Smart City Management Research Articles

High‐Accuracy Liquid Flow Monitoring via Triboelectric Nanogenerator Combined with Bionic Design and Common‐Mode Interference Suppression

AbstractIn the development of smart cities, accurate liquid flow monitoring is essential for the efficient operation of water supply systems. Current flow sensors often face limitations in sensitivity and environmental adaptability, affecting measurement accuracy, and restricting their application in smart city infrastructure. To address these challenges, this study proposes a high‐accuracy flow monitoring method. Specifically, by combining the bionic design with advanced signal processing techniques, the sensitivity and anti‐interference ability are improved, respectively, to enhance the measurement accuracy. Based on this method, a self‐powered flow sensor (SPFS) is developed using noncontact triboelectric nanogenerators (NC‐TENGs) as the sensing unit. The SPFS achieves a sensitivity of 2.07 Hz L−1 min−1 and improves the signal‐to‐noise ratio by more than 13 times over the initial sensing signal. In addition, an intelligent system is developed to accurately measure water resources. The maximum flow rate error rate is less than 0.97% compared to commercial flow sensors. The SPFS demonstrates higher sensitivity and accuracy compared to the existing TENG flow sensors. This study addresses the limitations of existing flow sensors and pioneers a novel solution for enhanced water resource management in smart cities.

A Practice of City-scale 3D Geographic Entity Representation and Application in China: The Smart Chongqing City Centre

Abstract. The Chinese photogrammetry industry is currently experiencing a significant revolution driven by digital intelligence technologies. Traditional city-scale 3D modelling, typically reconstructed from oblique photogrammetry or laser points, is facing increasing limitations in refined expression, real-time rendering, machine recognition, data sharing, and interoperability due to its large data volume and mesh structure. These limitations make it difficult to meet the substantial demands of spatial analysis, simulation, and prediction. The concept of geographic entities, which are machine-recognizable spatial data structures, is anticipated to provide a competitive solution for these challenges. This work employs 3D scene modelling and geographic knowledge graphs to construct geographic entities with semantic relationships at city-scale, addressing the limitations encountered in the application of traditional mesh/point-based 3D models. A case study in Chongqing city centre demonstrates the feasibility and effectiveness of the proposed approach and its potential applications in simulation, prediction, early warning, and feedback for urban planning and smart city management.

Assessing awareness of IoT-driven strategies for poultry management in mitigating increased ambient heat in Enugu, Nigeria

The study examined awareness level of Internet of Things (IoT)-driven strategies in mitigating impacts of increased ambient heat load in poultry pens by poultry farmers in a smart city like Enugu, Nigeria. With the rising challenges of climate change, increased ambient heat load within poultry pens poses a significant threat to poultry production. Understanding farmers’ perception of impact of increased ambient heat load and their awareness of IoT-driven strategies for its mitigation is crucial for sustainable poultry system management in smart cities. Multi-stage random sampling technique was used to select 240 poultry farmers from the three Local Government Areas that make up Enugu City. A structured questionnaire was used to collect data from the respondents. Descriptive statistics and Likert rating scale were used to analyze the data. The study identified socio-demographic factors influencing farmers’ perception of impact of increased heat load and awareness/ adoption of IoT-driven mitigation strategies. Education level (45% secondary education), access to information (29.2%) and financial resources (22.9%) were key determinants. The findings revealed varying levels of awareness among poultry farmers regarding IoT-driven strategies while the constraints faced by farmers in implementing sustainable IoT-driven mitigation strategies included lack of awareness, lack of funding/high initial investment, limited access to technology, inadequate training, poor extension contact, water availability and climate variability. The study concluded that majority of poultry farmers shared similar perceptions about the impact of increased ambient heat load on poultry species but however were not aware of many of the IoT-driven strategies to mitigate the impact and were limited by a number of constraints or challenges to adopting IoT-driven mitigation strategies which altogether could improve poultry production system and help to achieve animal protein security in a smart city like Enugu, Nigeria. The study further recommended enlightenment of poultry farmers on the impact of increased ambient heat load on poultry species and available/sustainable/harmonized IoT-enabled guidelines or solutions to mitigate heat load in smart cities’ poultry production system.

