Cyber Security within Smart Cities:A Comprehensive Study and a Novel Intrusion Detection-Based Approach

The expansion of smart cities,facilitated by digital communications,has resulted in an enhancement of the quality of life and satisfaction among residents.The Internet of Things(IoT)continually generates vast amounts of data,which is subsequently analyzed to offer services to residents.The growth and development of IoT have given rise to a new paradigm.A smart city possesses the ability to consistently monitor and utilize the physical environment,providing intelligent services such as energy,transportation,healthcare,and entertainment for both residents and visitors.Research on the security and privacy of smart cities is increasingly prevalent.These studies highlight the cybersecurity risks and the challenges faced by smart city infrastructure in handling and managing personal data.To effectively uphold individuals’security and privacy,developers of smart cities must earn the trust of the public.In this article,we delve into the realms of privacy and security within smart city applications.Our comprehensive study commences by introducing architecture and various applications tailored to smart cities.Then,concerns surrounding security and privacy within these applications are thoroughly explored subsequently.Following that,we delve into several research endeavors dedicated to addressing security and privacy issues within smart city applications.Finally,we emphasize our methodology and present a case study illustrating privacy and security in smart city contexts.Our proposal consists of defining an Artificial Intelligence(AI)based framework that allows:Thoroughly documenting penetration attempts and cyberattacks;promptly detecting any deviations from security standards;monitoring malicious behaviors and accurately tracing their sources;and establishing strong controls to effectively repel and prevent such threats.Experimental results using the Edge-IIoTset(Edge Industrial Internet of Things Security Evaluation Test)dataset demonstrated good accuracy.They were compared to related state-of-theart works,which highlight the relevance of our proposal.

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.

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%.

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.

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.

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.

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.

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.

Deep Learning Method to Detect the Road Cracks and Potholes for Smart Cities

The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.

Security and Privacy Concerns over IoT Devices Attacks in Smart Cities (2022)

Due to the long-term goal of bringing about significant changes in the quality of services supplied to smart city residents and urban environments and life, the development and deployment of ICT in city infrastructure has spurred interest in smart cities. Applications for smart cities can gather private data in a variety of fields. Different sectors such as healthcare, smart parking, transportation, traffic systems, public safety, smart agriculture, and other sectors can control real-life physical objects and deliver intelligent and smart information to citizens who are the users. However, this smart ICT integration brings about numerous concerns and issues with security and privacy for both smart city citizens and the environments they are built in. The main uses of smart cities are examined in this journal article, along with the security needs for IoT systems supporting them and the identified important privacy and security issues in the smart city application architecture. Following the identification of several security flaws and privacy concerns in the context of smart cities, it then highlights some security and privacy solutions for developing secure smart city systems and presents research opportunities that still need to be considered for performance improvement in the future.

Embracing the Future:AI and ML Transforming Urban Environments in Smart Cities

This research explores the increasing importance of Artificial Intelligence(AI)and Machine Learning(ML)with relation to smart cities.It discusses the AI and ML’s ability to revolutionize various aspects of urban environments,including infrastructure,governance,public safety,and sustainability.The research presents the definition and characteristics of smart cities,highlighting the key components and technologies driving initiatives for smart cities.The methodology employed in this study involved a comprehensive review of relevant literature,research papers,and reports on the subject of AI and ML in smart cities.Various sources were consulted to gather information on the integration of AI and ML technologies in various aspects of smart cities,including infrastructure optimization,public safety enhancement,and citizen services improvement.The findings suggest that AI and ML technologies enable data-driven decision-making,predictive analytics,and optimization in smart city development.They are vital to the development of transport infrastructure,optimizing energy distribution,improving public safety,streamlining governance,and transforming healthcare services.However,ethical and privacy considerations,as well as technical challenges,need to be solved to guarantee the ethical and responsible usage of AI and ML in smart cities.The study concludes by discussing the challenges and future directions of AI and ML in shaping urban environments,highlighting the importance of collaborative efforts and responsible implementation.The findings highlight the transformative potential of AI and ML in optimizing resource utilization,enhancing citizen services,and creating more sustainable and resilient smart cities.Future studies should concentrate on addressing technical limitations,creating robust policy frameworks,and fostering fairness,accountability,and openness in the use of AI and ML technologies in smart cities.

