Smart City Development Research Articles

IntelliHome: The Automated Household Infrastructure

Abstract: The rapid advancement of technology in recent years has ushered in an era of unprecedented connectivity and automation in our daily lives. One of the most promising developments in this domain is the concept of IntelliHome – an integrated and intelligent household infrastructure that seamlessly combines smart devices, artificial intelligence, and data analytics to enhance convenience, efficiency, and sustainability within the home environment. This research paper delves into the multifaceted realm of IntelliHome, exploring its technological underpinnings, potential benefits, challenges, and societal implications. The paper begins by elucidating the fundamental components of an IntelliHome, encompassing a spectrum of smart devices such as thermostats, lighting systems, security systems, and appliances, all interconnected through the Internet of Things (IoT). It discusses the pivotal role of artificial intelligence in orchestrating these devices, enabling autonomous decision-making, predictive analytics, and adaptive customization to cater to the unique needs and preferences of each household. Furthermore, the research paper delves into the manifold benefits of an IntelliHome, including energy conservation, increased security, enhanced convenience, and improved quality of life. It examines how IntelliHome systems can reduce carbon footprints, optimize resource utilization, and contribute to the development of sustainable smart cities. Finally, the research paper contemplates the broader societal implications of IntelliHome technology. It explores how these systems may redefine the boundaries of work, leisure, and domesticity, and how they may influence family dynamics, social interactions, and community engagement. It also investigates the economic aspects and economic impacts.

Electronic Waste Problem in Developing Nations: Mismanagement, Health Implications, and Circular Economy Opportunities

Introduction: Electronic waste (e-waste) has emerged as a global concern due to the exponential growth in electronic consumption and inadequate disposal practices, specifically in developing countries. Among the various dimensions of this problem, the contamination of soil with e-waste has garnered significant attention, owing to its profound implications for environmental integrity and human health. Discussion: The soil contamination leads to diverse ecological repercussions, encompassing disruptions in the cycling of nutrients and biodiversity, as well as the potential for contaminants to accumulate in the food chain. Effective management strategies are imperative to address this multifaceted challenge, the implementation of governmental regulations, the adoption of sustainable e-waste recycling practices, and the raising of public awareness campaigns. The circular economy holds great potential for addressing the environmental and economic challenges. To build a more resilient and eco-friendly urban future, collaborative solutions are necessary to address infrastructural, policy, and awareness issues. In many circumstances, waste management is not primarily the government's duty. Non-governmental organizations (NGOs) and the business sector actively participate in trash management efforts through public education events and charity initiatives. This significant revolution has the potential to meaningfully advance smart city development through the use of technology-driven initiatives. Conclusion: By proactively addressing and reducing e-waste contamination in soil, we can mitigate the effects of environmental hazards and safeguard human health from the negative consequences associated with this burgeoning predicament.

Developing Smart City security through Upgrade Internet of Things Layers security

Developing Smart City security through Upgrade Internet of Things Layers security

A hybrid LBP-CNN with YOLO-v5-based fire and smoke detection model in various environmental conditions for environmental sustainability in smart city.

Smart, secure, and environmentally friendly smart cities are all the rage in urban planning. Several technologies, including the Internet of Things (IoT) and edge computing, are used to develop smart cities. Early and accurate fire detection in a Smart city is always desirable and motivates the research community to create a more efficient model. Deep learning models are widely used for fire detection in existing research, but they encounter several issues in typical climate environments, such as foggy and normal. The proposed model lends itself to IoT applications for authentic fire surveillance because of its minimal configuration load. A hybrid Local Binary Pattern Convolutional Neural Network (LBP-CNN) and YOLO-V5 model-based fire detection model for smart cities in the foggy scenario is presented in this research. Additionally, we recommend a two-part technique for extracting features to be applied to YOLO throughout this article. Using a transfer learning technique, the first portion of the proposed approach for extracting features retrieves standard features. The section part is for retrieval of additional valuable information related to the current activity using the LBP (Local Binary Pattern) protective layer and classifications layers. This research utilizes an online Kaggle fire and smoke dataset with 13950 normal and foggy images. The proposed hybrid model is premised on a two-cascaded YOLO model. In the initial cascade, smoke and fire are detected in the normal surrounding region, and the second cascade fire is detected with density in a foggy environment. In experimental analysis, the proposed model achieved a fire and smoke detection precision rate of 96.25% for a normal setting, 93.2% for a foggy environment, and a combined detection average precision rate of 94.59%. The proposed hybrid system outperformed existing models in terms of better precision and density detection for fire and smoke.

