The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)...The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.展开更多
Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic top...Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.展开更多
The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often...The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.展开更多
This paper addresses urban sustainability challenges amid global urbanization, emphasizing the need for innova tive approaches aligned with the Sustainable Development Goals. While traditional tools and linear models ...This paper addresses urban sustainability challenges amid global urbanization, emphasizing the need for innova tive approaches aligned with the Sustainable Development Goals. While traditional tools and linear models offer insights, they fall short in presenting a holistic view of complex urban challenges. System dynamics (SD) models that are often utilized to provide holistic, systematic understanding of a research subject, like the urban system, emerge as valuable tools, but data scarcity and theoretical inadequacy pose challenges. The research reviews relevant papers on recent SD model applications in urban sustainability since 2018, categorizing them based on nine key indicators. Among the reviewed papers, data limitations and model assumptions were identified as ma jor challenges in applying SD models to urban sustainability. This led to exploring the transformative potential of big data analytics, a rare approach in this field as identified by this study, to enhance SD models’ empirical foundation. Integrating big data could provide data-driven calibration, potentially improving predictive accuracy and reducing reliance on simplified assumptions. The paper concludes by advocating for new approaches that reduce assumptions and promote real-time applicable models, contributing to a comprehensive understanding of urban sustainability through the synergy of big data and SD models.展开更多
Beijing defines a new model for digital-friendly cities where technology meets people’s needs and global cooperation drives inclusivity.THE 6,000-square-meter Beijing FUN Digital Complex,a vibrant pedestrian street i...Beijing defines a new model for digital-friendly cities where technology meets people’s needs and global cooperation drives inclusivity.THE 6,000-square-meter Beijing FUN Digital Complex,a vibrant pedestrian street in the historic Qianmen area of central Beijing,was bustling with visitors during the 2025 Beijing Digital Economy Experience Week,held from June 27 to July 5.Some climbed into a full-size C919 flight simulator,while others put on a domestically-made 8K VR headset to test a real-time voice-interaction model,immersing themselves in a dazzling virtual world.展开更多
This century's rapid urbanization has disrupted urban governance,sustainability,and resource management.The Internet of Things(IoT)and 5G have the potential to transform smart cities through real-time data process...This century's rapid urbanization has disrupted urban governance,sustainability,and resource management.The Internet of Things(IoT)and 5G have the potential to transform smart cities through real-time data processing,enhanced connectivity,and sustainable urban design.This study investigates the potential of 5G connectivity with the IoT's hierarchical framework to enhance public service provision,mitigate environmental effects,and optimize urban resource management.The article asserts that these technologies can enhance urban operations by tackling scalability,interoperability,and security issues.The research employs case studies from Singapore and Barcelona.The document moreover analyzes AI-driven security systems,6G networks,and the contributions of IoT and 5G to the advancement of a circular economy.The essay asserts that the growth of smart cities necessitates robust policy frameworks to guarantee equitable access,data protection,and ethical considerations.This study integrates prior research with practical experiences to tackle data-informed municipal governance and urban innovation.The importance of policy in fostering inclusive and sustainable urban futures is emphasized.展开更多
The convergence of Internet of things(IoT)and 5G holds immense potential for transforming industries by enabling real-time,massive-scale connectivity and automation.However,the growing number of devices connected to t...The convergence of Internet of things(IoT)and 5G holds immense potential for transforming industries by enabling real-time,massive-scale connectivity and automation.However,the growing number of devices connected to the IoT systems demands a communication network capable of handling vast amounts of data with minimal delay.These generated enormous complex,high-dimensional,high-volume,and high-speed data also brings challenges on its storage,transmission,processing,and energy cost,due to the limited computing capabilities,battery capacity,memory,and energy utilization of current IoT networks.In this paper,a seamless architecture by combining mobile and cloud computing is proposed.It can agilely bargain with 5G-IoT devices,sensor nodes,and mobile computing in a distributed manner,enabling minimized energy cost,high interoperability,and high scalability as well as overcoming the memory constraints.An artificial intelligence(AI)-powered green and energy-efficient architecture is then proposed for 5G-IoT systems and sustainable smart cities.The experimental results reveal that the proposed approach dramatically reduces the transmitted data volume and power consumption and yields superior results regarding interoperability,compression ratio,and energy saving.This is especially critical in enabling the deployment of 5G and even 6G wireless systems for smart cities.展开更多
