Smart City Management Research Articles

Smart Dustbins: Real-Time Monitoring and Optimization for Waste Management in Smart Cities through IoT Devices

The Internet of Things (IoT) provides a technological foundation for the development of smart cities. The technology has yielded novel possibilities for urban governance. The IoT offers a practical and cost-effective means of gathering vast quantities of data, which can be applied across a range of industries with the aim of increasing production. One of the most pressing challenges currently facing the development of smart cities is the issue of waste collection beyond the municipal corporation. The IoT has the potential to streamline the waste collection process across a smart city. The proposed model is designed to facilitate more efficient location determination of smart dustbins through the use of IoT enabling technologies. The primary objective of the proposed models is to monitor the status of the smart dustbin. The smart dustbins are equipped with a GPS module, a GSM module, ultrasonic sensors, and a Liquid-Crystal Display (LCD). The primary function of the proposed model is to ascertain whether the smart dustbin is full, medium, or empty. In the event that the smart dustbin is full, the GPS location and status of the smart dustbin are automatically transmitted via SMS by GSM module. This SMS is sent to the relevant authorities of the Municipal Corporation, who are then responsible for collecting the waste from the smart dustbin. The SMS contains longitude and latitude values, which are used to identify the correct route map for the smart dustbins. Ultimately, the city can be transformed into a smart city by using IoT-enabled smart dustbins.

LLM Agents for Smart City Management: Enhancing Decision Support Through Multi-Agent AI Systems

This study investigates the implementation of LLM agents in smart city management, leveraging both the inherent language processing abilities of LLMs and the distributed problem solving capabilities of multi-agent systems for the improvement of urban decision making processes. A multi-agent system architecture combines LLMs with existing urban information systems to process complex queries and generate contextually relevant responses for urban planning and management. The research is focused on three main hypotheses testing: (1) LLM agents’ capability for effective routing and processing diverse urban queries, (2) the effectiveness of Retrieval-Augmented Generation (RAG) technology in improving response accuracy when working with local knowledge and regulations, and (3) the impact of integrating LLM agents with existing urban information systems. Our experimental results, based on a comprehensive validation dataset of 150 question–answer pairs, demonstrate significant improvements in decision support capabilities. The multi-agent system achieved pipeline selection accuracy of 94–99% across different models, while the integration of RAG technology improved response accuracy by 17% for strategic development queries and 55% for service accessibility questions. The combined use of document databases and service APIs resulted in the highest performance metrics (G-Eval scores of 0.68–0.74) compared to standalone LLM responses (0.30–0.38). Using St. Petersburg’s Digital Urban Platform as a testbed, we demonstrate the practical applicability of this approach to create integrated city management systems with support complex urban decision making processes. This research contributes to the growing field of AI-enhanced urban management by providing empirical evidence of LLM agents’ effectiveness in processing heterogeneous urban data and supporting strategic planning decisions. Our findings suggest that LLM-based multi-agent systems can significantly enhance the efficiency and accuracy of urban decision making while maintaining high relevance in responses.

AI-Based Energy Management and Prediction System for Smart Cities

In recent years, there has been an increasing interest in smart city concepts in response to a growing demand for more sustainable and efficient urban spaces. In this paper, an in-depth exploration of load forecasting in an innovative city framework is carried out through the building of a software application with a graphical user interface (GUI) and prediction using long short-term memory (LSTM). To fulfill their role in enabling intelligent cities, accurate load forecasting is needed to enable energy management, reduce peak demand, and allow for well-informed decisions and energy distribution optimization. Specifically, in this work, we consider the application of LSTM to load forecasting systems within the context of a smart city. Since LSTM has a high capability in capturing complicated temporal structures, it is indeed an efficient approach to load forecasting, especially considering long-term dependencies. We focus on the strength of LSTM in comparison with the traditional statistical analysis and other machine learning techniques, most notably, LSTM’s ability to handle nonlinear and dynamic load behavior typical in innovative city energy systems. The GUI is the system's front end where the user enters the data, and at this point, we have the city officials, energy managers, and the community people and get their customized load forecast. The discussion of the outcomes of the experiment with the cross-platform GUI application and the improved LSTM forecasting model is given. Based on the evaluation of the model in terms of accuracy and performance, the research is done in an accepted manner to predict the complex load pattern in a smart city environment by proving the efficiency of the LSTM model. The effectiveness of different decisions in various cases is evaluated to consider the influence on decision-making processes and energy optimization; various cases demonstrate how the proposed GUI application is helpful in facilitating better management of energy. This research will, therefore, endeavor to create knowledge in the area of energy management to foster the development of effective intelligent cities.

