Smart Cities Research Articles

Industry 5.0 for Society 5.0 a new Transformation

Industry 5.0 represents a transformative approach within Society 5.0, leveraging advanced technologies like AI, IoT, Robotics, and Blockchain to enhance productivity, automation, and decision-making across various sectors. This abstract examines how privacy, security, and ethical issues are addressed by modern technologies to create a societal structure that is morally and humanely sound. IoT improves smart city infrastructure and environmental monitoring, while AI enables tailored education and predictive healthcare. Blockchain guarantees safe and transparent data transfers, while robotics helps with accurate healthcare operations. Collectively, these technologies support Society 5.0's sustainability, inclusivity, and resilience. They also hold out hope for a time when technological breakthroughs will preserve strict ethical standards and cybersecurity safeguards while prioritizing the needs of people and society.

How 3D Printing Technology Makes Cities Smarter: A Review, Thematic Analysis, and Perspectives

This paper presents a comprehensive review of the transformative impacts of 3D printing technology on smart cities. As cities face rapid urbanization, resource shortages, and environmental degradation, innovative solutions such as additive manufacturing (AM) offer potential pathways for sustainable urban development. By synthesizing 66 publications from 2015 to 2024, the study examines how 3D printing improves urban infrastructure, enhances sustainability, and fosters community engagement in city planning. Key benefits of 3D printing include reducing construction time and material waste, lowering costs, and enabling the creation of scalable, affordable housing solutions. The paper also addresses emerging areas such as the integration of 3D printing with digital twins (DTs), machine learning (ML), and AI to optimize urban infrastructure and predictive maintenance. It highlights the use of smart materials and soft robotics for structural health monitoring (SHM) and repairs. Despite the promising advancements, challenges remain in terms of cost, scalability, and the need for interdisciplinary collaboration among engineers, designers, urban planners, and policymakers. The findings suggest a roadmap for future research and practical applications of 3D printing in smart cities, contributing to the ongoing discourse on sustainable and technologically advanced urban development.

Parking Assistant with the Simulation of Application

Smart Parking can be used for any city. It aims to improve the efficiency and sustainability of urban transportation systems by using technology to manage parking systems. This article provides an overview of the different components of smart parking systems. The increasing vehicular population in urban areas has led to significant challenges related to parking management, exacerbating traffic congestion, environmental impact, and user dissatisfaction. The app facilitates real- time information on available parking spaces, reservation capabilities, and payment integration, thereby enhancing user experience and operational efficiency. We summarize the methodologies applied in developing the app, which include sensor integration for dynamic parking space monitoring and mobile application development for user interaction. The article concludes with a consideration of the future of smart parking and its role in the overall development of smart cities. In conclusion, the findings serve as a basis for future advancements in intelligent parking systems, ensuring improved service delivery for urban commuters.

Rethinking the Smart Green City Project in South Korea: A Critical Juncture Approach

Over the past 10 years, the Korean government has declared its intention to achieve carbon neutrality to the international community and strived to create smart cities and green cities as a practice to achieve carbon neutrality. In the same context, the Smart Green City project, which began in earnest at the end of 2020, initially attracted national attention due to the strong will of the administration. However, limitations in its performance were revealed. This study aimed to identify the policy mechanisms that led the Korean government to pursue the Smart Green City project using a critical juncture approach and to derive useful implications for policy selection. A critical juncture framework was applied for in-depth analysis, and a descriptive case study based on critical thinking was conducted. The results confirmed that endogenous and exogenous environmental changes surrounding the government at critical junctures were mechanisms for establishing the Smart Green City project. In addition, the government’s attempt to differentiate itself from the policies of previous governments was confirmed to be a constraint that actually hindered the project’s progress. This study concludes that if a government facing a complex crisis makes hasty policy decisions, it is likely to repeat the policy pattern or institutional path of previous governments. It also argued that in this situation, policy choices that aim to cut a new path may cause the government to choose a wrong path, which may have irreversible consequences.

Integration of cost-effective datasets to improve predictability of strategic noise mapping in transport corridors in Delhi city, India.