The exponential growth of the Internet of Things(IoT)has revolutionized various domains such as healthcare,smart cities,and agriculture,generating vast volumes of data that require secure processing and storage in clo...The exponential growth of the Internet of Things(IoT)has revolutionized various domains such as healthcare,smart cities,and agriculture,generating vast volumes of data that require secure processing and storage in cloud environments.However,reliance on cloud infrastructure raises critical security challenges,particularly regarding data integrity.While existing cryptographic methods provide robust integrity verification,they impose significant computational and energy overheads on resource-constrained IoT devices,limiting their applicability in large-scale,real-time scenarios.To address these challenges,we propose the Cognitive-Based Integrity Verification Model(C-BIVM),which leverages Belief-Desire-Intention(BDI)cognitive intelligence and algebraic signatures to enable lightweight,efficient,and scalable data integrity verification.The model incorporates batch auditing,reducing resource consumption in large-scale IoT environments by approximately 35%,while achieving an accuracy of over 99.2%in detecting data corruption.C-BIVM dynamically adapts integrity checks based on real-time conditions,optimizing resource utilization by minimizing redundant operations by more than 30%.Furthermore,blind verification techniques safeguard sensitive IoT data,ensuring privacy compliance by preventing unauthorized access during integrity checks.Extensive experimental evaluations demonstrate that C-BIVM reduces computation time for integrity checks by up to 40%compared to traditional bilinear pairing-based methods,making it particularly suitable for IoT-driven applications in smart cities,healthcare,and beyond.These results underscore the effectiveness of C-BIVM in delivering a secure,scalable,and resource-efficient solution tailored to the evolving needs of IoT ecosystems.展开更多
The smart city is not only a crucial means of promoting sustainable development,but also a strategic approach to advancing scientific urban development and governance on a global scale.Based on a textual co-occurrence...The smart city is not only a crucial means of promoting sustainable development,but also a strategic approach to advancing scientific urban development and governance on a global scale.Based on a textual co-occurrence analysis of policy documents,this study examines the components,development pathways,and models of smart cities in China.It identifies three distinct phases in the development process:the information project-driven phase,the holistic development phase,and the collaborative development phase.Overall,the development of smart cities in China-characterized by a strong focus on information technology projects-was primarily initiated by both central and local governments.Reflecting on the informatization process and the trajectory of smart city development in China since the mid-1990s,this paper argues that smart city initiatives must evolve beyond a purely technology-driven framework toward a more human-centered approach.Against the backdrop of ongoing urbanization and urban transformation in China,this study proposes future development strategies that integrate top-down government planning with bottom-up public engagement.It advocates for a comprehensive framework that integrates technology,human needs,and spatial planning,emphasizing the spatiotemporal coordination between technological implementation and stakeholder demands.This research offers valuable insights and strategic guidance for the future development of smart cities worldwide.展开更多
The world is being swept by the wave of smart cities,and Geographic Information System(GIS)technology plays a key role in it.This article explores the application status and development trend of GIS technology in urba...The world is being swept by the wave of smart cities,and Geographic Information System(GIS)technology plays a key role in it.This article explores the application status and development trend of GIS technology in urban planning from the perspective of smart cities.By sorting out the application value of GIS in urban strategic layout,multi system collaboration,and precise services,it explains the necessity of promoting the transformation of planning towards intelligence.We have summarized and organized the practical points of GIS in areas such as overall planning,underground space,landscape,and transportation,including spatial simulation,3D management,ecological integration,and dynamic optimization.Looking ahead to the future,the cross integration of GIS with artificial intelligence,3D modeling,carbon neutrality monitoring,data collaboration,and other technologies will drive planning towards a new stage of development that is more intelligent,refined,open,and green.With the empowerment of GIS,we aim to create sustainable cities that are resilient,low-carbon,and inclusive,making urban life better.展开更多
Internet of Things(IoT)technology has brought about significant new changes to residents’lives,prompting changes in management models across various industries and promoting the overall intelligence of urban construc...Internet of Things(IoT)technology has brought about significant new changes to residents’lives,prompting changes in management models across various industries and promoting the overall intelligence of urban construction.Especially in the context of continuous technological development,information sensor devices can be effectively utilized to connect multiple dimensions in urban construction,enhancing the intelligence level of cities in China.This paper mainly elaborates on the application significance of IoT technology in smart cities and proposes corresponding measures from aspects such as smart transportation systems,intelligent public utility management,urban safety and monitoring,environmental monitoring,and sustainability,providing references for relevant personnel.展开更多