Energy in Smart Cities: Technological Trends and Prospects

Energy management in smart cities has gained particular significance in the context of climate change and the evolving geopolitical landscape. It has become a key element of sustainable urban development. In this context, energy management plays a central role in facilitating the growth of smart and sustainable cities. The aim of this article is to analyse existing scientific research related to energy in smart cities, identify technological trends, and highlight prospective directions for future studies in this field. The research involves a literature review based on the analysis of articles from the Scopus and Web of Science databases to identify and evaluate studies concerning energy in smart cities. The findings suggest that future research should focus on the development of smart energy grids, energy storage, the integration of renewable energy sources, as well as innovative technologies (e.g., Internet of Things, 5G/6G, artificial intelligence, blockchain, digital twins). This article emphasises the significance of technologies that can enhance energy efficiency in cities, contributing to their sustainable development. The recommended practical and policy directions highlight the development of smart grids as a cornerstone for adaptive energy management and the integration of renewable energy sources, underpinned by regulations encouraging collaboration between operators and consumers. Municipal policies should prioritise the adoption of advanced technologies, such as the IoT, AI, blockchain, digital twins, and energy storage systems, to improve forecasting and resource efficiency. Investments in zero-emission buildings, renewable-powered public transport, and green infrastructure are essential for enhancing energy efficiency and reducing emissions. Furthermore, community engagement and awareness campaigns should form an integral part of promoting sustainable energy practices aligned with broader development objectives.

Blockchain-Assisted Secure and Lightweight Authentication Scheme for Multi-Server Internet of Drones Environments

Unmanned aerial vehicles (UAVs) have seen widespread adoption across diverse sectors, including agriculture, logistics, surveillance, and disaster management, due to their capabilities for real-time data acquisition and autonomous operations. The integration of UAVs with Internet of Things (IoT) systems further amplifies their functionality, enabling sophisticated applications such as smart city management and environmental monitoring. In this context, blockchain technology plays a pivotal role by providing a decentralized, tamper-resistant ledger that facilitates secure data exchange between UAVs and connected devices. Its transparent and immutable characteristics mitigate the risk of a single point of failure, thereby enhancing data integrity and bolstering trust within UAV–IoT communication networks. However, the interconnected nature of these systems introduces significant security challenges, including unauthorized access, data breaches, and a variety of network-based attacks. These issues are further compounded by the limited computational capabilities of IoT devices and the inherent vulnerabilities of wireless communication channels. Recently, a lightweight mutual authentication scheme using blockchain was presented; however, our analysis identified several critical security flaws in these existing protocols, such as drone impersonation and session key disclosure. To address these vulnerabilities, we propose a secure and lightweight authentication scheme for multi-server UAV–IoT environments. The proposed protocol effectively mitigates emerging security threats while maintaining low computational and communication overhead. We validate the security of our scheme using formal methods, including the Real-Or-Random (RoR) model and BAN logic. Comparative performance evaluations demonstrate that our protocol enhances security while also achieving efficiency, making it well-suited for resource-constrained IoT applications.