AI and Human-Centric Approach in Smart Cities Management: Case Studies from Silesian and Lesser Poland Voivodships

The presented paper examines the integration of Artificial Intelligence (AI) in the management of smart cities, focusing on the Silesian and Lesser Poland Voivodships in Poland. This research addresses a notable gap in the analysis of regional AI strategies within urban management, providing a comparative analysis of AI implementation in these two distinct regions. The Silesian Voivodship, with its emphasis on traditional industries such as manufacturing and energy, contrasts with the broader approach of the Lesser Poland Voivodship, which includes applications in life sciences and ICT. The paper explores how AI technologies enhance urban efficiency, sustainability, and livability through practical applications in traffic management, healthcare, energy efficiency, and environmental management. It highlights the importance of a human-centric approach in smart city development, emphasizing inclusivity, transparency, and ethical considerations. The paper also delves into the socio-technical dynamics of AI deployment, illustrating how these technologies can transform urban environments while ensuring that the benefits are equitably distributed and that urban developments are sustainable and resilient. By analyzing specific case studies, the authors aim to provide empirical evidence and insights that contribute to the academic and practical understanding of AI’s role in smart cities, ultimately advocating for the design of AI applications that prioritize human well-being and environmental health.

An ML-Based Solution in the Transformation towards a Sustainable Smart City

The rapid development of modern information technology (IT), power supply, communication and traffic information systems and so on is resulting in progress in the area of distributed and energy-efficient (if possible, powered by renewable energy sources) smart grid components securely connected to entire smart city management systems. This enables a wide range of applications such as distributed energy management, system health forecasting and cybersecurity based on huge volumes of data that automate and improve the performance of the smart grid, but also require analysis, inference and prediction using artificial intelligence. Data management strategies, but also the sharing of data by consumers, institutions, organisations and industries, can be supported by edge clouds, thus protecting privacy and improving performance. This article presents and develops the authors’ own concept in this area, which is planned for research in the coming years. The paper aims to develop and initially test a conceptual framework that takes into account the aspects discussed above, emphasising the practical aspects and use cases of the Social Internet of Things (SIoT) and artificial intelligence (AI) in the everyday lives of smart sustainable city (SSC) residents. We present an approach consisting of seven algorithms for the integration of large data sets for machine learning processing to be applied in optimisation in the context of smart cities.

AI-powered Strategies for Optimizing Waste Management in Smart Cities in Beijing

The study investigates the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies into Beijing's waste management system, emphasizing their effects on operational efficiency, environmental sustainability, and economic feasibility. The deployment of AI-driven route optimization and IoT-enabled real-time monitoring resulted in a 25% reduction in waste collection trips and a 30% decrease in waste overflow incidents. These advancements led to notable reductions in fuel consumption and environmental impact, while an economic analysis projected a Net Present Value (NPV) of $3.5 million over a 10-year period, affirming the financial benefits of these technologies. The findings highlight the pivotal role of AI and IoT in optimizing urban waste management practices. The study offers policy recommendations for the phased and strategic adoption of these technologies within Beijing, with the potential to enhance efficiency and contribute to the city’s sustainability objectives. Future research is advised to examine the long-term sustainability of AI-driven waste management strategies and assess the applicability of these technologies in diverse urban environments.