As urbanization process has been and will be happening in an unprecedented scale worldwide,strong requirements from academic research and practical fields for smart management and intelligent planning of cities are pr...As urbanization process has been and will be happening in an unprecedented scale worldwide,strong requirements from academic research and practical fields for smart management and intelligent planning of cities are pressing to handle increasing demands of infrastructure and potential risks of inhabitants’agglomeration in disaster management.Geospatial data and geographic information systems(GISs)are essential components for building smart cities in a basic way that maps the physical world into virtual environment as a referencing framework.On higher level,GIS has been becoming very important in smart cities on different sectors.In the digital city era,digital maps and geospatial databases have long been integrated in workflows in land management,urban planning and transportation in government.People have anticipated GIS to be more powerful not only as an archival and data management tool but also as spatial models for supporting decision-making in intelligent cities.Successful applications have been developed in private and public organizations by using GIS as a platform for data integration,a system for geospatial analysis and collection of models for visualization and decision-making.Location-based services on smart mobile devices in ubiquitous telecommunication networks are now an indispensable function that expands knowledge of the nature and connections among people.On data side,crowd-sourcing,real-time urban sensing and true 3-dimensional(3D)models and visualization have provided more advantages of GIS to final users and at the same time challenged current available solutions and technologies of data handling,visualization and human–computer interaction.On the technological side,Web 2.0 participatory applications provide the framework and environment for GIS to closer link to photogrammetry and computer vision,which empowers smart devices more capabilities.How to manage big geo-tagged data volumes collected by numerous sensors and implement professional GIS functions in a cloud computing environment are urgent questions to facilitate smart cities management.This paper reviews advancements of GIS in the management of cities as information systems to facilitate urban modelling and decision-making,as referencing basis to integrate social network media,and concludes that an interdisciplinary urban GIS is needed to support development of smart cities.We take Singapore as a case of GIS pervasive applications,which has strategically made a master plan of national information infrastructure and has been implementing geospatial collaboration environments for public and private sectors.展开更多
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, ...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.展开更多
Wireless sensor networks(WSNs)and Internet of Things(IoT)have gained more popularity in recent years as an underlying infrastructure for connected devices and sensors in smart cities.The data generated from these sens...Wireless sensor networks(WSNs)and Internet of Things(IoT)have gained more popularity in recent years as an underlying infrastructure for connected devices and sensors in smart cities.The data generated from these sensors are used by smart cities to strengthen their infrastructure,utilities,and public services.WSNs are suitable for long periods of data acquisition in smart cities.To make the networks of smart cities more reliable for sensitive information,the blockchain mechanism has been proposed.The key issues and challenges of WSNs in smart cities is efficiently scheduling the resources;leading to extending the network lifetime of sensors.In this paper,a linear network coding(LNC)for WSNs with blockchain-enabled IoT devices has been proposed.The consumption of energy is reduced for each node by applying LNC.The efficiency and the reliability of the proposed model are evaluated and compared to those of the existing models.Results from the simulation demonstrate that the proposed model increases the efficiency in terms of the number of live nodes,packet delivery ratio,throughput,and the optimized residual energy compared to other current techniques.展开更多
In recent years,Software Defined Networking(SDN)has become an important candidate for communication infrastructure in smart cities.It produces a drastic increase in the need for delivery of video services that are of ...In recent years,Software Defined Networking(SDN)has become an important candidate for communication infrastructure in smart cities.It produces a drastic increase in the need for delivery of video services that are of high resolution,multiview,and large-scale in nature.However,this entity gets easily influenced by heterogeneous behaviour of the user’s wireless link features that might reduce the quality of video stream for few or all clients.The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences.Besides,multicast routing protocol with multiple constraints in terms of Quality of Service(QoS)is a Nondeterministic Polynomial time(NP)hard problem which can be solved only with the help of metaheuristic optimization algorithms.With this motivation,the current research paper presents a new Improved BlackWidow Optimization with Levy Distribution model(IBWO-LD)-based multicast routing protocol for smart cities.The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive.Besides,a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints.A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects.The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods.展开更多