A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System

The working environment of the coal mine boom-type roadheader is harsh with large blind areas and numerous safety hazards for operators. Traditional on-site or remote control methods do not meet the requirements for intelligent tunneling. This paper proposes a digital twin monitoring system of an EBZ-type roadheader based on mixed reality (MR). First, the system integrates a five-dimensional digital twin model to establish the boom-type roadheader digital twin monitoring system. Second, the Unity3D software (v2020.3.25f1c1) and the MR Hololens (v22621.1133 produced by Microsoft) are used to build a digital twin human–machine interaction platform, achieving bidirectional mapping and driving of cutting operation data. Third, a twin data exchange program is designed by employing the Winform framework and the C/S communication architecture, making use of the socket communication protocol to transmit and store the cutting model data within the system. Finally, a physical prototype of the boom-type roadheader is built, and a validation experiment of the monitoring system’s digital twin is conducted. The experimental results show that the average transmission error of the cutting model data of the twin monitoring system is below 0.757%, and the execution accuracy error is below 3.7%. This system can achieve bidirectional real-time mapping and control between the twins, which provides a new monitoring method for actual underground roadheader operations. It effectively eliminates the operator’s blind areas and improves the intelligence level of roadheader monitoring. Beyond mining, this methodology can be extended to the monitoring and control of other mining equipment, predictive maintenance in manufacturing, and infrastructure management in smart cities.

Leveraging 3D Printing for Resilient Disaster Management in Smart Cities

This work explores the transformative impact of 3D printing technology and disaster management within the context of smart cities. By evaluating various 3D printing technologies, such as desktop and large-scale printers, this research highlights their application in rapidly producing customized structures and essential supplies infrastructure components. Methods included the review of existing technologies, practical application in disasters scenarios. and the analysis of community engagement programs that enhance local preparedness and resilience through 3D printing. Case studies illustrate the significant benefits of integrating 3D printing technologies in disaster management. Findings indicate that while 3D printing offers rapid production and efficiency, disabilities such as high initial cost, regulatory issues, and the need for skilled operators must be addressed. This study concludes that with strategic collaboration and investment in the education and regulatory frameworks, 3D printing can significantly enhance urban resilience and sustainability, making it an invaluable tool for future smart cities. This research underscores the potential of 3D printing to significantly aid disaster management practices, fostering more adaptive and efficient urban environments.

How to manage a smart city brand image, taking into account residents’ perspective?

The objective of this paper is to assess the impact of brand values and their components on smart city brand image (SCBI) from the residents’ perspective. The authors want also to formulate recommendations regarding the management of a smart city brand image in cooperation with inhabitants. Today, smart city branding is an important aspect of public management because it can facilitate better engagement with citizens, leading to a more successful implementation of public authorities’ initiatives and consequently to smart city development. The research was conducted on 250 French respondents and a selfadministered questionnaire was the research tool. This study makes it possible to indicate values and their components creating the smart city’s brand image to properly manage a smart city brand in cooperation with inhabitants. We confirmed the positive impact of smart (SV), functional (FV), and emotional (EV) values on a smart city’s brand image from the residents’ perspective. Considering together the importance of these three categories of values for building the smart city’s image, it should be noticed that on the first place, there are the smart values followed by the functional and emotional ones. The smart city’s brand image is changing with residents’ time living in a city. The longer residents live in the city, the stronger particular values impact a smart city’s brand image. Taking into account the relational and participatory nature of the city’s brand management, citizens – who have lived in a city for a long time or since birth – can be more involved in the cities. Research results concerning French citizens can provide references for other countries. The results of the study contribute to both the theory of city branding and smart city management as a part of modern public management. From the managerial perspective, our findings can be useful for smart cities’ authorities in the management of the smart city brand.

