Smart Cities Research Articles

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.

Innovate for Europe: a showcase of solutions in green tech, health tech and smart city solutions

Innovate for Europe: a showcase of solutions in green tech, health tech and smart city solutions URBAN TECH was a three-year project which, through its programme, provided opportunities for companies to propose solutions to more than 300 challenges. The selected 160 companies were able to validate the solution with challenge owners during hackathons. 80 companies were selected to develop an MVP, and 33 were able to do piloting with the help of a mentor and experts. Out of the 33 selected companies, 23 continued to a market discovery phase.

Smart PV Monitoring and Maintenance: A Vision Transformer Approach within Urban 4.0

The advancement to Urban 4.0 requires urban digitization and predictive maintenance of infrastructure to improve efficiency, durability, and quality of life. This study aims to integrate intelligent technologies for the predictive maintenance of photovoltaic panel systems, which serve as essential smart city renewable energy sources. In addition, we employ vision transformers (ViT), a deep learning architecture devoted to evolving image analysis, to detect anomalies in PV systems. The ViT model is pre-trained on ImageNet to exploit a comprehensive set of relevant visual features from the PV images and classify the input PV panel. Furthermore, the developed system was integrated into a web application that allows users to upload PV images, automatically detect anomalies, and provide detailed panel information, such as PV panel type, defect probability, and anomaly status. A comparative study using several convolutional neural network architectures (VGG, ResNet, and AlexNet) and the ViT transformer was conducted. Therefore, the adopted ViT model performs excellently in anomaly detection, where the ViT achieves an AUC of 0.96. Finally, the proposed approach excels at the prompt identification of potential defects detection, reducing maintenance costs, advancing equipment lifetime, and optimizing PV system implementation.

Identifying Re-identification Challenges: Past, Current and Future Trends

Person and vehicle re-identification has been a popular subject in the field of the computer vision technologies. Existing closed-set re-identification surpasses human-level accuracies on commonly used benchmarks, and the research focus for re-identification is shifting to the open world-setting. The latter setting is more suitable for practical applications, however, is less developed due to its challenges. On the other hand, existing research is more focused on person re-identification, even though both, person and vehicle, are important components for smart city applications. This review attempts to combine for the first time the problem of person and vehicle re-identification under closed and open settings, its challenges, and the existing research. Specifically, we start from the origin of the re-identification task and then summarize state-of-the-art research based on deep learning in different scenarios: person or vehicle or unified re-identification in closed- and open-world settings. Additionally, we analyse a new method for solving the re-identification task using the Transformer, a model architecture that relies entirely on an attention mechanism, which shows promising results. This survey facilitates future research by providing a summary on past and present trends, and aids to improve the usability of re-ID techniques.

Smart City Construction, Artificial Intelligence Development, and the Quality of Export Products: A Study Based on Micro-Level Data of Chinese Enterprises

Quality improvement is essential for a nation’s economy to transition from large to strong. In the 21st century, a new wave of quality development has emerged globally, and upgrading the quality of enterprise export products is a key measure for driving exports and supporting high-quality economic development. The development of artificial intelligence, as the new core engine driving technological revolution and industrial transformation, will profoundly alter various aspects of economic activities, including production, distribution, exchange, and consumption. Exploring and cultivating new artificial intelligence-driven momentum to enhance the quality of enterprise export products is inevitably a major theoretical and practical issue of common interest to governments, enterprises, and academia. This paper uses China, a major developing and export-oriented economy, as a case study to explore the policy measures for stimulating new momentum in artificial intelligence development and their effects and transmission mechanisms on improving the quality of enterprise export products. Specifically, it constructs a theoretical model to examine the relationship between smart city construction, artificial intelligence development, and the quality of enterprise export products. By considering the smart city construction projects launched by the Chinese government as a quasi-natural experiment to facilitate artificial intelligence development, the study employs matched city-enterprise data from 2007 to 2015 and utilizes a difference-in-differences (DID) methodology to empirically test the impact of smart city construction on enhancing the quality of enterprise export products. According to the study, the policy-driven nature of smart city construction significantly enhances the quality of enterprise export products. This beneficial impact is particularly evident in the eastern regions, as well as in labor-intensive and capital-intensive industries, and among foreign-invested and private enterprises. Mechanism tests and additional analyses indicate that artificial intelligence development is significantly more advanced in smart cities than in non-smart cities, with the gap between them steadily widening. The construction of smart cities significantly advances artificial intelligence development, which subsequently enhances the quality of enterprise export products. Furthermore, smart cities can substantially contribute to this improvement by facilitating a more efficient, market-oriented allocation of resources.