In this paper, a brief survey of smart citiy projects in Europe is presented. This survey shows the extent of transport and logistics in smart cities. We concentrate on a smart city project we have been working on tha...In this paper, a brief survey of smart citiy projects in Europe is presented. This survey shows the extent of transport and logistics in smart cities. We concentrate on a smart city project we have been working on that is related to A Logistic Mobile Application （ALMA）. The application is based on Internet of Things and combines a communication infrastructure and a High Performance Computing infrastructure in order to deliver mobile logistic services with high quality of service and adaptation to the dynamic nature of logistic operations.展开更多
Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green ...Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy management.In smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart grids.In complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research challenge.These challenges can be resolved through various machine learning techniques and data analytics.In this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart grid.The proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution companies.In the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm optimization.The simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.展开更多
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 perc...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.展开更多
The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas.This problem is an NP(nondeterministic polynomial time)-hard problem becau...The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas.This problem is an NP(nondeterministic polynomial time)-hard problem because of involving many objective functions,many constraints,and complex search space.Moreover,this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses.Different types ofMulti-Objective Optimization Algorithms(MOOAs)based on Artificial Intelligence(AI)have been frequently employed,but their ability and performance have not been evaluated and compared properly.This paper aims to employ and compare three commonly used MOOAs i.e.NSGA-II,MOPSO,and MOEA/D in urban land-use allocation problems.Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages.The objective functions of this study are compatibility,dependency,suitability,and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment.Evaluation of results is based on the dispersion of the solutions,diversity of the solutions’space,and comparing the number of dominant solutions in Pareto-Fronts.The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses.However,the run time of NSGA-II is the worst,related to the Diversity Metric(DM)which represents the regularity of the distance between solutions at the highest degree.Moreover,MOPSO provides the best Scattering Diversity Metric(SDM)which shows the diversity of solutions in the solution space.Furthermore,In terms of algorithm execution time,MOEA/D performed better than the other two.So,Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems.展开更多
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...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.展开更多
基金funded and supported by the Ongoing Research Funding program(ORF-2025-314),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.
基金funded by Hung Yen University of Technology and Education under grand number UTEHY.L.2025.62.
文摘Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025)。
文摘The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.
基金sponsored by the U.S.Department of Housing and Urban Development(Grant No.NJLTS0027-22)The opinions expressed in this study are the authors alone,and do not represent the U.S.Depart-ment of HUD’s opinions.
文摘This paper addresses urban sustainability challenges amid global urbanization, emphasizing the need for innova tive approaches aligned with the Sustainable Development Goals. While traditional tools and linear models offer insights, they fall short in presenting a holistic view of complex urban challenges. System dynamics (SD) models that are often utilized to provide holistic, systematic understanding of a research subject, like the urban system, emerge as valuable tools, but data scarcity and theoretical inadequacy pose challenges. The research reviews relevant papers on recent SD model applications in urban sustainability since 2018, categorizing them based on nine key indicators. Among the reviewed papers, data limitations and model assumptions were identified as ma jor challenges in applying SD models to urban sustainability. This led to exploring the transformative potential of big data analytics, a rare approach in this field as identified by this study, to enhance SD models’ empirical foundation. Integrating big data could provide data-driven calibration, potentially improving predictive accuracy and reducing reliance on simplified assumptions. The paper concludes by advocating for new approaches that reduce assumptions and promote real-time applicable models, contributing to a comprehensive understanding of urban sustainability through the synergy of big data and SD models.