A novel dual-pooling attention module for UAV vehicle re-identification

Vehicle re-identification (Re-ID) involves identifying the same vehicle captured by other cameras, given a vehicle image. It plays a crucial role in the development of safe cities and smart cities. With the rapid growth and implementation of unmanned aerial vehicles (UAVs) technology, vehicle Re-ID in UAV aerial photography scenes has garnered significant attention from researchers. However, due to the high altitude of UAVs, the shooting angle of vehicle images sometimes approximates vertical, resulting in fewer local features for Re-ID. Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles. Specifically, the CpA module operates between the channels of the feature map and splices features by combining four pooling operations so that vehicle regions containing discriminative information are given greater attention. The SpA module uses the same pooling operations strategy to identify discriminative representations and merge vehicle features in image regions in a weighted manner. The feature information of both dimensions is finally fused and trained jointly using label smoothing cross-entropy loss and hard mining triplet loss, thus solving the problem of missing detail information due to the high height of UAV shots. The proposed method’s effectiveness is demonstrated through extensive experiments on the UAV-based vehicle datasets VeRi-UAV and VRU.

Smart Cities: AI and IoT Integration for Urban Sustainability

The rapid growth of cities is putting immense pressure on resources and infrastructure. In response, the concept of smart cities has emerged, aiming to leverage technology to improve the efficiency and sustainability of urban environments. Artificial intelligence (AI) and the Internet of Things (IoT) are two key technologies playing a critical role in the development of smart cities. This paper explores the integration of AI and IoT for urban sustainability. It discusses the various applications of AI and IoT in different aspects of urban life, such as transportation, energy, waste management, and public safety. The paper also highlights the challenges and opportunities associated with integrating AI and IoT in smart cities.

AQMon: A Fine-grained Air Quality Monitoring System Based on UAV Images for Smart Cities

Air quality monitoring is important to the green development of smart cities. Several technical challenges exist for intelligent, high-precision monitoring, such as computing overhead, area division, and monitoring granularity. In this article, we propose a fine-grained air quality monitoring system based on visual inspection analysis embedded in unmanned aerial vehicle (UAV), referred to as AQMon . This system employs a lightweight neural network to obtain an accurate estimate of atmospheric transmittance in visual information while reducing computation and transmission overhead. Considering that air quality is affected by multiple factors, we design a dynamic fitting approach to model the relationship between scattering coefficients and PM2.5 concentration in real time. The proposed system is evaluated using public datasets and the results show that AQMon outperforms four existing methods with a processing time of 13.8 ms.

Fit Islands: Designing a Multifunctional Virtual Urban Community to Promote Healthy Aging for Chinese Older Adults

Within the context of an aging global population, the demographic structure of emerging economies is undergoing a dramatic transformation. Emerging economies have a large population base and rapid economic development, but they are ill-prepared to deal with population aging. Limited resources force many older adults to face health issues such as chronic diseases and loss of physical independence, exacerbating the burden of traditional family and societal elderly care. Uncontrollable events such as the COVID-19 pandemic and regional conflicts have exacerbated the plight of older adults. Improving the quality of life and health of older adults has become a development priority in emerging economies in the face of a rapidly aging population. The development of smart cities has brought with it many available digital technologies, and the consequent development of smart aging offers endless possibilities for improving the quality of life and health of older people, making cities more inclusive of older people. Researchers from developed economies have attempted to address the health issues of older adults through a technology that combines physical exercise and digital technology called Exergame. However, existing projects are not suitable for older adults in emerging economies due to differences in national conditions. The aim of this project is therefore to propose a universal approach to designing a health-promoting Exergame system in the format of a virtual urban community to help emerging economies cope with aging populations, making cities more inclusive. To verify the feasibility of this approach, the authors designed an expandable Exergame called “Fit Islands”, using China as a case study. Based on the initial demonstration, the authors conducted functional tests. The result is that Fit Islands can meet the development objective of motivating Chinese older people to increase their physical activity, providing initial evidence of the feasibility of an Exergame system to promote healthy aging in emerging economies. The application of Fit Islands demonstrates the feasibility of the universal Exergame development method, which can, in principle, provide comprehensive and practical guidance for other countries.