Internet of Things

The Internet of Things (IoT) represents a transformative trend in technology, enabling interconnected devices to communicate and exchange data seamlessly. This paper explores the fundamental concepts, current applications, challenges, and future prospects of IoT. It examines the impact of IoT on various industries, including healthcare, manufacturing, and smart cities, and discusses the security and privacy concerns associated with this technology. The study concludes with insights into future advancements and the potential societal implications of widespread IoT adoption

Multipurpose territorial cadastre and collaborative participation as tools for smart urban governance: An analysis considering the pandemic’s effect in the São Paulo Metropolitan Region, Brazil

The society lives in a dynamic, complex and contradictory urban system, seeking to reach a balance between urban development and environmental preservation. For this purpose, there is a need to perform an efficient urban management integrated with the information and communication technologies. From this integration, emerges the smart cities concept, which is composed of smart governance strategies and actions. In this sense, the present article aims to understand and discuss the contributions, potentials and public values of the multipurpose territorial cadastre and the collaborative participation as tools for smart governance. In order to validate the theoretical essay, a case study was performed to analyze urban governance in the São Paulo metropolitan region, Brazil, according to the use of information and geoinformation resources applied to cities’ management, pre- and post-pandemics. The results have shown that some practices of municipal governments have already been established aligned with the dimensions of smart governance. However, mainly, as for spatial activation in the use of geoinformation, as well as in the use of multipurpose territorial cadastre, it still has much to move forward and, therefore, to qualify with public value the strategies and actions of governance that a smart city requires.

Enhancing Energy Systems and Rural Communities through a System of Systems Approach: A Comprehensive Review

Today’s increasingly complex energy systems require innovative approaches to integrate and optimize different energy sources and technologies. In this paper, we explore the system of systems (SoS) approach, which provides a comprehensive framework for improving energy systems’ interoperability, efficiency, and resilience. By examining recent advances in various sectors, including photovoltaic systems, electric vehicles, energy storage, renewable energy, smart cities, and rural communities, this study highlights the essential role of SoSs in addressing the challenges of the energy transition. The principal areas of interest include the integration of advanced control algorithms and machine learning techniques and the development of robust communication networks to manage interactions between interconnected subsystems. This study also identifies significant challenges associated with large-scale SoS implementation, such as real-time data processing, decision-making complexity, and the need for harmonized regulatory frameworks. This study outlines future directions for improving the intelligence and autonomy of energy subsystems, which are essential for achieving a sustainable, resilient, and adaptive energy infrastructure.

Serverless Computing in the Edge-Cloud Continuum : Challenges, Opportunities, and a Novel Framework

This article explores the integration of serverless computing across the edge-cloud continuum, addressing the growing demand for low-latency, data-intensive applications in the era of the Internet of Things (IoT). We present EdgeServe, a novel framework that extends the serverless paradigm to encompass edge devices and cloud resources, overcoming the limitations of traditional cloud-centric approaches. Through comprehensive simulations and real-world case studies, including a smart city traffic management system, an industrial IoT predictive maintenance application, and a mobile augmented reality gaming platform, we evaluate the performance, scalability, and cost-effectiveness of our proposed framework. Our results demonstrate significant improvements in application response times, with latency reductions of up to 82% in time-critical functions, and an average cost reduction of 43% compared to cloud-only serverless deployments. We also observe a 28% decrease in overall energy consumption in certain scenarios. The article addresses key challenges in resource management, data consistency, adaptive function placement, and security in distributed environments. Our findings have important implications for application architects and cloud service providers, paving the way for a new generation of edge-native serverless platforms. This research contributes to the growing body of knowledge on distributed systems and offers insights into the future evolution of serverless computing in heterogeneous computing environments.