Exploring the influence of linear infrastructure projects 4.0 technologies to promote sustainable development in smart cities

Industry 4.0 technologies have a high potential to improve the planning and execution of linear projects such as roads, bridges and railroads. These technologies can help reduce emissions, enhance operations' efficiency, and improve users' quality of life. Despite their potential to promote sustainability in smart cities, there is a lack of research focused on analysing their impact. Therefore, this paper aims to identify 4.0 technologies related to linear projects and examine their influence on smart cities' sustainable development. The research method involved four main stages: identification of 4.0 technologies in linear projects, questionnaire design and application, influence analysis, and factor analysis. A systematic literature review identified a set of 4.0 technologies that favour smart cities' sustainable development. Subsequently, 66 experts were consulted to determine which of these technologies have the most significant influence on smart cities' sustainable development, highlighting data analysis and management, intelligent traffic control systems, and artificial intelligence. This study makes three main contributions: (1) it identifies thirty-seven 4.0 technologies associated with linear projects that promote sustainable development in smart cities; (2) it characterises the 4.0 technologies with the most significant influence on the sustainability of smart cities; and (3) it proposes nine principal components into which interrelated 4.0 technologies that promote sustainable development can be grouped. This study provides valuable evidence for smart city managers and offers guidance for the efficient adoption of 4.0 technologies in linear projects.

Internet of thing integration in green fintech for enhanced resource management in smart cities

The integration of the Internet of Things (IoT) and Green Fintech in smart cities represents a transformative approach to resource management, addressing the inefficiencies and sustainability challenges posed by rapid urbanization. IoT technologies, through interconnected sensors and real-time data processing, enable precise monitoring and management of urban resources such as energy, water, waste, and transportation. Concurrently, Green Fintech leverages financial innovations to support environmental sustainability, including sustainable investments, green banking, and carbon credit trading. This paper explores how the synergy of IoT and Green Fintech can enhance resource management in smart cities, offering dynamic pricing models, financial incentives for sustainable practices, and improved transparency. Case studies from Amsterdam and Singapore demonstrate the practical applications and benefits of this integration. Despite challenges related to data privacy, costs, and regulatory frameworks, the potential for IoT and Green Fintech to revolutionize urban resource management is substantial, promising increased efficiency, reduced environmental impact, and improved quality of life for city residents.

Research on Digital Greening Management of Smart Gardens in Chenggong District, Kunming City

With the rapid urbanization in China, city scales have expanded, bringing numerous management challenges. The continuous expansion of urban green spaces highlights the difficulty of adapting traditional urban garden management to complex conditions. The solution lies in using AI and big data for smart city management, specifically termed 'Smart Gardens'. This system integrates modern information technology into daily urban garden management. Through platform management systems, it enables comprehensive planning, construction, maintenance, supervision, and service of urban green spaces. This ensures real-time data updates, visualized management, and dynamic services. This digital transformation is essential. This paper analyzes the current issues and future development strategies of digitalized garden management in Chenggong District, Kunming. Through literature review, field research, and data collection, it aims to enhance the green space management in Chenggong District and other regions.

Simulation of low energy building thermal energy cycle in IoT smart city planning based on environmental sensors and deep learning

With the intensification of global climate change, the reduction of building energy consumption has become an important goal of smart city development. By optimizing the thermal energy circulation system, low energy buildings can not only reduce energy consumption, but also improve living comfort. In recent years, with the help of environmental sensors and deep learning technology, the intelligent management of building heat energy cycle has become a research hotspot. The research constructs an intelligent thermal energy circulation system that integrates multiple environmental sensors for real-time monitoring of indoor and outdoor temperature, humidity and other key environmental parameters. A deep learning algorithm is used to analyze the collected data to optimize the control strategy of the thermal energy cycle. Through simulation, the energy efficiency performance of the scheme under different climatic conditions and building types was evaluated. The experimental results show that the control strategy based on environmental sensors and deep learning can significantly improve the thermal energy utilization efficiency of low-energy buildings, and the average energy consumption is greatly reduced compared with the traditional management mode. The system shows good adaptability and stability under different climate conditions. Therefore, the application of environmental sensors and deep learning technology in the thermal energy cycle of low-energy buildings can effectively promote the energy efficiency management of smart cities.

