The Contribution of AI-Powered Mobile Apps to Smart City Ecosystems

In the groundbreaking study “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems,” authored by Zaki Ali Bayashot, the transformative role of artificial intelligence (AI) in urban development is meticulously examined. This comprehensive research delineates the multifaceted ways in which AI-powered mobile applications can significantly enhance the efficiency, sustainability, and livability of urban environments, marking a pivotal step towards the realization of smart cities globally. Bayashot meticulously outlines the critical areas where AI-powered apps offer unprecedented advantages, including urban mobility, public safety, energy management, and environmental monitoring. By leveraging AI’s capabilities, these applications not only streamline city operations but also foster a more sustainable interaction between city dwellers and their environment. The paper emphasizes the importance of data-driven decision-making in urban planning, showcasing how AI analytics can predict and mitigate traffic congestion, optimize energy consumption, and enhance emergency response strategies. The author also explores the social implications of AI in urban settings, highlighting the potential for these technologies to bridge the gap between government entities and citizens. Through engaging case studies, Bayashot demonstrates how participatory governance models, enabled by AI apps, can promote transparency, accountability, and citizen engagement in urban management. A significant contribution of this research is its focus on the challenges and opportunities presented by the integration of AI into smart city ecosystems. Bayashot discusses the technical, ethical, and privacy concerns associated with AI applications, advocating for a balanced approach that ensures technological advancements do not come at the expense of civil liberties. The study calls for robust regulatory frameworks to govern the use of AI in public spaces, emphasizing the need for ethical AI practices that respect privacy and promote inclusivity. Furthermore, Bayashot’s research underscores the necessity of cross-disciplinary collaboration in the development and implementation of AI technologies in urban contexts. By bringing together experts from information technology, urban planning, environmental science, and social sciences, the author argues for a holistic approach to smart city development. This interdisciplinary strategy ensures that AI applications are not only technologically sound but also socially and environmentally responsible. The paper concludes with a visionary outlook on the future of smart cities, posited on the seamless integration of AI technologies. Bayashot envisions a world where AI-powered mobile apps not only facilitate smoother urban operations but also empower citizens to actively participate in the shaping of their urban environments. This research serves as a critical call to action for policymakers, technologists, and urban planners to embrace AI as a tool for creating more sustainable, efficient, and inclusive cities. By presenting a detailed analysis of the current state of AI in urban development, coupled with practical insights and forward-looking recommendations, “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems” stands as a seminal work that is poised to inspire and guide the evolution of urban landscapes worldwide. Its comprehensive exploration of the subject matter, combined with its impactful conclusions, make it a must-read for anyone involved in the field of smart city development, AI technology, or urban policy-making.

An Enhanced Multiview Transformer for Population Density Estimation Using Cellular Mobility Data in Smart City

This paper addresses the problem of predicting population density leveraging cellular station data.As wireless communication devices are commonly used,cellular station data has become integral for estimating population figures and studying their movement,thereby implying significant contributions to urban planning.However,existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction.To address this,we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data.The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift.Further,we devise a multi-view enhancement model grounded on the Transformer(MVformer),targeting the improvement of the accuracy of extended time-series population predictions.Comparative experiments,conducted on the above-mentioned population dataset using four alternate Transformer-based models,indicate that our proposedMVformer model enhances prediction accuracy by approximately 30%for both univariate and multivariate time-series prediction assignments.The performance of this model in tasks pertaining to population prediction exhibits commendable results.

Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

Do smart city policies improve energy efficiency?Evidence from China

To address air pollution and offer a convenient and comfortable living environment,the Chinese government launched a smart city pilot(SCP)project in 2012,accompanied by a comprehensive set of environmental and energy-related laws and regulations.Although academic interest in smart cities has surged,there remains a notable gap in empirical research exploring the economic,environmental,and energy effects of such initiatives.Taking 232 prefecture-level cities from 2003 to 2017 as research subjects,this study measures energy effi‐ciency by using energy consumption per unit of GDP and adopts a difference-in-differences(DID)analysis to investigate the impact of SCPs on energy efficiency.The empirical results indicate that SCPs improved energy efficiency by promoting urban technological innovation capabilities and green total factor productivity,and this effect was more pronounced in cities that were more dependent on traditional fossil fuel energy sources and had more developed fiscal and financial levels.Studying the impact of smart city construction on energy utilization efficiency in developing countries,such as China,is not only significantly enlightening for China’s green and low-carbon transition but also provides reference opinions for constructing smart cities and the path to enhancing energy efficiency in other developing countries.The findings provide valuable insights into the global development of smart cities,urban sustainability,and high-quality economic growth.

