vietnam. Currently, in the world's urbanization trend associated with building e-government, digital transformation, building smart cities, etc., the issue of good management and administration of urban governments is increasing. is a requirement. Within the scope of this article, the author introduces the most common features of urban government, the distinction between urban government and rural government; At the same time, it points out the requirements for building an urban government that meets lean requirements and operates effectively and efficiently in Vietnam.

Taking Shenzhen and Shanghai as Examples to Analyze the Construction and Development of Smart Port Cities

In recent years, the enthusiasm for discussing building smart cities has been boosted greatly worldwide, and the residents of major super cities seem to be eager to understand what a smart city is. Unfortunately, due to the International Business Machines Corporations (IBM) recent introduction of the concept of smart cities, many people have misunderstood the concept of smart cities, and even mistakenly classify urban digitization as the scope of smart cities. The core element of a smart city is to use various Smart City Facilities (geographic information systems, urban security systems, intelligent traffic control systems, environmental monitoring systems, residents feedback systems) to bring the city onto an intelligent, automated, efficient, and livable road. This article aims to compare two significant Chinese port cities, Shenzhen and Shanghai, in their efforts to establish smart cities. It delves into the challenges they encountered during this process, analyzes the benefits accrued, and offers valuable reminding for cities aspiring to embark on their own smart city journey in the future.

Comprehensive Review of Smart Cities using IOT in Coud

The rapid growth of urbanization necessitates smarter and more sustainable city management. The convergence of Internet of Things (IoT) devices and cloud computing offers a transformative solution for building Smart Cities. This review explores the integration of IoT and cloud technologies in creating intelligent urban infrastructure. It examines how data collected from a network of sensors and devices across the city can be leveraged to improve various aspects of urban life, including traffic management, energy efficiency, public safety, waste management, and parking. The paper discusses the key benefits of cloud computing for Smart City IoT applications, such as scalability, centralized data management, cost-effectiveness, security, and advanced analytics capabilities. Additionally, it explores the challenges associated with implementing Smart City IoT solutions, such as data privacy concerns, cybersecurity risks, and the need for interoperability between different devices and platforms. Finally, the review concludes by highlighting the potential of Smart City IoT powered by cloud computing to create a more efficient, sustainable, and livable future for our cities.

Machine Learning-assisted Distance Based Residual Energy Aware Clustering Algorithm for Energy Efficient Information Dissemination in Urban VANETs

A Vehicular Ad-hoc Network (VANET) is an essential component of intelligent transportation systems in the building of smart cities. A VANET is a self-configure high mobile and dynamic potential wireless ad-hoc network that joins all vehicle nodes in a smart city to provide in-vehicle infotainment services to city administrators and residents. In the smart city, the On-board Unit (OBU) of each vehicle has multiple onboard sensors that are used for data collection from the surrounding environment. One of the main issues in VANET is energy efficiency and balance because the small onboard sensors can’t be quickly recharged once installed on On-board Units (OBUs). Moreover, conserving energy stands out as a crucial challenge in VANET which is primarily contingent on the selection of Cluster Heads (CH) and the adopted packet routing strategy. To address this issue, this paper proposes distance and energy-aware clustering algorithms named SOMNNDP, which use a Self-Organizing Map Neural Network (SOMNN) machine learning technique to perform faster multi-hop data dissemination. Individual Euclidean distances and residual node energy are considered as mobility parameters throughout the cluster routing process to improve and balance the energy consumption among the participating vehicle nodes. This maximizes the lifetime of VANET by ensuring that all intermediate vehicle nodes use energy at approximately the same rate. Simulation findings demonstrate that SOMNNDP improves Quality of Service (QoS) better and consumes 17% and 14% less energy during cluster routing than distance and energy-aware variation of K-Means (KM) and Fuzzy C-Means (FCM) called KMDP and FCMDP respectively.

Integration of Smart City Technologies with Advanced Predictive Analytics for Geotechnical Investigations

This paper addresses challenges and solutions in urban development and infrastructure resilience, particularly in the context of Japan’s rapidly urbanizing landscape. It explores the integration of smart city concepts to combat land subsidence and liquefaction, phenomena highlighted by the 2011 Great East Japan Earthquake. Additionally, it examines the current situation and lack of geoinformation and communication technology in the concept of smart cities in Japan. Consequently, this study employs advanced technologies, including smart sensing and predictive analytics through kriging and ensemble learning, with the objective of enhancing the precision of geotechnical investigations and urban planning. By analyzing data in Setagaya, Tokyo, it develops predictive models to accurately determine the depth of bearing layers that are critical to urban infrastructure. The results demonstrate the superiority of ensemble learning in predicting the depth of bearing layers. Two methods have been developed to predict undetected geographic data and prepare ground reality and digital smart maps for the construction industry to build smart cities. This study is useful for real-time analysis of existing data, for the government to make new urban plans, for construction companies to conduct risk assessments before doing their jobs, and for individuals to obtain real-time geographic data and hazard warnings through mobile phones and other means in the future. To the best of our knowledge, this is the first instance of predictive analysis of geographic information being conducted through geographic information, big data technology, machine learning, integrated learning, and artificial intelligence.

Smart city development Models: A cross-cultural regional analysis from theory to practice

Based on the multiple definitions and cultural interpretations of smart cities, this study systematically compiled the components, standards, and development modes of smart cities in various regions of the world and proposed a new integration of smart city dimensions and operational framework based on the development process, aiming to provide a theoretical basis and practical orientation for the transition of future development of smart cities and the formulation of planning policies. The conclusions of study are as follows: (1) In the process of building smart cities, countries’ understanding of smart cities is guided by their indigenous cultures, presenting various perceptions of smart city definitions, components, and standards in different geographic regions; (2) Scholars have proposed various dimensions and combinations for the development of smart cities based on their regional cultural, technological conditions, and temporal contexts, aiming to explore how cities can become 'smart'. (3) With the accumulation of experience in the construction of smart cities, the individual-technology-culture (IndiTeCultural) dimension model, which is a combination of individuals, technology, and culture, demonstrates better alignment with the requirements of smart city development around the world. This establishes the foundation for the transition from theory to practice in crafting a future operational framework for smart cities. (4) On the basis of cross-cultural geographical analysis, drawing on the practical experiences of smart city initiatives in various countries and the rating levels of their progress, this study analyses the challenges and opportunities faced by smart city development and presents the problems that need to be solved urgently for more effective mechanisms for smart city development.