Evaluating the impact of unmanned aerial vehicles (UAVs) on air quality management in smart cities: A comprehensive analysis of transportation-related pollution

Urban environments face significant air pollution challenges affecting health and sustainability. Using unmanned aerial vehicles for air quality monitoring offers a promising solution. This research aims to improve these vehicles' ability to identify pollution sources and develop strategies, with a focus on transportation-related pollution. Detailed air pollution tests revealed that pollutant concentrations surged during morning rush hours, especially in high-traffic coastal areas, while areas away from traffic had much lower levels. Evening tests showed that pollutants from daytime traffic had dispersed throughout the urban area. The consistent northward shift in pollutant concentrations underscored the link between traffic patterns and pollutant distribution. Quantitative analyses and the resulting air pollutant maps showed average increases in pollutant concentrations between morning and evening hours in high-traffic areas: 12.1% for NO2, 5.3% for CO, 9.8% for PM10, and 11.4% for PM2.5. In contrast, the increases in pollutant concentrations in less trafficked urban regions were 45.9%, 48.2%, 32.7%, and 29.5%, respectively. This demonstrates how air pollution originating from areas with heavy traffic impacts other regions through environmental and geographical factors. The findings underscore the need for comprehensive air quality management strategies at the city level, targeting emissions in high-traffic areas and adapting to temporal fluctuations in pollutant levels.

Experimental investigation on wireless integrated smart system for energy and water resource management in Indian smart cities

In the realm of India's smart cities, precise meter readings are essential for effectively managing domestic energy and water systems. Nevertheless, meter reading employing conventional techniques prove to be both costly and time-consuming, especially considering the vast user base and deficit of everyday consumption analysis. To cope with this challenge, the proposed solution introduces a novel unified wireless smart metering system for measuring energy and water usage. This prototype harnesses the smart, advanced metering technology to manage energy and water resources. The presence of data and remote control capabilities in smart distribution transformers offers distribution operators the chance to optimize system operation and control. The innovative system proposed utilizes smart metering technology and incorporates a distribution system self-healing mechanism against power outages, revolutionizing the way utilities manage water and energy consumption. The system not only offers real-time consumption statistics to the utility provider but also provides the flexibility to remotely control the system's turn-on and turn-off functions. The system effectively captures real-time data, transmits it via LoRa to Telegram application. The system automatically re-establishes remote connections in the event of disconnections caused by emergency conditions or non-payment of the bill, once the relevant issue has been resolved. Additionally, the system promptly notifies authorities on overload, over-temperature, and instances of electricity theft. The system proficiently generates bills based on the consumed data and seamlessly transmits the consumed units to the authorities. The innovative system proposed has proven its ability to communicate effectively, yielding results that align with the given circumstances.

Webbing 3D map from UAV images in Da Lat City, Lam Dong Province

Abstract Worldwide, big cities are accelerating to fulfill the establishment of Spatial Data Infrastructure (SDI), which is a key task towards smart city. The mainstay of SDI is to create an important foundation of data capacity to provide necessary and essential information related to the requirements of urban and smart city management. With the advantages of high accuracy, low cost, and flexibility in 3D data creation, UAV (Unmanned Aerial Vehicle) images have been widely applied for the purpose of digital transformation purposes. This article aims to firstly utilize the new opportunities of using UAVs in 3D mapping and sharing 3D map data through the WebGIS platform. Secondly, processing techniques will be described after collecting the UAV images to create orthomosaics, point clouds, and Digital Surface Model (DSM). A classification of ground points was conducted to create a Digital Terrain Model (DTM) in combining with DSM to form a calibrated surface numerical model (Canopy Height Model, CHM). The images map was then digitalized coupled with the object height extracted from the CHM model and assigning attributable data to each object. The results suggested that ArcGIS Online allows users to share information widely, targeting multiple users, and to reduce the cost of GIS software investment in building thematic classes for urban infrastructure management.