Smart cities from low cost to expensive solutions under an optimal analysis

This scientific approach mainly aims to develop a smart city/smart community concept to objectively evaluate the progress of these organizational forms in relation to other classical/traditional forms of city organizations.The elaborated model allowed the construction of the dashboard of access actions in the smart city/smart community category on two levels of financial effort correlated with the effect on the sustainable development of smart cities.The validity of the proposed model and our approach was supported by the complex statistical analysis performed in this study.The research concluded that low-cost solutions are the most effective in supporting smart urban development.They should be followed by the other category of solutions,which implies more significant financial and managerial efforts as well as a higher rate of welfare growth for urban citizens.The main outcomes of this research include modelling solutions related to smart city development at a low-cost level and identifying the sensitivity elements that maximize the growth function.The implications of this research are to provide viable alternatives based on smart city development opportunities with medium and long-term effects on urban communities,economic sustainability,and translation into urban development rates.This study’s results are useful for all administrations ready for change that want the rapid implementation of the measures with beneficial effects on the community or which,through a strategic vision,aim to connect to the European objectives of sustainable growth and social welfare for citizens.Practically,this study is a tool for defining and implementing smart public policies at the urban level.

An evaluation model for smart grids in support of smart cities based on the Hierarchy of Needs Theory

Smart cities depend highly on an intelligent electrical networks to provide a reliable,safe,and clean power supplies.A smart grid achieves such aforementioned power supply by ensuring resilient energy delivery,which presents opportunities to improve the cost-effectiveness of power supply and minimize environmental impacts.A systematic evaluation of the comprehensive benefits brought by smart grid to smart cities can provide necessary theoretical fundamentals for urban planning and construction towards a sustainable energy future.However,most of the present methods of assessing smart cities do not fully take into account the benefits expected from the smart grid.To comprehensively evaluate the development levels of smart cities while revealing the supporting roles of smart grids,this article proposes a model of smart city development needs from the perspective of residents’needs based on Maslow’s Hierarchy of Needs theory,which serves the primary purpose of building a smart city.By classifying and reintegrating the needs,an evaluation index system of smart grids supporting smart cities was further constructed.A case analysis concluded that smart grids,as an essential foundation and objective requirement for smart cities,are important in promoting scientific urban management,intelligent infrastructure,refined public services,efficient energy utilization,and industrial development and modernization.Further optimization suggestions were given to the city analyzed in the case include strengthening urban management and infrastructure constructions,such as electric vehicle charging facilities and wireless coverage.

An Optimized Offloaded Task Execution for Smart Cities Applications

Wireless nodes are one of the main components in different applications that are offered in a smart city.These wireless nodes are responsible to execute multiple tasks with different priority levels.As the wireless nodes have limited processing capacity,they offload their tasks to cloud servers if the number of tasks exceeds their task processing capacity.Executing these tasks from remotely placed cloud servers causes a significant delay which is not required in sensitive task applications.This execution delay is reduced by placing fog computing nodes near these application nodes.A fog node has limited processing capacity and is sometimes unable to execute all the requested tasks.In this work,an optimal task offloading scheme that comprises two algorithms is proposed for the fog nodes to optimally execute the time-sensitive offloaded tasks.The first algorithm describes the task processing criteria for local computation of tasks at the fog nodes and remote computation at the cloud server.The second algorithm allows fog nodes to optimally scrutinize the most sensitive tasks within their task capacity.The results show that the proposed task execution scheme significantly reduces the execution time and most of the time-sensitive tasks are executed.

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.