Role In Smart Cities Research Articles

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.

Multi-step trend aware graph neural network for traffic flow forecasting

Traffic flow prediction plays an important role in smart cities. Although many neural network models already existed that can predict traffic flow, in the face of complex spatio-temporal data, these models still have some shortcomings. Firstly, they although take into account local spatio-temporal relations, ignore global information, leading to inability to capture global trend. Secondly, most models although construct spatio-temporal graphs for convolution, ignore the dynamic characteristics of spatio-temporal graphs, leading to the inability to capture local fluctuation. Finally, the current popular models need to take a lot of training time to obtain better prediction results, resulting in higher computing cost. To this end, we propose a new model: Multi-Step Trend Aware Graph Neural Network (MSTAGNN), which considers the influence of global spatio-temporal information and captures the dynamic characteristics of spatio-temporal graph. It can not only accurately capture local fluctuation, but also extract global trend and dramatically reduce computing cost. The experimental results showed that our proposed model achieved optimal results compared to baseline. Among them, mean absolute error (MAE) was reduced by 6.25% and the total training time was reduced by 79% on the PEMSD8 dataset. The source codes are available at: https://github.com/Vitalitypi/MSTAGNN.

The role of smart cities in sustainable development: empirical evidence from Türkiye

PurposeThis study aims to empirically examine the effects of smart cities on sustainable development for the period 1990–2019 for Türkiye.Design/methodology/approachThe relationship between smart cities and sustainable development was analyzed with the help of the ARDL Bounds Test. In addition, the consistency of the model was tested with the FMOLS estimator. The indicators of the smart city were selected following the literature to represent smart cities, and the author created the smart city index. The study used other variables thought to impact sustainable development as secondary data.FindingsThe results show that smart cities positively and significantly impact sustainable development in Turkiye in both models during the sampling period. In addition, while real GDP, population density, and financial development variables positively affect sustainable development, population density has a negative effect on sustainable development, according to the results obtained from FMOLS estimators.Originality/valueThe first novelty of this study is the creation of the smart city index. The second novelty is that there are almost no studies on the effects of smart cities on sustainable development, especially for Türkiye.

Data-Driven Internet of Things: Role in Smart Cities

“Smart city” is a place where cutting-edge technology optimizes resource utilization, progress facilitation, environmental friendliness, and socially inclusive neighborhoods are established. Technology, economics, and social progress were discussed. Since 2000, political actors, business, administration, and urban planning have utilized the notion to define urban technological advancements and developments. The smart city is a planned, technologically advanced "city". In the face of economic, social, and political defeats in many nations after 2000, it uses all digital technology. Air pollution, overcrowding, and urban population growth are the main concerns. Along with technical advancements that make cities more habitable, financial constraints constantly present a challenge. Existing infrastructural inefficiency. Machine learning technology is essential to the IoT because it manages large amounts of data. This paper will explain how healthcare uses AI with IoT and WSN. This research underpins smart city Internet of Things duties and examines the IoT's impact on health facilities.

Anomaly detection in Smart Traffic Light system using blockchain: Securing through proof of stake and machine learning

<p>The Smart Traffic Light system plays a crucial role in smart cities, contributing significantly to enhancing the overall urban living standards, supporting sustainable practices, assuring public safety, and optimizing operational efficiency. Smart lighting systems leverage advanced technology such as the Internet of Things (IoT), data analytics, and networking to construct a complex and flexible lighting infrastructure. Anomalies within the context of a Smart Light system’s data pertain to patterns, events, or behaviors that are unexpected or uncommon, displaying a considerable deviation from the system’s typical operational state. The distributed and tamper-resistant ledger of blockchain technology renders it well-suited for maintaining an open and immutable record of data and events pertaining to smart lighting systems. The integration of blockchain technology and anomaly detection techniques enables the establishment of a resilient and reliable smart lighting system. The primary objective of this study is to investigate the application of the Isolation Forest algorithm for anomaly detection and its enhancement through the implementation of Proof of Stake for enhanced security measures. The Isolation Forest algorithm is utilized for anomaly prediction and evaluation of accuracy and precision. This is achieved by employing synthetic ground truth labels. Subsequently, the non-anomalous data is incorporated into the system as blocks using blockchain technology. The experimentation involves the use of synthetic numerical data derived from a dataset accessible on Kaggle. This process is conducted in two distinct phases. The first phase focuses on the analysis of synthetic numerical data, specifically targeting the identification of anomalies, prior to the implementation of blockchain technology. In the second phase, the same technique is applied to the synthetic numerical data following the integration of blockchain technology. Various machine learning (ML) models were utilized to analyze the data, resulting in improved accuracy from 48% to 94%, as evidenced by the validation of the obtained results. The empirical results indicate that the utilization of blockchain technology in data processing leads to an improvement in accuracy. Furthermore, the random forest algorithm exhibits robust performance when integrated with blockchain technology.</p>

The role of smart cities in managing the COVID-19 outbreak in India

The role of smart cities in managing the COVID-19 outbreak in India

Evaluating the Performance of Pre-Trained Convolutional Neural Network for Audio Classification on Embedded Systems for Anomaly Detection in Smart Cities.