Assessing exposure to environmental noise levels at transport corridors remains complex in conditions where no standardized noise prediction model is available. In planning and policy implementation for noise control, noise mapping is an important step. In the present study, land use regression model has been developed to predict the environmental noise levels in Delhi city, India, using previously developed approaches along with machine learning techniques, however improved using new datasets. Lday, Lnight, LAeq,24h, and Ldn were modeled at daily resolution by utilizing an annual noise levels dataset from 31 locations in Delhi city. The noise-monitored data was integrated with travel time data, nighttime light data along with common parameters including land use, road networks, and meteorological parameters. The developed LUR models showed good fit with R2 of 0.72 for Lday, 0.55 for Lnight, 0.71 for LAeq,24h, and 0.61 for Ldn, which was further improved up to 0.88 for Lday, 0.79 for Lnight, 0.86 for LAeq,24h, and 0.81 for Ldn by integrating machine learning approaches. The developed models were validated through tenfold cross validation and by comparison to a separate noise level dataset. The average travel time variable was observed to be the most influential predictor of LUR models for Lday and LAeq,24h, which signifies the crucial impact of road traffic congestion on environmental noise levels. The study also analyzed the parametric sensitivity of various infrastructural factors reported in the study, which shall be helpful for planning for smart cities.

Can smart city construction be the answer to sustainable development? Evidence from Chinese corporates

The construction of smart cities plays a pivotal role in promoting regional sustainability by utilizing technology-driven urban development in the digital era. This study employs the difference-in-differences model to empirically analyze the impact of the smart city pilot policy on corporate carbon intensity, using data from Chinese A-share listed corporates from 2009 to 2021. The findings are as follows: First, the smart city pilot policy significantly reduces corporate carbon intensity in pilot cities, and this conclusion remains robust after a series of sensitivity tests. Second, the policy exhibits heterogeneous effects on corporate carbon intensity across different industries and city locations, with more pronounced effects observed in central cities, traditional industries, and heavily polluting industries. Third, mechanism analysis reveals that the policy reduces corporate carbon intensity through three channels: promoting technological innovation, increasing external market attention, and providing ex-ante government subsidies.

Fermatean Fuzzy TOPSIS Method and Its Application in Ranking Business Intelligence-Based Strategies in Smart City Context

With the expansion of smart cities, the use of business intelligence (BI) has emerged as a crucial tool for resource optimization, increasing efficiency, and improving the citizens’ quality of life. BI enables companies to make better strategic decisions by analyzing vast amounts of urban data, helping them remain competitive in the dynamic smart city environment. This study utilizes content analysis and the Fermatean Fuzzy TOPSIS (FF-TOPSIS) method to rank the strategies based on business intelligence in the context of smart city. Initially, relevant criteria were identified through content analysis, and subsequently, five strategies were developed and ranked based on these criteria. The results revealed that the "Development of IOT-enabled smart networks (S2)" ranked highest, as it plays a significant role in optimizing resource management and enhancing urban service performance, thereby contributing greatly to the advancement of smart cities. "Process automation and the deployment of robotic systems (S5)" ranked second, as it enhances efficiency and reduces human errors. "Cloud platform integration for seamless access to data and services (S3)" also proved to be of considerable importance, ranking third, as it provides seamless access to data and services. " Artificial intelligence deployment for predictive analytics and process optimization (S4)" ranked fourth and was vital for predictive analytics and process optimization, while " Big data analytics for smart decision-making (S1)"—despite its importance—ranked fifth. Urban managers should prioritize the development of IOT networks to fully leverage their potential for resource management and efficiency gains. Following this, attention to process automation and AI integration can significantly enhance the quality of life for citizens and reduce urban costs.

HARNESSING BLOCKCHAIN WITH ENSEMBLE DEEP LEARNING-BASED DISTRIBUTED DOS ATTACK DETECTION IN IOT-ASSISTED SECURE CONSUMER ELECTRONICS SYSTEMS

Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques.