Leveraging Digital Twins and Intrusion Detection Systems for Enhanced Security in IoT-Based Smart City Infrastructures

In this research, we investigate the integration of an Intrusion Detection System (IDS) with a Digital Twin (DT) to enhance the cybersecurity of physical devices in cyber–physical systems. Using Eclipse Ditto as the DT platform and Snort as the IDS, we developed a near-realistic test environment that included a Raspberry Pi as the physical device and a Kali Linux virtual machine to perform common cyberattacks such as Hping3 flood attacks and NMAP reconnaissance scans. The results demonstrated that the IDS effectively detected Hping3-based flood attacks but showed limitations in identifying NMAP scans, suggesting areas for IDS configuration improvements. Furthermore, the study uncovered significant system resource impacts, including high Central Processing Unit (CPU) usage during SYN and ACK flood attacks and persistent memory usage after Network Mapper (NMAP) scans, highlighting the need for enhanced recovery mechanisms. This research presents a novel approach by coupling a Digital Twin with an IDS, enabling real-time monitoring and providing a dual perspective on both system performance and security. The integration offers a holistic method for identifying vulnerabilities and understanding resource impacts during cyberattacks. The work contributes new insights into the use of Digital Twins for cybersecurity and paves the way for further research into automated defense mechanisms, real-world validation of the proposed model, and the incorporation of additional attack scenarios. The results suggest that this combined approach holds significant promise for enhancing the security and resilience of IoT devices and other cyber–physical systems.

A Hybrid Method Based on Haris Corner and Maximally Stable Extremal Regions for Vehicle Plate Number Detection

An intelligent transportation system (ITS) is a crucial element of a smart city, and it includes the capability to identify vehicle license plates. Utilizing digital image processing is a cost-effective method for identification. The tiny size of the number plate is just one of the many unfortunate difficulties with this approach. Hence, this research is crucial, particularly in enhancing the precision of detection. The Harris Corner approach is one way to locate the motor vehicle number plate location. However, the Harris corner method could be more optimal for analyzing moving vehicle videos as input. Since frame-by-frame variations in the video input's lighting, accuracy cannot be maximized. Furthermore, the vehicle and license plate appear significantly smaller due to the camera's distant positioning. Consequently, the authors suggest a hybrid approach using the Maximally Stable Extremal Regions (MSER) method. The Harris Corner and MSER methods will concurrently identify the initial position of the vehicle number plate. Moreover, the initial detection outcomes of the two techniques are compared and adjusted to achieve a more precise determination of the placement of the number plate. The results show that integrating the MSER method into the Harris Corner method yields a hybrid approach that enhances accuracy by 13%. Furthermore, it visually represents the selected number plate with greater accuracy.

The Transition toward Smart and Sustainable Cities

Increasing population followed by uncontrolled city expansion neglected many essential criteria, resulting in management challenges, social service inequities, and worsened environmental conditions. However, this development is always ongoing and overshadows daily life. City administrators desire to implement smart cities to address a wide variety of prevalent issues in metropolitan areas. This study aims to take steps toward creating a smart city. Citizens' understanding and support for a project in a city are crucial to its success. The focus of this research is to examine citizens' awareness of smart cities and their importance in helping city managers, urban planners, and architects in improved planning to realize the vision of a smart city. This article comprehensively explains the smart city by compiling and evaluating relevant facts. Also, numerous smart cities throughout the world have been researched and compared to gain insight. Furthermore, being the most important part of a smart city, the level of public knowledge about "smart cities" in Iran through a survey distribution has been analyzed. This study has the potential to create new insight into city development. The findings offer novel perspectives that can be applied to the design and planning of intelligent cities.

Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities

The rapid urbanization trend underscores the need for effective management of city resources and services, making the concept of smart cities increasingly important. This study leverages the IMD Smart City Index (SCI) dataset to analyze and rank smart cities worldwide. Our research has a dual objective: first, we aim to apply a set of unsupervised learning models to cluster cities based on their smartness indices. Second, we aim to employ supervised learning models such as random forest, support vector machines (SVMs), and others to determine the importance of various features that contribute to a city’s smartness. Our findings reveal that while smart living was the most critical factor, with an importance of 0.259014. Smart mobility and smart environment also played significant roles, with the importance of 0.170147 and 0.163159, respectively, in determining a city’s smartness. While the clustering provides insights into the similarities and groupings among cities, the feature importance analysis elucidates the critical factors that drive these classifications. The integration of these two approaches aims to demonstrate that understanding the similarities between smart cities is of limited utility without a clear comprehension of the importance of the underlying features. This holistic approach provides a comprehensive understanding of what makes a city ’smart’ and offers a robust framework for policymakers to enhance urban living standards.

Enhancing Small Target Detection in Aerial Imagery with Bi-PAN-FPN and EDMOA-Optimized YOLOv8-s

Across the globe, people are working to build "smart cities" that will employ technology to make people's lives better and safer. Installing cameras at strategic spots across the city to monitor public spaces besides provide real-time footage to law enforcement besides other local authorities is a crucial part of smart city infrastructure, which includes video surveillance. A more effective answer is provided by deep learning algorithms, however research in this area still faces significant problems from changes in target size, form change, occlusion, and illumination circumstances as seen from the drone's perspective. In light of the aforementioned issues, this study presents a highly effective and resilient approach for aerial picture identification. To begin, the concept of Bi-PAN-FPN is presented to enhance the neck component of YOLOv8-s, taking into consideration the prevalent issue of small targets being easily misdetected or ignored in aerial photos. We achieve a more advanced and thorough feature fusion procedure much as feasible by completely considering and reusing multiscale features. To further reduce the amount of parameters in the model and prevent info loss during long-distance feature transfer, the benchmark model's backbone incorporates the GhostblockV2 structure in lieu of a portion of the C2f module. With the help of the Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA), the suggested model's hyper-parameters are optimised. Lastly, a dynamic nonmonotonic focusing mechanism is employed in conjunction with WiseIoU loss as bounding box regression loss. The detector accounts for varying anchor box quality by utilizing "outlier" evaluations, thus improving the complete presentation of the detection task.

Building an IoB ecosystem for influencing energy consumption in smart cities

Introduction: The Internet of Behaviors (IoB) represents a paradigm shift in integrating digital technologies with human behaviors, offering unprecedented insights and opportunities across various domains. This research paper explores the transformative potential of IoB and presents an innovative IoB framework applied to an energy consumption scenario. Objective: We offer an innovative IoB ecosystem aimed at heightening citizens' responsibility and awareness regarding home energy consumption in smart cities.Methodology: We propose a framework that elicits behavioral insights by leveraging smart meter data, clusters citizens based on similar energy consumption patterns using K-Means into groups, applies an LSTM-based prediction model to forecast their future energy consumption, and influences their behavior through a continuous personal reflection loop. Moreover, to foster trust, XAI principles are also integrated into our framework to ensure citizens comprehend and trust the IoB model's results.Results: Our proposed LSTM-based prediction model achieved, on the smart meters’ dataset, high-performance results, an R² value equal to 0.986, a root mean squared error of 0.492 and a mean squared error equal to 0.242. Conclusions: This paper presents how we can leverage the IoB and XAI into the energy sector. However, the IoB's potential is not restricted to a certain domain. It has a revolutionary influence across sectors, with the power sector standing out as one of the domains where the IoB has the potential to alter social practices

Strategizing Low Carbon Urban Planning Through Environmental Impact Assessment by Artificial Intelligence Driven Carbon Foot-print Forecasting

Addressing the associated rise in Carbon Emissions (CE) as smart cities expand becomes paramount. Effective low-carbon urban planning demands robust, precise assessments. This research introduces a cutting-edge solution via an Artificial Intelligence (AI) -driven Carbon Footprint (CF) impact assessment. A detailed dataset, collected over 3 years, was harnessed to gather insights into vital urban factors, including CE, Energy Consumption (EC) patterns, variations in land use, transportation dynamics, and changes in air quality. The cornerstone of this research is developing the Multi-modal Stacked VAR-LSTM model. This model proposes to provide accurate CF predictions for urban environments by merging the capabilities of Vector Autoregression (VAR) with Long Short-Term Memory (LSTM) neural networks. The process encompasses dedicated assessments for each data segment, harnessing VAR to delineate interdependencies and refining these predictions with the LSTM network using the residuals from the VAR analysis. By interweaving AI-driven carbon footprint impact assessments into the urban planning discourse, this study underscores the vast potential in sculpting future urban development strategies that are sustainable and sensitive to carbon impact.