Environmental Sound Recognition (ESR) plays a crucial role in smart cities by accurately categorizing audio using well-trained Machine Learning (ML) classifiers. This application is particularly valuable for cities that analyzed environmental sounds to gain insight and data. However, deploying deep learning (DL) models on resource-constrained embedded devices, such as Raspberry Pi (RPi) or Tensor Processing Units (TPUs), poses challenges. In this work, an evaluation of an existing pre-trained model for deployment on Raspberry Pi (RPi) and TPU platforms other than a laptop is proposed. We explored the impact of the retraining parameters and compared the sound classification performance across three datasets: ESC-10, BDLib, and Urban Sound. Our results demonstrate the effectiveness of the pre-trained model for transfer learning in embedded systems. On laptops, the accuracy rates reached 96.6% for ESC-10, 100% for BDLib, and 99% for Urban Sound. On RPi, the accuracy rates were 96.4% for ESC-10, 100% for BDLib, and 95.3% for Urban Sound, while on RPi with Coral TPU, the rates were 95.7% for ESC-10, 100% for BDLib and 95.4% for the Urban Sound. Utilizing pre-trained models reduces the computational requirements, enabling faster inference. Leveraging pre-trained models in embedded systems accelerates the development, deployment, and performance of various real-time applications.

Internet of Cross-Chains: Model-Driven Cross-Chain as a Service Platform for the Internet of Everything in Smart City

Modern consumer electronics included consumer robots, which are made by heterogeneous Internet of Things, and Internet of robotics things are integral members of the Internet of Everything (IoE . IoE plays an incumbent role in smart cities and generates big data. Moreover, smart city IoE maintains various levels of security provisions. However, those are still vulnerable to security and privacy breaches. Nonetheless, mobility increases the risk of security and privacy breaches. As a result, in the internet era, providing seamless security and privacy for inter and intra communication of heterogeneous things is a significant challenge. Furthermore, blockchain is one of the established communication techniques that ensure both security and privacy. On the other hand, blockchain inherits trivial constraints with respect to scalability, latency, and interoperability. Cross-chain is a blockchain concept that aims to alleviate the aforementioned concerns. Therefore, we proposed a multifactor aware (privacy, security, interoperability, and scalability model-driven reference architecture for the Internet of cross-chains, also known as cross-chain as a service platform.

Demographic Bias in Low-Resolution Deep Face Recognition in the Wild

Face biometrics play a primary role in smart cities, from consumer- to organizational-level applications. This class of technologies has been recently shown to emphasize performance disparities across gender and ethnic groups. Prior work on demographic bias in deep face recognition has tended to focus exclusively on high-resolution images, leaving out low-resolution images captured, for example, by a certain surveillance camera at a distance. Notable reasons for this focus include the lack of low-resolution face image data sets that report user's demographic attributes. Therefore, demographic bias in low-resolution deep face recognition in the wild still remains under-explored. In this paper, we propose a framework for exploring demographic disparities in low-resolution face recognition at a distance. To this end, we devised a deep generative approach to turn high-resolution face images to their low-resolution counterpart. We then trained state-of-the-art face recognition models on different combinations of high- and low-resolution images. Finally, we assessed the model demographic disparities on artificially degraded images from five data sets. Our results show that face images artificially degraded through our generative approach are more realistic than those obtained with other statistical changes. Key disparities across gender and ethnic groups exist and urge timely interventions. Source code: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://cutt.ly/FChLfOC</uri>

The role of smart cities in countering health threats: A review of practices

Modern cities face multiple global challenges. Despite the diversity of threats to sustainable development, public health is a priority. The authors will show, by analysing urban practices, how the integration of digital technologies, monitoring systems and data analysis can help to address the issues of disease control, environmental monitoring, access to urban medical infrastructure and health services. The article emphasises that the success of smart cities in public health is highly dependent on community engagement. Smart cities offer a set of innovative, flexible and scalable solutions that can significantly improve the situation in this area. The application of big data technologies, artificial intelligence, sensor networks and other advanced tools allows for creation of efficient mechanisms for monitoring, prediction and counteracting threats to public health. The provided conclusions and recommendations can serve as a basis for the development of sustainable development strategies for smart cities aimed at long-term improvement of people’s quality of life.