Renewable energy driven on-road wireless charging infrastructure for electric vehicles in smart cities: A prototype design and analysis

The adoption of electrical vehicles (EVs) has increased due to the increasing concerns about fossil fuel shortages, rising costs, and environmental impacts. This increasing adoption of EVs significantly increase power demand on utility grids. Moreover the EVs require more time for charging compared to fossil fuel vehicles. To address these challenges, this study proposes a renewable energy-based on-road wireless charging (ORWC) infrastructureto reducing charging time and utilizing renewable energy within a smart city framework. This study develops a prototype of the ORWC system and examines its techno-economic and environmental benefits. Moreover, the study also evaluates annualized energy share, energy economics, and cost-effectiveness. The results of the proposed study indicate that the ORWC system in combination with RERs, effectively reduces the cost of energy from $0.112/kWh to $0.30/kWh and lowers carbon emissions from 76.9 tons/year to 29.5 tons/year. Furthermore, the results also reveals optimal energy distribution among different energy resources PV, BESS, and the grid. These results highlight that ORWC infrastructure can reduce peak load demand on existing grids and enhance the power density of EV charging systems. This has potential implications for future-oriented fast charging stations and policy development to support the widespread adoption of EVs.

A Cross-Layer Secure and Energy-Efficient Framework for the Internet of Things: A Comprehensive Survey

This survey delves into cross-layer energy-efficient solutions and cutting-edge security measures for Internet of Things (IoT) networks. The conventional security techniques are considered inadequate, leading to the suggestion of AI-powered intrusion detection systems and novel strategies such as blockchain integration. This research aims to promote the development of smart cities by enhancing sustainability, security, and efficiency in the industrial and agricultural sectors through the use of IoT, blockchain, AI, and new communication technologies like 5G. In this paper, we provide a comprehensive review and analysis of secure and energy-efficient cross-layer IoT frameworks based on survey of more than 100 published research articles. We highlight the significance of developing IoT security for robust and sustainable connected systems. We discuss multi-layered security approaches and ways to enhance the energy efficiency of resource-constrained devices in IoT networks. Finally, we identify open research issues and future research directions in the emerging field of cross-layer design for secure and energy-efficient IoT networks. In order to improve cybersecurity and efficiency in smart cities, the research also focuses on developing a secure, energy-efficient IoT framework integrating blockchain, artificial intelligence, and quantum-safe cryptography.

Real-Time Tracking System for City Bus Location And Passenger Count

This survey paper reviews the advancements in real-time tracking and passenger counting systems for city buses, emphasizing the growing need for efficient, data-driven public transit solutions. As urban populations rise, city transport systems face challenges in managing demand, reducing congestion, and improving service reliability. Real-time tracking systems using GPS, GIS, and IoT-enabled sensors enable passengers and transit authorities to monitor bus locations and optimize travel times. Additionally, passenger counting systems—utilizing infrared, AI-based vision models, and pressure sensors—provide data on bus occupancy levels, allowing commuters to make informed travel decisions and helping transit agencies manage resources efficiently. This paper examines existing technologies, compares various tracking and counting methods based on accuracy, cost, and scalability, and explores recent trends in artificial intelligence, big data analytics, and smart city integration. Furthermore, it identifies critical challenges, including data privacy, accuracy in urban environments, and energy consumption, proposing future research directions for enhancing the reliability, sustainability, and user-friendliness of these systems. Our findings underscore the transformative potential of real-time bus tracking and passenger counting in advancing public transportation infrastructure and enhancing urban mobility

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

Modeling and Optimization of NO2 Stations in the Smart City of Barcelona

The growing problem of nitrogen dioxide (NO2) pollution in urban environments is driving cities to adopt smart and sustainable approaches to address this challenge. To quantify and compare the effect of environmental policies, cities must be able to make informed decisions with real-time data that reflect the actual situation. Therefore, the objective of this work is threefold: The first is to study the behavior of the key performance indicator (KPI) of NO2 concentrations per station in Barcelona through exploratory analysis and clustering. The second is to predict NO2 concentration behavior, considering meteorological data. Lastly, a new distribution of current and new stations will be proposed using an optimization algorithm that maximizes the distance between them and covers the largest area of the city. As a result of this study, the importance of the location of measurement points and the need for better distribution in the city are highlighted. These new spatial distributions predict an 8% increase in NO2 concentrations. In conclusion, this study is a comprehensive tool for obtaining an accurate representation of NO2 concentrations in the city, contributing to informed decision-making, helping to improve air quality, and promoting a more sustainable urban environment.

