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.

City Network Evolution Characteristics of Smart Industry: Evidence from Yangtze River Delta, China

The dual-path model of industrial evolution and spatial progression has been widely acknowledged and incorporated into the strategic planning to promote the development of urban industries and regional collaborations.However,current research on inter-enter-prise city networks mainly focuses on the single sector of flows on all enterprise branches,such as product value chains and production factors,but neglects that of particular industry department.Built upon the new economic geography and city networks theory,this paper develops a methodological framework that focuses on the analysis of city network evolution characteristics of smart industry.Particu-larly,a conceptual model of smart industry enterprise-industry-city is proposed and then applied to a case study of smart industry in the Yangtze River Delta Region,China.Using enterprise supplier-customer data,a city network of smart industry is constructed and sub-sequently analyzed with the proposed model.Findings indicate that the smart industry network in Yangtze River Delta Region exhibits a hierarchical structure and the expansion of the network presents a small-world network characteristic.The study not only makes a meth-odological contribution for revealing the industrial and spatial evolution path of the current smart industry,but also provides empirical support for the formulation of new economic development policies focused on smart industries,demonstrating the role of city clusters as carriers of regional synergistic development.

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%.

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.

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.

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.

The Application of Old City Planning Based on the Concept of Smart Cities: A Case Study of Wanli District, Nanchang

The impact of a smart city as one of the new urban forms on the city is still in the exploratory stage. Therefore, it is necessary to fully analyze the impact of a smart city in the urban planning field, and actively explore the planning ideas and methods that are compatible with the planning and renovation of the old city. On the basis of applying the concept of smart cities, Wanli District of Nanchang City has paid attention to the optimization of urban spatial structure, focused on the development of tourism industry, built an intelligent transportation system with priority to public transport, and improved infrastructure construction, in order to provide guidance for the wisdom update of the old city.

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.

Enlightenment of Smart Growth Theories on the Development of Nanjing City,China

The background and connotations of smart growth theories of America were introduced first,spatial expansion features of Nanjing City were summarized as the following 3 points:population growth,expansion of urban spaces and industrial expansion.And then problems emerged in the spatial expansion were pointed out as:A."Sprawl" in the urban development;B.Improper internal spatial structure of the city;C.Destructed historical and cultural context of the city.Smart growth theories were applied in this study to analyze potential problems in the spatial expansion of Nanjing,corresponding countermeasures were given as:defining proper growth boundaries,restricting total growth volume,saving land;strictly controlling growth intensity,fully utilizing resources,intensively using land;scientifically evaluating growth benefits,promoting recycling of resources,and realizing sustainable growth.By learning from overseas smart growth theories,the authors hoped to provide useful references for the overall land use planning and urban planning of Nanjing City.

Blockchain-based sharing services:What blockchain technology can contribute to smart cities

Background:The notion of smart city has grown popular over the past few years.It embraces several dimensions depending on the meaning of the word“smart”and benefits from innovative applications of new kinds of information and communications technology to support communal sharing.Methods:By relying on prior literature,this paper proposes a conceptual framework with three dimensions:(1)human,(2)technology,and(3)organization,and explores a set of fundamental factors that make a city smart from a sharing economy perspective.Results:Using this triangle framework,we discuss what emerging blockchain technology may contribute to these factors and how its elements can help smart cities develop sharing services.Conclusions:This study discusses how blockchain-based sharing services can contribute to smart cities based on a conceptual framework.We hope it can stimulate interest in theory and practice to foster discussions in this area.

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.

Securing Parked Vehicle Assisted Fog Computing With Blockchain and Optimal Smart Contract Design

Vehicular fog computing(VFC)has been envisioned as an important application of fog computing in vehicular networks.Parked vehicles with embedded computation resources could be exploited as a supplement for VFC.They cooperate with fog servers to process offloading requests at the vehicular network edge,leading to a new paradigm called parked vehicle assisted fog computing(PVFC).However,each coin has two sides.There is a follow-up challenging issue in the distributed and trustless computing environment.The centralized computation offloading without tamper-proof audit causes security threats.It could not guard against false-reporting,free-riding behaviors,spoofing attacks and repudiation attacks.Thus,we leverage the blockchain technology to achieve decentralized PVFC.Request posting,workload undertaking,task evaluation and reward assignment are organized and validated automatically through smart contract executions.Network activities in computation offloading become transparent,verifiable and traceable to eliminate security risks.To this end,we introduce network entities and design interactive smart contract operations across them.The optimal smart contract design problem is formulated and solved within the Stackelberg game framework to minimize the total payments for users.Security analysis and extensive numerical results are provided to demonstrate that our scheme has high security and efficiency guarantee.

Optimal Confidential Mechanisms in Smart City Healthcare

Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely manner.The system also concedes the freedom of a patient.IoT is a part of this system and it helps in providing care to the patients.IoTbased healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate care.Existing methodologies exhibit few shortcomings in terms of computational complexity,cost and data security.Hence,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in PRESENT cipher.This procedure aims at changing the sub bytes in which a single function is connected with several bytes’information to upgrade the security level through Swam optimization.The key contribution of this research article is the development of a secure healthcare model for smart city using SHC2 security via LWC and Optimal S-Box models.The study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC2 information,constrained by Mutual Authentication(MA).The security challenges,in healthcare information systems,emphasize the need for a methodology that immovably concretes the establishments.The methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing threats.Healthcare service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare service.The proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access policies.The model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum range.Then,the level of the model and throughput were analyzed by maximum value i.e.,50Mbps/sec and 95.56%for PRESENT-Authorization cipher to achieve smart city security.The proposed model achieved better results than the existing methodologies.

Mitigating and Monitoring Smart City Using Internet of Things

The present trends in smart world reflects the extensive use of limited resources through information and communication technology.The limited resources like space,mobility,energy,etc.,have been consumed rigorously towards creating optimized but smart instances.Thus,a new concept of IoT integrated smart city vision is yet to be proposed which includes a combination of systems like noise and air loss monitoring,web monitoring and fire detection systems,smart waste bin systems,etc.,that have not been clearly addressed in the previous researches.This paper focuses on developing an effective system for possible monitoring of losses,traffic management,thus innovating smart city at large with digitalized and integrated systems and software for fast and effective implementations.In our proposed system,a real time data analysis is performed.These data are collected by various sensors to analyze different factors that are responsible for such losses.The proposed work is validated on a real case study.

Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic accidents.Various congestion pricing and electric vehicle charging policies have been introduced in recent years.Nonetheless,the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management system.In this paper,we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city environment.The proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging stations.We propose a token management system that serves as a virtual currency,where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging fees.The proposed system is designed for Vehicular Ad-hoc Networks(VANETs)in the context of a smart city environment without the need to set up any expensive toll collection stations.Through large-scale traffic simulation in different smart city scenarios,it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.

Endogenous Security-Aware Resource Management for Digital Twin and 6G Edge Intelligence Integrated Smart Park

The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.

Smart Cities as Cyber-Physical Social Systems

The emerging prototype for a Smart City is one of an urban environment with a new generation of inno- vative services for transportation, energy distribution, healthcare, environmental monitoring, business, commerce, emergency response, and social activities. Enabling the technology for such a setting re- quires a viewpoint of Smart Cities as cyber-physical systems （CPSs） that include new software platforms and strict requirements for mobility, security, safety, privacy, and the processing of massive amounts of information. This paper identifies some key defining characteristics of a Smart City, discusses some lessons learned from viewing them as CPSs, and outlines some fundamental research issues that remain largely open.