Digital transformation in transport infrastructure energy efficiency: Smart cities and sustainable mobility

In the context of today's rapidly changing urban environment, digital transformation and the concept of smart cities are becoming important components for sustainable mobility and efficient transport infrastructure. This article is a research study on the trends and prospects of the digital transformation of Russia's transport complex, as well as the role of smart cities in ensuring sustainable mobility in this context. It analyzes key aspects of the integration of digital technologies into transport infrastructure, including the use of the Internet of Things and autonomous vehicles. Advantages and risks of digitalization are discussed, including issues of cybersecurity and data privacy. The paper also highlights the importance of developing joint strategies and regulatory frameworks for smart-city development and sustainable mobility. This research suggests an important assumption that modern cities can achieve outstanding results in sustainable mobility and transport infrastructure efficiency through digital integration and smart city development.

Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging

The rise of the intelligent, local charging facilitation and environmentally friendly aspects of electric vehicles (EVs) has grabbed the attention of many end-users. However, there are still numerous challenges faced by researchers trying to put EVs into competition with internal combustion engine vehicles (ICEVs). The major challenge in EVs is quick recharging and the selection of an optimal charging station. In this paper, we present the most recent research on EV charging management systems and their role in smart cities. EV charging can be done either in parking mode or on-the-move mode. This review work is novel due to many factors, such as that it focuses on discussing centralized and distributed charging management techniques supported by a communication framework for the selection of an appropriate charging station (CS). Similarly, the selection of CS is evaluated on the basis of battery charging as well as battery swapping services. This review also covered plug-in charging technologies including residential, public and ultra-fast charging technologies and also discusses the major components and architecture of EVs involved in charging. In a comprehensive and detailed manner, the applications and challenges in different charging modes, CS selection, and future work have been discussed. This is the first attempt of its kind, we did not find a survey on the charging hierarchy of EVs, their architecture, or their applications in smart cities.

IAMPDNet: Instance-aware and Multi-part Decoupled Network for Joint Detection and Embedding

&lt;p&gt;Video surveillance applications play an important role in smart cities. Recently, intelligent video surveillance methods have been widely investigated to address large-scale video data, among which multi-object tracking (MOT) is the most popular method, which aims to track every object appearing in the video for monitoring. MOT is essential for deep intelligent perception. Considering inference speed, joint detection and embedding (JDE) has become a new paradigm for MOT. JDE is to obtain detection results and object features through one forward propagation. However, most JDE models lack instance awareness ability and multi-part feature extraction ability, which may lead to the lack of discrimination of extracted instance features. To address these problems, in this paper we propose an instance-aware and multi-part decoupled network (IAMPDNet), which can perceive all instances in the environment and extract multi-part features from the instances. Specifically, our IAMPDNet consists of three key modules: a complementary attention module used to perceive all instances in the environment, a feature extraction module used to decouple multi-part features from the instances, and an adaptive aggregation module used to fuse multi-level features of instances. Extensive experiments on multiple MOT benchmarks demonstrate that our IAMPDNet achieves higher tracking accuracy and lower identity switches against recent MOT methods.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

FARDA: A fog-based anonymous reward data aggregation security scheme in smart buildings

Nowadays the Internet of Things (IoT) for smart city is prevalent around the world, named as City IoT. Since building is the key facility in the city, smart building (SB) plays an important role in smart city. With the development of new AI technology and expansion of clients’ demand, smart buildings spread gradually in cities. Thereby, amounts of edge devices are accessed to the City IoT leading to the computation resources explosion and security issues in the smart environment. Here, we study the security of the data sensing in smart IoT and propose a novel data aggregation scheme, named Fog-based Anonymous Reward Data Aggregation scheme (later referred to as FARDA), to preserve the user privacy for developing a sustainable City IoT system. Specifically, this proposed scheme merges the AI based blockchain for enhancing the security on the edge devices and fog computing for the energy sustainability. More, FADRA attracts mobile sensing users and fog nodes by an anonymous reward mechanism in the blockchain framework. The framework leverages the provable Monero’s blockchain to endow two privacy requirements: (i) unlinkability between the payer’s address and the payee’s address and (ii) the non-traceability of the transaction sources. Moreover, two PKCs in the blockchain guarantee the benefits of sensing data participants theoretically. We verify the security properties of FARDA in theory and evaluate the efficiency for data sensing in smart environments on smartphones by experiments. Our study paves a way for broad researches on sustainable IoT in smart scenarios.