Japan’s Society 5.0 and the Organizational Vision of ‘Human-Centric’ AI

Abstract This article examines the Japanese government’s Society 5.0 initiative and its intersection with surveillance based on artificial intelligence (AI). Announced in 2019, Society 5.0 seeks to leverage AI to address Japan’s aging population and other social issues. I examine government documents on the initiative and an example of a smart city project that implements the Society 5.0 vision and, in doing so, highlight a tension between its architects’ proclaimed commitment to socially responsible, ‘human-centric’ uses of AI and the frequent ambiguity of their invocations of the technology. I argue that this lack of a clear definition of AI forestalls an interrogation of the expansive surveillance infrastructure that the initiative would entail. Examining Society 5.0 and its proposed infrastructure, I reveal a schema of digital surveillance and control best characterized as organizational in nature, as it seeks to monitor and coordinate movements, interactions, and behaviours.

Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach

Pipeline leakage represents a critical challenge in smart cities and various industries, leading to severe economic, environmental, and safety consequences. Early detection of leaks is essential for overcoming these risks and ensuring the safe operation of pipeline systems. In this study, a hybrid convolutional neural network–long short-term memory (CNN-LSTM) model for pipeline leak detection that uses acoustic emission signals was designed. In this model, acoustic emission signals are initially preprocessed using a Savitzky–Golay filter to reduce noise. The filtered signals are input into the hybrid model, where spatial features are extracted using a CNN. The features are then passed to an LSTM network, which extracts temporal features from the signals. Based on these features, the presence or absence of a leakage is determined. The performance of the proposed model was compared with two alternative approaches: a method that employs combined features from the time domain and LSTM and a bidirectional gated recurrent unit model. The proposed approach demonstrated superior performance, as evidenced by lower validation loss, higher validation accuracy, enhanced confusion matrices, and improved t-distributed stochastic neighbor embedding plots compared to the other models when tested on industrial data. The findings indicate that the proposed model is more effective in accurately detecting pipeline leaks, offering a promising solution for enhancing smart cities and industrial safety.

Crowd Flow Prediction: An Integrated Approach Using Dynamic Spatial–Temporal Adaptive Modeling for Pattern Flow Relationships

ABSTRACTPredicting crowd flows in smart cities poses a significant challenge for the intelligent transportation system (ITS). Traffic management and behavioral analysis are crucial and have garnered considerable attention from researchers. However, accurately and timely predicting crowd flow is difficult due to various complex factors, including dependencies on recent crowd flow and neighboring regions. Existing studies often focus on spatial–temporal dependencies but neglect to model the relationship between crowd flow in distant areas. In our study, we observe that the daily flow of each region remains relatively consistent, and certain regions, despite being far apart, exhibit similar flow patterns, indicating a strong correlation between them. In this paper, we proposed a novel Multiscale Adaptive Graph‐Gated Network (MSAGGN) model. The main components of MSAGGN can be divided into three major parts: (1) To capture the parallel periodic learning architecture through a layer‐wise gated mechanism, a layer‐wise functional approach is employed to modify gated mechanism, establishing parallel skip periodic connections to effectively manage temporal and external factor information at each time interval; (2) a graph convolutional‐based adaptive mechanism that effectively captures crowd flow traffic data by considering dynamic spatial–temporal correlations; and (3) we proposed a novel intelligent channel encoder (ICE). The task of this block is to capture citywide spatial–temporal correlation along external factors to preserve correlation for distant regions with external elements. To integrate spatio‐temporal flexibility, we introduce the adaptive transformation module. We assessed our model's performance by comparing it with previous state‐of‐the‐art models and conducting experiments using two real‐world datasets for evaluation.