Tensor-Based Viterbi Algorithms for Collaborative Cloud-Edge Cyber-Physical-Social Activity Prediction

With the rapid development and application of smart city, Cyber-Physical-Social Systems (CPSS) as its superset is becoming increasingly important, and attracts extensive attentions. For satisfying the smart requirements of CPSS design, a cloud-edge collaborative CPSS framework is first proposed in this paper. Then Coupled-Hidden-Markov-Model (CHMM) and tensor algebra are used to improve existing activity prediction methods for providing CPSS with more intelligent decision support. There are three key features (timing, periodicity and correlation) implied in CPSS data from multi-edge, which affects the accuracy of activity prediction. Thus, these features are synthetically integrated into improved Tensor-based CHMMs (T-CHMMs) to enhance the prediction accuracy. Based on the multi-edge CPSS data, three Tensor-based Viterbi Algorithms (TVA) are correspondingly proposed to solve the prediction problem for T-CHMMs. Compared with traditional matrix-based methods, the proposed TVA could more accurately compute the optimal hidden state sequences under given observation sequences. Finally, the comprehensive performances of proposed models and algorithms are validated on three open datasets by self-comparison and other-comparison. The experimental results show that the proposed methods is superior to the compared three classical methods in terms of F1 measure, average precision and average recall.

Sustainable Connectivity—Integration of Mobile Roaming, WiFi4EU and Smart City Concept in the European Union

This article takes a comprehensive look at the integration of mobile roaming, WiFi4EU and the smart city concept within the European Union in the context of sustainability. These initiatives form key elements of the digital development and transformation of European cities. Starting with a brief look at the functioning of the European Union’s internal market, the article briefly analyzes the objectives of these projects, highlighting their interplay and benefits for citizens. It focuses on the development of smart cities and the importance of digital connectivity in the process of building smart cities. It discusses the WiFi4EU initiative, which provides funding for free public WiFi networks and promotes digital inclusion. It also looks at the core pillars of smart cities, including digital connectivity, efficient transport, environmental protection, innovation and citizen participation. The article discusses the challenges associated with this integration, such as ensuring interoperability of different technological solutions and data privacy. It also highlights the importance of cooperation between city authorities, local communities and European institutions to achieve successful digital urban development. The research emphasizes the economic sustainability implications of these integrated technologies, considering the potential for innovation, job creation and economic growth within the digital and tech sectors. The main method used in the writing process was the analysis method, which was complemented by the comparison and synthesis methods. The final discussion assesses the benefits and challenges that this integration brings for the development of cities and the improvement of the quality of life of citizens. By critically examining the convergence of mobile roaming, WiFi4EU and smart cities in the European Union, this study aims to provide insights into the transformative potential of sustainable connectivity. The findings contribute to ongoing discussions on urban development strategies, emphasizing the need for a holistic approach that addresses both technological advancements and the imperative of sustainable practices for the benefit of current and future generations.

Možnosti využitia Internet of Things zariadení v doprave v podmienkach miest SR

Current development of information technology offers a wide number of possible data collection approaches, but it is often limited by economic or legal barriers. Cities in Slovakia have a wide range of possible data sources available concerning their inhabitants, services and properties. However, targeted data collection is often lacking when it comes to transport. Larger cities face problems related to traffic congestion and safety, where data of sufficient quality is essential for managing traffic flows and monitoring the condition of roads and parking. This paper focuses on analyzing experience from other countries that use IoT devices to collect data in the field of transport. The goal is to provide inspiration and guidelines for the effective collection of relevant data in Slovak cities. The introduction of these technologies could contribute to better traffic management, increased traffic safety and more efficient use of parking capacities, which would significantly contribute to the development of smart and sustainable cities in Slovakia.

An efficient wastewater collection model for groundwater resource protection in smart cities