‘Built from the internet up’: assessing citizen participation in smart city planning through the case study of Quayside, Toronto

Citizen participation in smart cities has come under ever more scrutiny in recent years. Whilst smart city projects across the world have proclaimed themselves as citizen-centric, scholars have found that these claims are still framed within a neoliberal, post-political conception of citizenship, whereby citizens are afforded little agency. In evaluating such projects and in aid of developing a better understanding of the citizen’s role in smart cities, scholars have developed various heuristics. This paper aims to further both empirical and theoretical developments in the field to evaluate citizen participation in Quayside, Toronto’s first smart city neighbourhood, using Cardullo and Kitchin’s Scaffold of Smart Citizen Participation. A document analysis of seventeen citizen engagement summary reports and advertisements, corresponding to eight citizen engagement initiatives, has revealed that the quality of citizen participation varied substantially according to individual initiatives in Quayside. It was also discovered that Cardullo and Kitchin’s scaffold was ineffective at capturing the complexity of citizen engagement in smart city planning. In light of this, a new heuristic which assesses the post-political spaces of citizen engagement has been developed. This heuristic can provide a productive foundation for further research in the field.

Role of Artificial Intelligence in Smart Cities for Information Gathering and Dissemination (A Review)

The rise in urban population and technological innovation has resulted in climatic, technological, and economic changes that can severely impact the value of life in cities. In reaction to the changes, the idea of smart cities has been developed, which refers to the novel application of information communication technology to minimize the severe impacts on the cities and their occupants. Artificial Intelligence is among the technologies being adopted to develop smart cities. Research shows that Artificial Intelligence (AI) is improving rapidly, and it plays a substantial role in helping intelligent city-wide systems in various domains. Thus, it is vital to decide the modern research advances to realize the role played by Artificial Intelligence in smart cities for intelligence gathering and dissemination. Also, there is a need to analyze the relationship between Artificial Intelligence and smart cities scientifically. To identify this relationship, this research paper will focus on the role of Artificial Intelligence, its applications, and the challenges associated with concepts and technologies identified as Artificial Intelligence in smart cities. Therefore, this research paper attempts to give insights into Artificial Intelligence's role in smart cities for intelligence gathering and dissemination.

Social Influence as a Major Factor in Smart Meters’ Acceptance: Findings from Brazil

Smart meters are IoT devices that play a central role in smart cities. However, consumers' acceptance of these smart meters is crucial for the smart meter'’s implementation and sustainable use of resources. Despite the need to further understand what influences the acceptance of smart meters, only a few studies tackle this issue, none of them in South America. This article seeks to investigate which factors influence the acceptance of smart meters by consumers in Brazil to provide insights into public policies that help in smart meter implementation projects. This work features a survey (N = 144) in the city of Florianópolis, the capital of Santa Catarina, that comprises items derived from research and technology acceptance models, mainly the UTAUT2 model. PLS-SEM method is used to assess the influence relations on acceptance and the moderation of these relations by gender, age, and years of formal education of respondents. Social Influence is estimated as one of the main factors for the acceptance of smart meters in Florianópolis, something not foreseen in previous research on this topic. Another surprising result is the lack of influence from Performance Expectancy. This study presents a detailed acceptance model based on the survey carried out, and specific strategies for public policies are given based on the constructed acceptance model.

The role of smart cities in supporting smart tourism

The role of smart cities in supporting smart tourism

The Role of Smart Cities during the COVID-19 Pandemic – the Example of New York City

This paper investigates how smart cities responded to the COVID-19 pandemic-related issues based on the example of New York City (the case study formula). The first part of the research constitutes a background to the study. The specific challenges and city responses that the pandemic introduced to urban centres are indicated. The second part contains research on various definitions of the smart city concept. Since there is no official characteristic of such an urban centre, the commonly accepted attributes were indicated in the study. The first and second parts are based on desk research. Finally, in the last phase of the research, a case study analysis of New York City was conducted to present manifestations of previously indicated attributes of a smart city concept. The actions taken by the city authorities and other city actors to address the challenges that were both common for cities and specific ones (that applied to New York City) were investigated.

Magnetic Sensor-Based Multi-Vehicle Data Association

Sensors have been playing an increasingly important role in smart cities. Using small roadside magnetic sensors provides a cost-efficient method for monitoring vehicle traffic. However, there are significant challenges associated with vehicle data misalignment due to the timing-offsets between sensors and missed or increased data because of vehicle lane-changing. In this paper, we propose a novel traffic information acquisition and vehicle state estimation scheme using multiple road magnetic sensors. To efficiently solve the multi-sensor registration problem in the presence of timing-offset, we develop a linear discrimination analysis method to achieve vehicle separation and classification. To handle the situation of lane-changing, we propose a data smoothing technique based on a multi-hypotheses tracker that exploits vehicle correlation. The road density effect on the probability of correct data association is investigated, with numerical and experimental results provided. The results show that our proposed scheme can effectively detect vehicles with a 95.5% accuracy rate. It also outperforms some other speed sensing methods in terms of the vehicle speed estimation accuracy.