Enhancing Smart City Infrastructure: An Iot-Integrated System Approach for Real-Time Urban Management

Objectives: The primary objective of this study is to evaluate the effects of new information technologies on the workability, scalability, and reliability of urban installations. This study also aims to enhance smart city infrastructure by integrating various Internet of Things (IoT) systems, particularly for real-time smart city management. Methods : A complete experimental setup was established in a controlled urban environment encompassing numerous IoT technologies, such as environmental sensors, traffic management units, and public security systems, as well as data communication units. The Tensorflow framework, along with Python, was used to perform simulations and experiments. The system design was categorised into four key levels: data reception and transmission, computation, network and data analytics, and application. The proposed systems were subsequently tested in real-world environments, including zones frequently hosting urban events, to analyse key performance indicators. Findings : The results demonstrate significant improvements in urban management through IoT initiatives, including a 25% reduction in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) levels, leading to decreased respiratory hospital admissions and enhanced air quality and health benefits. Additionally, notable advancements were observed in noise control, energy efficiency, and emergency response. Specifically, the findings recorded a 10 dB decrease in nighttime noise, a 30% reduction in energy consumption for public lighting, and a 40% faster emergency response time. These outcomes highlight the broad impact of IoT on urban sustainability and safety. Novelty : This paper introduces a novel concept focused on the design perspective of IoT-connected systems to boost smart city infrastructure, with particular emphasis on the real-time management of urban environments. Keywords: Smart Cities, IoT Technologies, Urban Infrastructure, Real ­Time Urban Management, System Architecture

City lights revolution: Next-gen optical fibre for smart cities

In the rapidly evolving landscape of smart cities, the integration of advanced technologies is crucial for ensuring safety, optimizing traffic flow, and enhancing the urban living experience. Vehicle-to-vehicle (V2V) communication and visible light communication (VLC) have emerged as promising solutions to address these challenges. This paper explores the integration of V2V communication and VLC at smart pedestrian crosswalks to enhance pedestrian safety and traffic management in smart cities. It explores the impact of neighbouring vehicles on V2V-VLC performance and proposes novel methodologies to assess traffic density effects. Results indicate a significant chance of encountering nearby cars during rush hours, emphasizing the importance of these integrated systems for safety and mobility in urban environments. The outcomes show that the chance of running into extra cars in nearby lanes is independent of the particular lane and increases to 80% through rush hours, but falls to a lower amount than 20% through off-peak and initial morning hours.

Extended reality for citizen participation: A conceptual framework, systematic review and research agenda

This paper explores the integration of Extended Reality (XR) technologies, including Virtual Reality (VR) and Augmented Reality (AR), into citizen participation processes, a crucial yet challenging area in urban planning and smart city management. Despite the potential of XR, the existing literature remains scattered and often lacks practical application guidelines, hindering a full understanding of XR's potential to enhance democratic engagement. Through a systematic literature review, this study consolidates existing knowledge with an e-participation theoretical lens, spanning various disciplines, to provide a cohesive analysis of XR's role and potential. The study critically examines the empirical literature and suggests a conceptual framework consisting of several analysis themes: the contextual elements driving the use of XR by stakeholders, the characteristics of the technologies used, and outcomes of the use of XR. Leveraging this framework, we outline six future research directions, emphasizing the need to longitudinally study organizational implications of XR use beyond punctual user studies, extending the use cases to other political areas beyond urban planning, and investigating the creation of new realities as channels for effective and creative participation. The conceptual framework is also translated to a canvas for practitioners for the design of future XR-enabled participation initiatives.