Research on the performance of new assembled box ribbed floor structure based on BIM technology in smart city management platform

Research on the performance of new assembled box ribbed floor structure based on BIM technology in smart city management platform

Prospects for the development of cartography through the integration of SLAM technology with GIS technology

Abstract. With the continued development of Simultaneous Localization and Mapping (SLAM) and Geographic Information Systems (GIS) technologies, their application scenarios have become increasingly complex. Combining these technologies can significantly enhance operational efficiency in challenging environments. This paper presents an analysis of existing cases where SLAM and GIS technologies have been integrated, demonstrating that such a merger facilitates the consolidation and complementarity of spatial data. This integration allows robots or systems to simultaneously utilize the global information provided by GIS and the dynamic local data captured by SLAM for a more comprehensive and detailed environmental analysis, which is highly beneficial for the field of cartography. Further research has developed a series of operational procedures for integrating SLAM and GIS, utilizing MATLAB as a tool. This study also reviews several existing technical challenges, including real-time performance and computational capacity, environmental complexity and dynamic changes, and multi-scale data processing, and proposes potential solutions. The paper concludes by predicting that the integration of SLAM and GIS will play a crucial role in areas such as smart city management and disaster emergency response, indicating that this research area will become a hot topic in future cartographic technology.

Study of Access Control Techniques on the Blockchain-enabled Secure Data Sharing Scheme in Edge Computing

Background: The pervasive adoption of edge computing is reshaping real-time big data analysis, smart city management, intelligent transportation, and various other domains. Its appeal lies in its distributed nature, decentralization, low latency, mobile support, and spatial awareness. However, this ubiquity exposes data to security threats, jeopardizing privacy and integrity. Consequently, access control assumes paramount importance in securing data sharing within edge computing and blockchain technologies. Methods: This research addresses this critical issue by conducting a comprehensive study on access control techniques within the context of edge computing and blockchain for secure data sharing. Our methodology commences with an exhaustive review of relevant articles, aiming to identify and expound upon gaps in existing research. Subsequently, we perform a meticulous analysis of access control methods, mechanisms, and performance metrics, seeking to establish a holistic understanding of the landscape. Results: The culmination of this research effort is a multifaceted contribution. We distill insights from a diverse range of access control schemes, shedding light on their nuances and effectiveness. Our analysis extends to evaluating the performance metrics vital for ensuring robust access control. Through this research, we also pinpoint critical research gaps within traditional access control methods, creating a foundation for innovative approaches to address the evolving challenges within edge computing and blockchain environments. Conclusion: In conclusion, this research venture paves the way for secure data sharing in edge computing and blockchain by offering a thorough examination of access control. The findings from this study are anticipated to guide future developments in access control techniques and facilitate the evolution of secure, privacy-conscious, and efficient data sharing practices in the dynamic landscape of digital technology.

IoT-enabled smart cities towards green energy systems: a review

<span>Integration of internet of things (IoT) in smart city management to improve various functions and living standards due to increasing population growth has dramatically evolved ubiquitous and essential services at various stages of urbanization. Hence, smart cities need to be eco-friendly by improving various sectors like education, health, and transport to provide an urban and sustainable quality of life through solving complicated green energy networks, controlling toxic pollution risks, and public safety. Linking optimized green energy systems with the production and automation of advanced applications is crucial to compose implementation strategies for smart city services. This paper aims to conduct a review on eco-friendly plans and infrastructure of IoT-enabled smart cities by exploiting green energy approaches. This study performs critical observations, ideas, and analyses of recent research in the context of our mentioned research theme. This paper points out the technical and functional challenges of an optimal performance-based green IoT-enabled smart city infrastructure. In this sense, this study organizes observations of relevant initiatives, technologies, and experiences in IoT-enabled smart cities, as well as how to embed it with green energy. Moreover, it can provide significant directions to intellectuals and authorities to develop IoT-enabled smart city applications for prospective research.</span>

Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review

Numerous devices, including sensors, RF ID, and other types of smart devices, have been developed as a result of the Artificial Intelligence (AI), and Internet of Things (IoT) revolution. Urban areas can become smart by monitoring and collecting data about their surroundings through the deployment of technologies with powerful computational capabilities and those that are converted into intelligent things. Waste management is among the most significant issues in smart cities, a rise in metropolitan regions and faster increases in population are the main reasons. When it comes to gathering data about waste management, intelligent services can serve as the front line. Waste management with IoT support is a common example of a service offered by smart cities. Various duties, like gathering, processing, and use of waste in appropriate facilities, are included in waste management. The present study proposed an updated waste management system architecture design after reviewing existing artificial intelligence and IoT-based waste management systems and automation in smart cities. The proposed system architecture deals with the automation of municipality trash in smarter urban areas, using IoT technology and sending notification messages based on sensor data relating to the dustbin state, such as full or empty. The notifications are sent simultaneously to the municipality office and the waste carrier vehicle driver, so that waste can be emptied on time. The proposed system architecture represents a scalable and adaptable model for municipalities that aim to transform their waste collection processes and play a key step in minimizing municipality waste in smart cities. By deploying this proposed system architecture with smart sensors and IoT devices, municipalities can monitor waste levels to ensure that bins are emptied when it is necessary. This reduces the frequency of waste collection, lowers fuel consumption, and minimizes operational costs. The Route optimization algorithms further enhance efficiency by determining the most efficient paths for waste collection trucks, so they can reduce travel time and fuel emissions.

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.

Security Protection Systems and Technical Challenges in Smart City Construction

With the rapid development of information technology and the internet, smart cities have become a new trend in urban development globally. However, the security challenges faced by smart cities are becoming increasingly severe. The security protection of smart cities involves multiple issues such as cybersecurity, data security, and physical security. It must also balance efficiency and sustainability while safeguarding urban infrastructure and citizens' privacy. This paper analyzes the security protection needs and system construction of smart cities against the backdrop of their development, explores the technical challenges they face, and proposes corresponding strategies. Finally, the paper summarizes the future development direction of smart city security protection, emphasizing the importance of cross-departmental collaboration and technological innovation, offering valuable references for the future security management of 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.

Enhancing traffic flow and congestion management in smart cities utilizing SVM-based linear regression approach

With the development of smart cities, it is essential to monitor traffic flow and manage congestion effectively to ensure smooth movement for people and address their social and economic needs. As these needs continue to change, roadside infrastructure faces challenges in meeting the demands of citizens in smart cities. Traffic congestion is a major issue in road networks and occurs when the number of vehicles exceeds the capacity of the roads. Emerging technologies like Vehicular Networks (VN) and Support Vector Machine (SVM)-based linear regression offer promising solutions for vehicle-to-vehicle communication and managing autonomous roadside infrastructure. SVM-based linear regression is a well-known and effective method for addressing various issues related to roadside infrastructure, traffic management, data integration, analytics, and environmental monitoring. The main goal of using SVM-based linear regression in this research is to help citizens and city authorities make informed decisions and better understand and control traffic. This study demonstrates the application of SVM-based linear regression in integrating autonomous roadside infrastructure, achieving a high accuracy rate of 92% and reducing errors by 8%, showing a notable improvement compared to previous methods.

Efficient wastewater management for smart cities using Internet of Things (IoT) and blockchain technology

Access to clean and sufficient water is considered a basic right for humans and other living organisms sharing the environment. However, factors like rapid urban growth, climate change, population increase, and the overproduction of industrial and agricultural goods have contributed to water shortages and quality problems in many communities worldwide. Digital technologies, such as blockchain, machine learning, and the Internet of Things (IoT), offer opportunities to develop new solutions for creating smart and sustainable environments. In this paper, we introduce a unified water management system (IB-WMS) using IoT and blockchain technologies to monitor water quality, level, temperature, pressure, and consumption. This system is designed to be reliable, scalable, and transparent due to the integration of blockchain and IoT. Simulations indicate that the IB-WMS system achieves 89% efficiency, a 90% wastewater reuse rate, and a 95% water recycling rate.

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.