文摘Beijing defines a new model for digital-friendly cities where technology meets people’s needs and global cooperation drives inclusivity.THE 6,000-square-meter Beijing FUN Digital Complex,a vibrant pedestrian street in the historic Qianmen area of central Beijing,was bustling with visitors during the 2025 Beijing Digital Economy Experience Week,held from June 27 to July 5.Some climbed into a full-size C919 flight simulator,while others put on a domestically-made 8K VR headset to test a real-time voice-interaction model,immersing themselves in a dazzling virtual world.
文摘This century's rapid urbanization has disrupted urban governance,sustainability,and resource management.The Internet of Things(IoT)and 5G have the potential to transform smart cities through real-time data processing,enhanced connectivity,and sustainable urban design.This study investigates the potential of 5G connectivity with the IoT's hierarchical framework to enhance public service provision,mitigate environmental effects,and optimize urban resource management.The article asserts that these technologies can enhance urban operations by tackling scalability,interoperability,and security issues.The research employs case studies from Singapore and Barcelona.The document moreover analyzes AI-driven security systems,6G networks,and the contributions of IoT and 5G to the advancement of a circular economy.The essay asserts that the growth of smart cities necessitates robust policy frameworks to guarantee equitable access,data protection,and ethical considerations.This study integrates prior research with practical experiences to tackle data-informed municipal governance and urban innovation.The importance of policy in fostering inclusive and sustainable urban futures is emphasized.
文摘The convergence of Internet of things(IoT)and 5G holds immense potential for transforming industries by enabling real-time,massive-scale connectivity and automation.However,the growing number of devices connected to the IoT systems demands a communication network capable of handling vast amounts of data with minimal delay.These generated enormous complex,high-dimensional,high-volume,and high-speed data also brings challenges on its storage,transmission,processing,and energy cost,due to the limited computing capabilities,battery capacity,memory,and energy utilization of current IoT networks.In this paper,a seamless architecture by combining mobile and cloud computing is proposed.It can agilely bargain with 5G-IoT devices,sensor nodes,and mobile computing in a distributed manner,enabling minimized energy cost,high interoperability,and high scalability as well as overcoming the memory constraints.An artificial intelligence(AI)-powered green and energy-efficient architecture is then proposed for 5G-IoT systems and sustainable smart cities.The experimental results reveal that the proposed approach dramatically reduces the transmitted data volume and power consumption and yields superior results regarding interoperability,compression ratio,and energy saving.This is especially critical in enabling the deployment of 5G and even 6G wireless systems for smart cities.
基金supported by King Saud University,Riyadh,Saudi Arabia,through Researchers Supporting Project number RSP2025R498.
文摘The exponential growth of the Internet of Things(IoT)has revolutionized various domains such as healthcare,smart cities,and agriculture,generating vast volumes of data that require secure processing and storage in cloud environments.However,reliance on cloud infrastructure raises critical security challenges,particularly regarding data integrity.While existing cryptographic methods provide robust integrity verification,they impose significant computational and energy overheads on resource-constrained IoT devices,limiting their applicability in large-scale,real-time scenarios.To address these challenges,we propose the Cognitive-Based Integrity Verification Model(C-BIVM),which leverages Belief-Desire-Intention(BDI)cognitive intelligence and algebraic signatures to enable lightweight,efficient,and scalable data integrity verification.The model incorporates batch auditing,reducing resource consumption in large-scale IoT environments by approximately 35%,while achieving an accuracy of over 99.2%in detecting data corruption.C-BIVM dynamically adapts integrity checks based on real-time conditions,optimizing resource utilization by minimizing redundant operations by more than 30%.Furthermore,blind verification techniques safeguard sensitive IoT data,ensuring privacy compliance by preventing unauthorized access during integrity checks.Extensive experimental evaluations demonstrate that C-BIVM reduces computation time for integrity checks by up to 40%compared to traditional bilinear pairing-based methods,making it particularly suitable for IoT-driven applications in smart cities,healthcare,and beyond.These results underscore the effectiveness of C-BIVM in delivering a secure,scalable,and resource-efficient solution tailored to the evolving needs of IoT ecosystems.