Geospatial Technology Integration in Smart City Frameworks for Achieving Climate Neutrality by 2050: A Case Study of Limassol Municipality, Cyprus

This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate challenges, the research analyzed how smart city frameworks, aligned with climate neutrality objectives, leverage geospatial technologies for urban planning and climate action. The study included case studies from three leading European cities, extracting lessons and best practices in implementing Climate City Contracts across sectors like energy, transport, and waste management. These insights highlighted the essential role of EU and national authorities in providing technical, regulatory, and financial support. Additionally, the paper presented the application of a WEBGIS platform in Limassol Municipality, Cyprus, demonstrating citizen engagement and acceptance of the proposed geospatial framework. Concluding with recommendations for future research, the study contributed significant insights into the advancement of urban sustainability and the effectiveness of geospatial technologies in smart city initiatives for combating climate change.

Exploration of Smart City Construction

In recent years,all walks of life have begun to propose the construction of“smart cities”.It is hoped that the construction of“smart cities”can pro-mote the transformation and upgrading of urban development and make our cities prosperous and sustainable.In the future,urban construction is very likely to develop in the direction of intelligence and intelligence,and a scientific and effective understanding and objective evaluation of the devel-opment status of China’s smart cities will be conducive to the planning and design of China’s smart cities in the new era,formulate scientific construc-tion policies,and strengthen the supervision and guidance of construction.At present,as far as China is concerned,the urbanization process is accel-erating,the urbanization rate is increasing year by year,and the level of urbanization has increased significantly,and people are beginning to think about how to better combine technology and services to serve the people.Smart cities,as one of the concerns,may be able to better solve this kind of problem.

Smart City Rail Transit Operation and Management Information Construction Strategy

Rail transit is considered one of the safest and most efficient modes of transportation.Ticketing,vehicle dispatching,and passenger flow control during rail transit operations in China have been improving over the years.Smart city construction and intelligent management models has also been increasingly emphasized with the rapid development of information and internet technology.Therefore,it is essential to conduct relevant research and discussions to improve the overall efficiency and quality of urban rail transit operation and management.This article provides an overview of smart city rail transit operation and management informatization,the principles of construction,and the functions of smart city rail transit operation and management informatization.Additionally,it discusses the strategies for the construction of smart city rail transit operation and management information and its development prospects.

Intelligent System Application to Monitor the Smart City Building Lighting

A smart city incorporates infrastructure methods that are environmentally responsible,such as smart communications,smart grids,smart energy,and smart buildings.The city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality,with energy resources taking precedence.To achieve optimal energy management in themultidimensional system of a city tribe,it is necessary not only to identify and study the vast majority of energy elements,but also to define their implicit interdependencies.This is because optimal energy management is required to reach this objective.The lighting index is an essential consideration when evaluating the comfort indicators.In order to realize the concept of a smart city,the primary objective of this research is to create a system for managing and monitoring the lighting index.It is possible to identify two distinct phaseswithin the intelligent system.Once data collection concludes,the monitoring system will be activated.In the second step,the operation of the control system is analyzed and its effect on the performance of the numerical model is determined.This evaluation is based on the proposed methodology.The optimized resultswere deemed satisfactory because they maintained the brightness index value(79%)while consuming less energy.The intelligent implementation system generated satisfactory outcomes,which were observed 1.75 times on average.

Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

In recent times,cities are getting smart and can be managed effectively through diverse architectures and services.Smart cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic diseases.With latest developments in digital data collection,AI techniques can be employed for clinical decision making process.On the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the globe.Generally,wearables can be employed in healthcare systems that instigate the development of CVD detection and classification.With this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC technique.The proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data collection.Then,the collected data is saved in cloud server for examination.Followed by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health condition.To accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)technique.In addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of CVD.The experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%.