Implementation of Smart Government, Smart Mobility, and Smart Living Policies in Realizing the Quality of Public Services in Yogyakarta City

Major cities around the world believe in the concept of "smart cities" or "intelligent cities". This concept encourages people to participate in city management through a citizen-centered approach. This results in more active and close interaction between citizens and service providers, in this case the local government. This study examines how the Yogyakarta City government can implement e-government to become a Smart City and improve its public services by implementing smart government, smart mobility, and smart living policies. Cities around the world are currently championing this idea as the central government's idea to address various problems in different places. This research uses a descriptive qualitative method by relying on secondary data from digital mass media news which is assisted by data processing using Nvivo 14 as a research tool. Based on the results of the research, it can be found that Smart City in Yogyakarta City shows that (1) Smart Government regarding its online procedures has started to run well; (2) Smart Mobility is superior in traffic monitoring; (3) Smart Living is more prominent in indicators about security. Where with these results, Policy Implementation in the City of Yogyakarta has started to run well, but there must still be improvements, especially in Smart Government, Smart Mobility, and Smart Living so that the quality of public services in the City of Yogyakarta can run even better. Key word: Implementation of smart government, smart mobility, smart living, Yogyakarta CIty

Beautifying urban environment: Smart city construction and sustainable pollution control in China

Faced with the environmental pollution challenges brought by urbanization, smart city construction (SCC) offers a practical and viable pathway for environmental protection. This study treats China's SCC policy as a quasi-natural experiment, utilizing balanced panel data from 285 cities across the country spanning from 2003 to 2019. Through the multi-period Difference-in-Differences (DID) model, the influence of smart city initiatives on urban pollution emissions is systematically examined. Results demonstrate that SCC effectively control urban pollution emissions, leading to a 1.46% decrease in pollution levels in pilot cities. These findings are robust to various tests, including parallel trends analysis, placebo tests, Propensity Score Matching (PSM)-DID, alternative pollution calculation methods, pollution intensity adjustments, control for other policy interferences, and sample adjustments, alongside endogeneity tests using instrumental variables. SCC achieves pollution control through mechanisms that enhance urban technological innovation, facilitate industrial restructuring, and talent agglomeration and financial agglomeration. Heterogeneity analysis reveals a more pronounced impact in the eastern area, large cities, higher administrative levels, cities with superior digital infrastructure, and those with higher human capital levels and pollution severity. This research explores a practical approach towards achieving sustainable pollution control by optimizing technology, industrial structure, and human resources.

Home Services Recommendation System using Machine Learning for Urban Areas

The Kotlin-based on-demand home service system streamlines household tasks by offering services such as repairs, cleaning, gardening, and plumbing. In today's fast-paced world, it provides quick solutions for maintaining a clean and hygienic living environment. The Housekeeper Finder app is a prominent player in this space, utilizing advanced machine learning algorithms to deliver personalized service recommendations based on user preferences. It includes essential services like cooking, laundry, and elder care, along with a robust feedback system to improve service quality. Users can easily book services from anywhere, leveraging GPS technology to connect them with nearby providers. The "Domestic Android Application for Home Services" further enhances accessibility through location-based service requests and mapping capabilities for a seamless user experience across platforms.A s cities transition into Smart Cities, integrated information systems open new avenues for urban service recommendations, fostering liveability and sustainability. Recommendation systems analyse user behaviour to provide tailored suggestions using techniques like collaborative filtering and hybrid methods. In response to the challenges of modern work culture, the e-commerce landscape now offers easy access to essential services via mobile applications, ultimately transforming how households address their needs and promoting safer, cleaner living space.

A Novel Spatial Data Pipeline for Streaming Smart City Data

Point cloud data from light detection and ranging (LiDAR) is often used for its spatial qualities, particularly in smart city projects involving vehicles and pedestrians. In this paper, the authors introduce a streaming and an on-demand pipeline for capturing LiDAR data from Velodyne Ultra Pucks placed along nine northern Nevada intersections known as the Living Lab within smart city project for the City of Reno. This pipeline is an iteration of a previously proposed pipeline with several feature enhancements. A streaming point cloud service was implemented to stream Point Cloud Data (PCD), LASzip (LAZ), and Google Draco. Also, two web services were built for the packet capture (PCAP) and ROS 2 bag files that enables acquisition of these formats for LiDAR data. A metadata service tracks edge device states and a GraphQL service interfaces with multiple services across the Living Lab. Draco provided the best processing time and had more options that affected the quality of the point cloud. To evaluate this pipeline, a discussion is provided, with an analysis of the point cloud formats.