基金Under the auspices of National Natural Science Foundation of China(No.42330510,42471245,52478061,52478060)。
文摘The smart city is not only a crucial means of promoting sustainable development,but also a strategic approach to advancing scientific urban development and governance on a global scale.Based on a textual co-occurrence analysis of policy documents,this study examines the components,development pathways,and models of smart cities in China.It identifies three distinct phases in the development process:the information project-driven phase,the holistic development phase,and the collaborative development phase.Overall,the development of smart cities in China-characterized by a strong focus on information technology projects-was primarily initiated by both central and local governments.Reflecting on the informatization process and the trajectory of smart city development in China since the mid-1990s,this paper argues that smart city initiatives must evolve beyond a purely technology-driven framework toward a more human-centered approach.Against the backdrop of ongoing urbanization and urban transformation in China,this study proposes future development strategies that integrate top-down government planning with bottom-up public engagement.It advocates for a comprehensive framework that integrates technology,human needs,and spatial planning,emphasizing the spatiotemporal coordination between technological implementation and stakeholder demands.This research offers valuable insights and strategic guidance for the future development of smart cities worldwide.
文摘The world is being swept by the wave of smart cities,and Geographic Information System(GIS)technology plays a key role in it.This article explores the application status and development trend of GIS technology in urban planning from the perspective of smart cities.By sorting out the application value of GIS in urban strategic layout,multi system collaboration,and precise services,it explains the necessity of promoting the transformation of planning towards intelligence.We have summarized and organized the practical points of GIS in areas such as overall planning,underground space,landscape,and transportation,including spatial simulation,3D management,ecological integration,and dynamic optimization.Looking ahead to the future,the cross integration of GIS with artificial intelligence,3D modeling,carbon neutrality monitoring,data collaboration,and other technologies will drive planning towards a new stage of development that is more intelligent,refined,open,and green.With the empowerment of GIS,we aim to create sustainable cities that are resilient,low-carbon,and inclusive,making urban life better.
文摘Internet of Things(IoT)technology has brought about significant new changes to residents’lives,prompting changes in management models across various industries and promoting the overall intelligence of urban construction.Especially in the context of continuous technological development,information sensor devices can be effectively utilized to connect multiple dimensions in urban construction,enhancing the intelligence level of cities in China.This paper mainly elaborates on the application significance of IoT technology in smart cities and proposes corresponding measures from aspects such as smart transportation systems,intelligent public utility management,urban safety and monitoring,environmental monitoring,and sustainability,providing references for relevant personnel.
文摘As urbanization process has been and will be happening in an unprecedented scale worldwide,strong requirements from academic research and practical fields for smart management and intelligent planning of cities are pressing to handle increasing demands of infrastructure and potential risks of inhabitants’agglomeration in disaster management.Geospatial data and geographic information systems(GISs)are essential components for building smart cities in a basic way that maps the physical world into virtual environment as a referencing framework.On higher level,GIS has been becoming very important in smart cities on different sectors.In the digital city era,digital maps and geospatial databases have long been integrated in workflows in land management,urban planning and transportation in government.People have anticipated GIS to be more powerful not only as an archival and data management tool but also as spatial models for supporting decision-making in intelligent cities.Successful applications have been developed in private and public organizations by using GIS as a platform for data integration,a system for geospatial analysis and collection of models for visualization and decision-making.Location-based services on smart mobile devices in ubiquitous telecommunication networks are now an indispensable function that expands knowledge of the nature and connections among people.On data side,crowd-sourcing,real-time urban sensing and true 3-dimensional(3D)models and visualization have provided more advantages of GIS to final users and at the same time challenged current available solutions and technologies of data handling,visualization and human–computer interaction.On the technological side,Web 2.0 participatory applications provide the framework and environment for GIS to closer link to photogrammetry and computer vision,which empowers smart devices more capabilities.How to manage big geo-tagged data volumes collected by numerous sensors and implement professional GIS functions in a cloud computing environment are urgent questions to facilitate smart cities management.This paper reviews advancements of GIS in the management of cities as information systems to facilitate urban modelling and decision-making,as referencing basis to integrate social network media,and concludes that an interdisciplinary urban GIS is needed to support development of smart cities.We take Singapore as a case of GIS pervasive applications,which has strategically made a master plan of national information infrastructure and has been implementing geospatial collaboration environments for public and private sectors.