GTDIM: Grid-based Two-stage Dynamic Incentive Mechanism for Mobile Crowd Sensing

Mobile Crowd Sensing (MCS) technology, as an emerging data collection paradigm, offers distinct advantages, particularly in applications like smart city management. However, existing researches inadequately address the comprehensive solution to the problem of reliable task allocation according to the requirements such as task budget, sensory data quality, and real-time data collection, especially under varying participant engagement in MCS systems. To bridge this gap, we propose the Grid-based Two-stage Dynamic Incentive Mechanism (GTDIM). In the first stage, the Candidate Participant Set (CPS) establishment phase, participants receive compensation for collecting sensory data when a sufficient number are available. When participants are insufficient, additional rewards inspired by the grid division of sensing areas are progressively offered to attract more participants. In the subsequent stage, utilizing the established CPS, participants are selected through a greedy algorithm based on the newly devised Participant Matching Index (PMI), which integrates various participant features. Extensive simulation results reveal the impact of PMI on participant selection. Numerical findings conclusively demonstrate GTDIM’s superior performance over baseline incentive mechanisms in terms of task assignment ratio, participant payment, and especially when dealing with larger sensing tasks.

The Role of Internet of Things (IoT) in Transforming Facilities Management in Smart Cities

The shift in urban foundation and administrations inside keen cities requests a comprehensive, innovative, and user-centric strategy that continues coordinating cutting-edge advances. This paper presents a spearheading approach that leverages the control of the Web of Things (IoT), Counterfeit Insights (AI), and Machine Learning (ML) to revolutionize the way shrewd city offices are overseen. At the centre of this strategy is the improvement of a centralized, cloud-based Offices Administration Stage (FMP) that serves as the spine for keen city operations. The FMP acts as an integrated hub, drawing together information from numerous IoT sensors, building automation systems, and other urban infrastructure. The policy also encourages user-centred design in light of improving occupant comfort, productivity, and well-being. Backed by IoT sensors and AI-driven building computerization frameworks, robust natural condition control and vitality utilization streamlining of the FMP can assist in conveying a predominant client involvement inside savvy city office.

A novel framework for waste management in smart city transformation with industry 4.0 technologies

Most of the developed and developing nations focus on the transformation of city livelihood through the development of smart cities. Waste management in smart cities is a critical concern for policymakers and smart city administration. The fourth industrial transformation (hereafter Industry 4.0) helps to improve waste management practices in smart cities to improve the sustainability of the city. This paper tries to develop a roadmap for industry 4.0 technologies relevant to managing waste in smart cities. This paper proposes a novel framework for analyzing the deployment enablers of Industry 4.0 technologies, implementation barriers, current best practices, and opportunities for digital technologies (EBPO) for smart city transformation. The proposed EBPO conceptual framework is analyzed in the context of studies conducted in developed and developing countries. The EBPO presents the unique linkages among the technological advancements for managing waste in smart cities. The present research is an early attempt to explore the relevance of industry 4.0 technologies in the smart waste management context for improving environmental sustainability. The keyword and theme mapping analysis proposes three major waste management clusters in smart cities. This paper provides insight for improving EBPO elements for smart cities’ waste management. The policy and theoretical implications are presented for smart city stakeholders. The paper also highlights the different dimensions of the EBPO framework.

The indicators included in ISO 37120 and performance measurement in smart cities

Purpose: Local government units are undergoing a shift in management practices driven by the implementation of the New Public Management (NPM) doctrine. One element of NPM is the measurement of performance in local government. As cities grow, the new concept of ‘smart cities’ is rapidly developing. However, it is difficult to determine how to measure the level of ‘smartness’. This paper focuses on measuring the performance of smart cities using the indicators included in ISO 37120. Methodology/approach: In the theoretical part, we overview the aims of performance measurement within NPM and the idea of smart cities. Meanwhile, the empirical part includes statistical data to analyse performance measurement using the indicators included in ISO 37120. Findings: Performance measurement is possible using the indicators included in ISO 37120. The indicators make it possible to compare the results of cities in various countries. Thus, standardising the indicators is the key to the development of smart city performance measurement. Originality/value: Our findings will be of value for future research about using ISO standards for the performance management of smart cities. Our research is also important for implementing NPM in local government. Research limitations: Due to a lack of data, the observation period is too short to make a more in-depth analysis.