Efficient wastewater collection is a critical concern in the development of smart cities, where sustainable urban infrastructure is paramount. In the context of smart city wastewater optimization, this study highlights the need for a comprehensive approach that considers both surface water and groundwater dynamics. Existing methods often fall short in addressing groundwater-related concerns, with limitations in accounting for potential contamination and aquifer vulnerability. The proposed method integrates a nuanced understanding of groundwater aspects, providing a more robust and sustainable solution for urban water resource management. This research introduces a new approach that harnesses the power of Divide-And-Conquer (DAC) and Variable Neighborhood Search (VNS) to optimize wastewater collection systems in smart cities. The DAC strategy divides the city into manageable zones, simplifying the optimization problem for parallel processing in large-scale urban environments. VNS fine-tunes wastewater collection networks within each zone by exploring diverse neighborhood solutions. The DAC and VNS offers several benefits, including improved efficiency in wastewater transport, reduced energy consumption, and minimized environmental impact. Zoning the urban landscape is a strategic approach to optimize wastewater collection in smart cities. It involves dividing the city into manageable zones based on specific criteria such as geographical features, population density, and infrastructure layout. Each zone is treated as an independent unit for focused analysis and optimization. The optimization process employs a scoring function that combines weighted criteria, yielding a composite score for each geographical location. Applying a threshold or clustering algorithm group's locations into zones, facilitating tailored wastewater management strategies. By optimizing collection routes and infrastructure design, this approach assists smart cities in achieving sustainability goals while efficiently managing wastewater. The study showcases the potential of DAC and VNS as a robust and adaptable solution for wastewater collection in smart cities, contributing to the development of greener, more resource-efficient urban areas.

Environmental Assessment of Green Areas in the City of Campinas, SP: A Study of Sustainability and Biodiversity

Objective: To present a diagnosis of the environmental quality of three (3) urban green areas in Campinas, SP, so that the impact caused by urban pressure arising from the growth of cities can be evaluated. Theoretical Benchmark: Research has been conducted on sustainable development goals, smart cities and urban green areas, and can understand the importance of urban green areas in the sustainable development of smart cities. Method: Application of the LAP (Landscape Assessment Protocol) methodology, where, through Landscape Assessment using 15 metrics, the LAP CI index is obtained. Results and conclusion: The areas evaluated have LAP CI indices ranging from moderate with index 61 for the Taquaral Lagoon, 81 being good for the German Forest and 95, excellent for the Santa Geneva Forest. The methodology used can be applied to different types of areas, reflecting their environmental condition through the urban pressure suffered and having an inverse relationship between the number of visitors to the parks and the index found. Implications of the research: The research demonstrated that the methodology can be applied by different evaluators bringing different views about the environmental quality of urban green areas, precisely for the subjetive character of the evaluator in scoring the metrics. This environmental classification can help decision-makers create urban green area recovery programs. Originality / value: The research showed that the evaluation of urban green areas is an important ally in the search for sustainability in cities in order to provide better quality of life to the population.

International Perception and Local Pride in Smart City Development: The Case of Hong Kong

Abstract Local citizens’ perceptions of their own cities are often neglected when assessing cities’ performance. Using the perceived international image and collective pride in Hong Kong as the entry point, this article aims to discover the relationship between citizens’ trust in public authorities and urban technologies. Four angles of investigation are developed: the impact of cities’ promotion of their international image on local pride; the attitudinal and demographic characteristics of proud citizens; the linkage of local pride with public trust, and digital trust. This article uses data from a Hong Kong-based territory-wide survey in 2021 to conduct the analysis. The result suggests that public trust is an elemental factor having a positive relationship with collective pride and digital trust in Hong Kong. This article also identifies the group of citizens with the least pride. Regaining the citizen's trust is the best remedy for facilitating smart city development.

Exploring the nexus between the barriers and drivers for sustainable smart cities in developing countries: The case of Nigeria

AbstractAmidst the rapid urbanisation and increasing calls for sustainable development, this study examines the key drivers and barriers influencing sustainable smart city initiatives in Nigeria. By employing a quantitative approach, the research amalgamates insights from diverse professionals, integrating descriptive statistics, exploratory factor analysis, and Spearman rank correlation analysis to illuminate the intricate landscape of sustainable smart city development within the Nigerian context. The findings underscore the interconnected nature of various factors, underscoring the imperative of an all‐encompassing approach that synergistically incorporates infrastructure integration, environmental sustainability, efficient governance, social inclusivity, and economic innovation. Furthermore, identifying specific barriers, including challenges related to integrated urban transformation, socioeconomic equity, and governance and infrastructure, highlights the critical need for precise interventions to surmount these obstacles. The implications and recommendations derived from this study emphasise the pivotal role of collaborative endeavours among diverse stakeholders, accentuating the significance of inclusive decision‐making processes, sustainable environmental practices, and equitable economic expansion. The insights gleaned from this research serve as an invaluable resource for policymakers and urban planners, providing a robust groundwork for informed policy formulation and strategic planning to guide sustainable smart city development in Nigeria and other developing nations.

Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review

As Smart Cities development grows, deploying advanced technologies, such as the Internet of Things (IoT), Cyber–Physical Systems, and particularly, Artificial Intelligence (AI), becomes imperative for efficiently managing energy resources. These technologies serve to coalesce elements of the energy life cycle. By integrating smart infrastructures, including renewable energy, electric vehicles, and smart grids, AI emerges as a keystone, improving various urban processes. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and the Scopus database, this study meticulously reviews the existing literature, focusing on AI technologies in four principal energy domains: generation, transmission, distribution, and consumption. Additionally, this paper shows the technological gaps when AI is implemented in Smart Cities. A total of 122 peer-reviewed articles are analyzed, and the findings indicate that AI technologies have led to remarkable advancements in each domain. For example, AI algorithms have been employed in energy generation to optimize resource allocation and predictive maintenance, especially in renewable energy. The role of AI in anomaly detection and grid stabilization is significant in transmission and distribution. Therefore, the review outlines trends, high-impact articles, and emerging keyword clusters, offering a comprehensive analytical lens through which the multifaceted applications of AI in Smart City energy sectors can be evaluated. The objective is to provide an extensive analytical framework that outlines the AI techniques currently deployed and elucidates their connected implications for sustainable development in urban energy. This synthesis is aimed at policymakers, urban planners, and researchers interested in leveraging the transformative potential of AI to advance the sustainability and efficiency of Smart City initiatives in the energy sector.

The recovery plans at the time of COVID-19 foster the journey toward smart city development and sustainability: a narrative review

The recovery plans at the time of COVID-19 foster the journey toward smart city development and sustainability: a narrative review

How do smart city pilots affect the ESG performance of manufacturing firms? evidence from China

Introduction: The existing literature extensively covers factors influencing environmental, social, and governance (ESG) performance. However, there’s a lack of studies exploring the relationship between smart cities and ESG performance. This gap is surprising, particularly considering the significant role that governments in developing countries play in leading smart city initiatives.Methods: As the impact of smart city pilot (SCP) policies, initiated by governments, on the ESG performance of manufacturing firms lacks verification, our study leverages SCP as a quasi-experiment to examine the effects of smart city development on this performance. Data was collected from 2,229 listed manufacturing firms in China from 2009 to 2020.Results: Our findings indicate a substantial increase in ESG performance among manufacturing firms due to SCP implementation. Heterogeneity analysis reveals that the positive influence of SCP is primarily advantageous for non-state-owned enterprises. Geographically, SCP significantly enhances corporate ESG performance in eastern and central China, with more muted effects observed in the western region. Two key mechanisms driving the enhancement of corporate ESG performance due to SCP are the promotion of green innovation investment and the improvement of internal control quality.Discussion: This paper provides valuable insights for policymakers and business leaders in China and other emerging economies. It guides them in fortifying ESG performance, thereby facilitating sustainable corporate growth.

The Importance of Clean Energy and Technology in the Development of Smart Cities

In today's global context, reducing CO2 emissions and mitigating environmental impacts has become an important issue, leading to the inclusion of smart cities among the top priorities of every country. This article aims to provide solutions for creating appropriate policies and overcoming obstacles in this field, with a focus on the importance of clean energy in the development of smart cities. Specifically, by focusing on three important sectors, namely Energy, Transportation, and Buildings, the aim is to help create efficient roadmaps for developing smart cities. To achieve this goal, successful policies and strategies implemented in successful cities will be examined to overcome the barriers in these sectors and achieve smart city status. The article emphasizes the importance of local government and stakeholder collaboration. This collaboration plays a critical role in creating appropriate policies for increasing the use of clean energy in different sectors, as the ideas of politicians and energy experts cannot be implemented without the support and participation of local governments. Therefore, the cooperation and support of local governments and stakeholders are of great importance for the development of smart cities. In short, the article emphasizes the importance of clean energy in the development of smart cities, provides recommendations for identifying appropriate policies and overcoming barriers, and highlights the critical role of local government and stakeholder collaboration. Therefore, this collaboration can be considered as a successful step towards the development of smart cities.

Ageing in a smart city poses concerns on sustainability from a model perspective

Ageing is not formally in the core pillars of Smart City framework under the Sustainable Development Goals of United Nations. Its undermined significance is usually detected under some of these pillars connected with each other. Somehow not many measurements on ageing to positively or negatively influence smart city development are well-defined or in some sense provide solid insights to policy makers. This article tries to guide readers where ageing considerations should be recommended on any stages of smart city development and how research studies should look for or measure these performances and interconnections with no overlooking on ageing aspects.