文摘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.
基金the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fasttrack Research Funding Program.
文摘Wireless sensor networks(WSNs)and Internet of Things(IoT)have gained more popularity in recent years as an underlying infrastructure for connected devices and sensors in smart cities.The data generated from these sensors are used by smart cities to strengthen their infrastructure,utilities,and public services.WSNs are suitable for long periods of data acquisition in smart cities.To make the networks of smart cities more reliable for sensitive information,the blockchain mechanism has been proposed.The key issues and challenges of WSNs in smart cities is efficiently scheduling the resources;leading to extending the network lifetime of sensors.In this paper,a linear network coding(LNC)for WSNs with blockchain-enabled IoT devices has been proposed.The consumption of energy is reduced for each node by applying LNC.The efficiency and the reliability of the proposed model are evaluated and compared to those of the existing models.Results from the simulation demonstrate that the proposed model increases the efficiency in terms of the number of live nodes,packet delivery ratio,throughput,and the optimized residual energy compared to other current techniques.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP.1/282/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R191),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In recent years,Software Defined Networking(SDN)has become an important candidate for communication infrastructure in smart cities.It produces a drastic increase in the need for delivery of video services that are of high resolution,multiview,and large-scale in nature.However,this entity gets easily influenced by heterogeneous behaviour of the user’s wireless link features that might reduce the quality of video stream for few or all clients.The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences.Besides,multicast routing protocol with multiple constraints in terms of Quality of Service(QoS)is a Nondeterministic Polynomial time(NP)hard problem which can be solved only with the help of metaheuristic optimization algorithms.With this motivation,the current research paper presents a new Improved BlackWidow Optimization with Levy Distribution model(IBWO-LD)-based multicast routing protocol for smart cities.The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive.Besides,a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints.A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects.The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods.
文摘In this paper, a brief survey of smart citiy projects in Europe is presented. This survey shows the extent of transport and logistics in smart cities. We concentrate on a smart city project we have been working on that is related to A Logistic Mobile Application （ALMA）. The application is based on Internet of Things and combines a communication infrastructure and a High Performance Computing infrastructure in order to deliver mobile logistic services with high quality of service and adaptation to the dynamic nature of logistic operations.
文摘Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy management.In smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart grids.In complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research challenge.These challenges can be resolved through various machine learning techniques and data analytics.In this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart grid.The proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution companies.In the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm optimization.The simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.
基金Hunan Provincial Science and Technology Innovation Leader Project,Grant/Award Number:2021RC4025National Natural ScienceFoundation of China,Grant/Award Number:51808209Hunan Provincial Innovation Foundation for Postgraduate,Grant/Award Number:QL20210106.
文摘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.
文摘The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas.This problem is an NP(nondeterministic polynomial time)-hard problem because of involving many objective functions,many constraints,and complex search space.Moreover,this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses.Different types ofMulti-Objective Optimization Algorithms(MOOAs)based on Artificial Intelligence(AI)have been frequently employed,but their ability and performance have not been evaluated and compared properly.This paper aims to employ and compare three commonly used MOOAs i.e.NSGA-II,MOPSO,and MOEA/D in urban land-use allocation problems.Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages.The objective functions of this study are compatibility,dependency,suitability,and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment.Evaluation of results is based on the dispersion of the solutions,diversity of the solutions’space,and comparing the number of dominant solutions in Pareto-Fronts.The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses.However,the run time of NSGA-II is the worst,related to the Diversity Metric(DM)which represents the regularity of the distance between solutions at the highest degree.Moreover,MOPSO provides the best Scattering Diversity Metric(SDM)which shows the diversity of solutions in the solution space.Furthermore,In terms of algorithm execution time,MOEA/D performed better than the other two.So,Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems.
文摘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.