A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

Smart city refers to the information system with Intemet of things and cloud computing as the core tec hnology and government management and industrial development as the core content,forming a large scale,heterogeneous and dynamic distributed Internet of things environment between different Internet of things.There is a wide demand for cooperation between equipment and management institutions in the smart city.Therefore,it is necessary to establish a trust mechanism to promote cooperation,and based on this,prevent data disorder caused by the interaction between honest terminals and malicious temminals.However,most of the existing research on trust mechanism is divorced from the Internet of things environment,and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of hings devices,resuling in the fact that the research on abstract trust trust mechanism cannot be directly applied to the Internet of things;On the other hand,various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered.Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals,a cross domain trust model(CDTM)based on self-authentication is proposed.Unlike most trust models,this model uses self-certified trust.The cross-domain process of internet of things(IoT)terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction.At the same time,in order to alleviate the collision attack and improve the accuracy of trust evaluation,the overall trust value is calculated by comprehensively considering the quantity weight,time attenuation weight and similarity weight.Finally,the simulation results show that CDTM has good anti collusion attack ability.The success rate of malicious interaction will not increase significantly.Compared with other models,the resource consumption of our proposed model is significantly reduced.

Design the IoT Botnet Defense Process for Cybersecurity in Smart City

The smart city comprises various infrastructures,including health-care,transportation,manufacturing,and energy.A smart city’s Internet of Things(IoT)environment constitutes a massive IoT environment encom-passing numerous devices.As many devices are installed,managing security for the entire IoT device ecosystem becomes challenging,and attack vectors accessible to attackers increase.However,these devices often have low power and specifications,lacking the same security features as general Information Technology(IT)systems,making them susceptible to cyberattacks.This vulnerability is particularly concerning in smart cities,where IoT devices are connected to essential support systems such as healthcare and transportation.Disruptions can lead to significant human and property damage.One rep-resentative attack that exploits IoT device vulnerabilities is the Distributed Denial of Service(DDoS)attack by forming an IoT botnet.In a smart city environment,the formation of IoT botnets can lead to extensive denial-of-service attacks,compromising the availability of services rendered by the city.Moreover,the same IoT devices are typically employed across various infrastructures within a smart city,making them potentially vulnerable to similar attacks.This paper addresses this problem by designing a defense process to effectively respond to IoT botnet attacks in smart city environ-ments.The proposed defense process leverages the defense techniques of the MITRE D3FEND framework to mitigate the propagation of IoT botnets and support rapid and integrated decision-making by security personnel,enabling an immediate response.

Analysis and Modeling of Mobile Phone Activity Data Using Interactive Cyber-Physical Social System

Mobile networks possess significant information and thus are considered a gold mine for the researcher’s community.The call detail records(CDR)of a mobile network are used to identify the network’s efficacy and the mobile user’s behavior.It is evident from the recent literature that cyber-physical systems(CPS)were used in the analytics and modeling of telecom data.In addition,CPS is used to provide valuable services in smart cities.In general,a typical telecom company hasmillions of subscribers and thus generatesmassive amounts of data.From this aspect,data storage,analysis,and processing are the key concerns.To solve these issues,herein we propose a multilevel cyber-physical social system(CPSS)for the analysis and modeling of large internet data.Our proposed multilevel system has three levels and each level has a specific functionality.Initially,raw Call Detail Data(CDR)was collected at the first level.Herein,the data preprocessing,cleaning,and error removal operations were performed.In the second level,data processing,cleaning,reduction,integration,processing,and storage were performed.Herein,suggested internet activity record measures were applied.Our proposed system initially constructs a graph and then performs network analysis.Thus proposed CPSS system accurately identifies different areas of internet peak usage in a city(Milan city).Our research is helpful for the network operators to plan effective network configuration,management,and optimization of resources.

Do companies influenced by smart cities pay more attention to green governance?Mechanism and optimal spatial correlation

Smart cities are a way for China to construct an innovative and environmentally conscious nation.The paper examines the impact of smart cities on corporate green governance and provides a theoretical foundation for formulating and executing smart city policy in China.Based on panel data from Chinese A-share listed companies in Shanghai and Shenzhen from 2008 to 2020,this study constructs a multiperiod double-difference model to examine the influence of smart cities on corporate green governance.Additionally,it uses a spatial double-difference model to investigate the spatial spillover effect of smart cities on neighboring areas.The findings indicate that smart cities effectively enhance corporate green governance.Analyzing the influencing mechanisms reveals that resource allocation efficiency,technological innovation,management environmental awareness,and regional environmental enforcement efforts act as mediators.Furthermore,the study reveals that the impact of smart cities on promoting corporate green governance is more pronounced in regions with lower levels of marketization and resource-based cities.Moreover,the research explores the spatial spillover effects of smart cities,with an effective radius of approximately 350 km.The optimal spatial correlation zone for green governance of businesses in neighboring areas in relation to smart cities is within a range of 250-350 km.This is manifested by the significant promotion of green governance in neighboring area businesses facilitated by smart cities.

Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

Real-Time Control System Adopted to Energy Storage for Smart Grid Low Wind Applications:A Part of Distributed Renewables in Smart City

In this paper,extensive efforts have been undertaken to design and develop a control system,which is incorporated with an energy storage device that can store energy from low-voltage renewable sources.The developed device acts as a storage element,which can be used to charge small-scale batteries,cellular devices,and other applications in remote places where the grid connection is not available.The circuit is developed using a case-by-case analysis.In order to solve the low output voltage problem,a bipolar junction transistor-metal oxide semiconductor field-effect transistor(BJT-MOSFET)based switch control technology with the Arduino microcontroller has been implemented.The developed control system is extremely efficient in charging batteries through a supercapacitor for low-voltage sources.In this research,a small-scale 200-W portable vertical axis wind turbine is used at a wind speed of 3 m/s.The result shows the efficiency of the proposed system as compared with the conventional systems.The proposed system can be an important tool of the latest distributed energy generation technology which is an important part of a smart city.Lastly,the limitations and future scopes of the development of the control device are discussed for the future barrier.An important future scope identified is to integrate the Internet of Things based mobile interface for remote monitoring for any kind of pandemic situation like COVID-19.Now,it is high time to get our smart city concept aligned with the post COVID pandemic situation and get us prepared smartly for similar future occurrences.

Research on the construction of smart city railvehicle technical standards system

With the rapid development and application of cloud computing,big data,artificial intelligence,5G,satellite communication,blockchain and other emerging information technologies,conditions have been provided for the intelligent development of urban rail vehicles.With the development of smart urban rail vehicles,the standards system of traditional urban rail vehicle cannot meet the development requirements,so it is necessary to study and reconstruct the standards system.To embody the intelligent level of urban rail vehicles,the paper conducts the research on the standards for the urban rail vehicle train control system,monitoring and diagnosis system,passenger service system,and then proposes the overall architecture and standard list of smart city rail vehicle technical standards system,providing reference for the planning and establishment of China's smart city rail standards system.

Application of Landscape Smart Lighting in Construction Projects

Currently,to effectively construct high-quality smart cities and achieve comprehensive governance goals,various industries must embrace the transformation and upgrading of traditional management methods through the integration of new technologies.Among these,landscape smart lighting serves as a crucial tool for advancing smart city development.By judiciously applying this technology,it is possible to enhance traditional lighting methods and offer people novel experiences in their daily lives.Recognizing this importance,this article analyzes and organizes the content of landscape smart lighting,delves into its system characteristics,and summarizes its principles and practices in the field of architectural engineering as practical references for application scenarios.

HoD Detector, a System to Create Indicators about the Happiness of Citizens in a Smart City: Case of Study—GDL Smart City

Happiness is the ultimate goal for every living human being, and this condition motivates people to stay active and want to be alive. Having this feeling also fosters the proactivity of citizens. The opposite is depression, a condition capable of significantly affecting a person’s behavior or perception. HoD Detector is a low-cost, easy to use application that aids in the diagnosis of a depressive disorder and motivates the user to seek professional help. The purpose of this study is to propose a way to measure happiness through the measurement of depression;to develop HoD Detector, evaluate the accuracy and usability of this application (with university students) for future enhancements of the system. The development of this application involved basic algorithms and the use of a database to successfully make a diagnosis and store the results of each user for further reference. The application is capable of giving a proper diagnosis with the time interval of 2 - 5 minutes. HoD Detector has the potential to become an extremely useful tool to diagnose depression and motivate users to seek professional help not only in Mexico, but also on an international scale for improving in people’s happiness condition.

Smart City Architecture： A Technology Guide for Implementation and Design Challenges

Urban development is becoming increasingly reliant on effective use of intelligent services.In the process of providing better services to all citizens and improving the efficiency of administration processes,the concept of a smart city has been lauded as a promising solution for the coming challenge of global urbanization.However,due to the broad scope of smart cities their definition has not been standardized,thus proposed architectures are diverse.This work proposes a new architecture from the perspective of the data that underpins all the functionality of smart cities.The proposed architecture is discussed,outlining design challenges and offering insight into the development of smart cities.