Low Network Power Challenges in IoT-Based Applications in Smart Cities

The emergence of Internet of Things (IoT) technologies has led to the development of smart cities, potentially improving urban life through efficient and effective use of resources. The success of IoT-based applications in smart cities is contingent on several factors, including network power challenges. Deploying IoT-based applications in smart cities requires a reliable and efficient network infrastructure to ensure smooth and uninterrupted data communication. One of the significant challenges faced in this regard is low network power, which can significantly impact the performance of IoT devices and, consequently, the entire smart city system. This article addresses the issue of low network power in IoT-based applications in smart cities. It investigates the causes of this problem and the potential solutions that can be adopted to mitigate its impact. The article also highlights the importance of network power management in the context of IoT-based applications in smart cities. By implementing effective network power management techniques, it is possible to optimize the performance of IoT devices and extend their battery life, thus ensuring the overall efficiency and sustainability of the smart city system. The article concludes by emphasizing the need for continuous research and development to overcome the challenges of low network power and further enhance IoT technology's capabilities in smart cities.

A Bibliometric Analysis of Research on the Metaverse for Smart Cities: The Dimensions of Technology, People, and Institutions

The “Metaverse” is evaluated as having significant potential in a “Smart city” design and operation. Despite growing interest, there is still a lack of comprehensive quantitative analysis on the “Metaverse”, particularly in the context of smart cities. This study conducts a bibliometric analysis of 604 articles selected from the “WoS” database and employs three dimensions of technology, people, and institutions as a balanced perspective on smart cities, providing a comprehensive understanding of research trends on the “Metaverse” in the context of smart cities. This study identifies the “Metaverse” as a Virtual reality technology, popular since 2021, and provides information on the active years, countries, fields, journals, authors, and institutions involved in “Metaverse” research on smart cities. This study also identifies three stages of research development as follows: Stage 1 (2007–2013) to Stage 2 (2014–2020) and Stage 3 (2021–20 October 2023), revealing the research focus evolution from basic “urban planning” to complex “urban governance” and “Smart city” construction with consideration of multi-stakeholders’ perspectives. Additionally, this study reveals that “Metaverse” research studies on the “technology” dimension have consistently outnumbered that on “institutions” and “people” across all stages in the “Smart city” domain. These findings address current theoretical gaps and offer a foundation for future research.

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.

State-led smart city with Chinese characteristics: the case of Xiong’an New Area, China

ABSTRACT Smart cities have emerged as either a technical fix to challenges of urbanized accumulation or an adaptable governance strategy under neoliberal, entrepreneurial, and austerity politics worldwide. Utilizing Xiong'an New Area, the most ambitious experimentation in China, as an empirical case, we present a distinct smart city model under the governance framework of state entrepreneurialism. This paper examines the salient and proactive role of the state in the ongoing smartification and digitalization in Xiong’an New Area. We highlight the plural (geo)political agendas parallel to capital accumulation in this state project. Based on in-depth interviews and documentary analysis, we illustrate how the state steered experimental practices through innovating institutional settings and financing strategy, legitimizing ‘digital twin’ experimentation in urban management, and co-opting non-state actors, including technical professional organizations and domestic technology enterprises. Its success rests more on its demonstration effects nationwide than the ‘actually existing’ smart city/urbanism within the territory of Xiong’an. This ‘state-led smart city with Chinese Characteristics’ shows unique features of smart governance shaped by political economic dynamics in China, compared to typical smart city models in Western capitalist countries and Asian developmental countries. The findings further the nuanced understanding of smart initiatives and projects in different contexts and the role of the government through the perspective of entrepreneurialism.