Each morning at Yangluo Port in Wuhan,Hubei Province,the all-electric cargo vessel Huahang Xinneng No.1 completes a battery swap in under 10 minutes before returning to service with nearly 8,000 kWh of power onboard。
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
The Smart Era urgently demands small-size, low-energy consuming and multi-functional devices which can satisfy versatile application scenarios, including autonomous systems, wireless sensor networks,biomedical equipme...The Smart Era urgently demands small-size, low-energy consuming and multi-functional devices which can satisfy versatile application scenarios, including autonomous systems, wireless sensor networks,biomedical equipment, wearable gadgets, and the Internet of Things.This overwhelming trend has drawn much attention and stimulates intensive collaborative efforts spanning diverse fundamental and applied research related to energy generation-harvesting-storage-managementapplications at the small scale. For instance, on one hand.展开更多
The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats,making intrusion detection a critical aspect of ensuring their secure operation.Tradit...The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats,making intrusion detection a critical aspect of ensuring their secure operation.Traditional intrusion detection systems have limitations in terms of centralized architecture,lack of transparency,and vulnerability to single points of failure.This is where the integration of blockchain technology with signature-based intrusion detection can provide a robust and decentralized solution for securing smart transportation systems.This study tackles the issue of database manipulation attacks in smart transportation networks by proposing a signaturebased intrusion detection system.The introduced signature facilitates accurate detection and systematic classification of attacks,enabling categorization according to their severity levels within the transportation infrastructure.Through comparative analysis,the research demonstrates that the blockchain-based IDS outperforms traditional approaches in terms of security,resilience,and data integrity.展开更多
Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring ...Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring the security of Industrial Control Production Systems(ICPSs)has become a critical challenge.These systems are highly vulnerable to attacks such as denial-of-service(DoS),eclipse,and Sybil attacks,which can significantly disrupt industrial operations.This work proposes an effective protection strategy using an Artificial Intelligence(AI)-enabled Smart Contract(SC)framework combined with the Heterogeneous Barzilai-Borwein Support Vector(HBBSV)method for industrial-based CPS environments.The approach reduces run time and minimizes the probability of attacks.Initially,secured ICPSs are achieved through a comprehensive exchange of views on production plant strategies for condition monitoring using SC and blockchain(BC)integrated within a BC network.The SC executes the HBBSV strategy to verify the security consensus.The Barzilai-Borwein Support Vectorized algorithm computes abnormal attack occurrence probabilities to ensure that components operate within acceptable production line conditions.When a component remains within these conditions,no security breach occurs.Conversely,if a component does not satisfy the condition boundaries,a security lapse is detected,and those components are isolated.The HBBSV method thus strengthens protection against DoS,eclipse,and Sybil attacks.Experimental results demonstrate that the proposed HBBSV approach significantly improves security by enhancing authentication accuracy while reducing run time and authentication time compared to existing techniques.展开更多
The smart meter communication system has substantial application value for the construction and upgrading of the entire power system.The deployment of the transmitter(Tx)of the smart meter system in the residential sc...The smart meter communication system has substantial application value for the construction and upgrading of the entire power system.The deployment of the transmitter(Tx)of the smart meter system in the residential scenarios is vexed by the need for more theoretical support.This paper mainly studies the communication channel between the Tx at semibasement and receiver(Rx)at outdoor.The design of an effective communication system relies on an accurate understanding of channel characteristics.Channel measurements and ray-tracing channel modeling are conducted to obtain channel data.The influence of different positions at same semi-basement is studied.Typical channel characteristics are analyzed,such as power delay profile(PDP),power angular profile(PAP),root-mean-square(RMS)delay spread(DS),channel capacity,received power,and path loss.The influence of different semi-basement placements and different floor heights is also compared.Besides,the channel measurements and simulation data fit well,which can illustrate the validity and reliability of the acquired channel data.This paper can provide theoretical support for the design and optimization of smart meter communication systems in semi-basement scenarios.展开更多
As urbanization continues to accelerate,the challenges associated with managing transportation in metropolitan areas become increasingly complex.The surge in population density contributes to traffic congestion,impact...As urbanization continues to accelerate,the challenges associated with managing transportation in metropolitan areas become increasingly complex.The surge in population density contributes to traffic congestion,impacting travel experiences and posing safety risks.Smart urban transportation management emerges as a strategic solution,conceptualized here as a multidimensional big data problem.The success of this strategy hinges on the effective collection of information from diverse,extensive,and heterogeneous data sources,necessitating the implementation of full⁃stack Information and Communication Technology(ICT)solutions.The main idea of the work is to investigate the current technologies of Intelligent Transportation Systems(ITS)and enhance the safety of urban transportation systems.Machine learning models,trained on historical data,can predict traffic congestion,allowing for the implementation of preventive measures.Deep learning architectures,with their ability to handle complex data representations,further refine traffic predictions,contributing to more accurate and dynamic transportation management.The background of this research underscores the challenges posed by traffic congestion in metropolitan areas and emphasizes the need for advanced technological solutions.By integrating GPS and GIS technologies with machine learning algorithms,this work aims to pay attention to the development of intelligent transportation systems that not only address current challenges but also pave the way for future advancements in urban transportation management.展开更多
Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e....Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.展开更多
The rise of wearable electronics and intelligent robotics has created an urgent demand for tactile sensors that are soft,biocompatible,and responsive.Hydrogels,with high water content and mechanical compliance such as...The rise of wearable electronics and intelligent robotics has created an urgent demand for tactile sensors that are soft,biocompatible,and responsive.Hydrogels,with high water content and mechanical compliance such as biological tissues,provide a unique platform for constructing next-generation tactile sensors that mimic human skin’s sensory functions.This paper surveys recent progress in smart hydrogel tactile sensors and systems from fundamental concepts to practical applications.Beyond molecular structural design and material selection,we focus on the discussion and summary of the key sensing mechanisms,including triboelectric,piezoresistive,piezoelectric,piezoionic,and piezocapacitive modes.We also discuss material innovations such as ionic hydrogels,dual-conductive networks,zwitterionic matrices,and nanocomposite reinforcement,highlighting strategies to improve sensitivity,durability,and multifunctionality.Finally,the challenges and possible future directions for smart hydrogel tactile systems are outlined.展开更多
The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an over...The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an overwhelming tendency,providing powerful tools for remote health monitoring and personal health management.Among many candidates,two-dimensional(2D)materials stand out due to several exotic mechanical,electrical,optical,and chemical properties that can be efficiently integrated into atomic-thin films.While previous reviews on 2D materials for biodevices primarily focus on conventional configurations and materials like graphene,the rapid development of new 2D materials with exotic properties has opened up novel applications,particularly in smart interaction and integrated functionalities.This review aims to consolidate recent progress,highlight the unique advantages of 2D materials,and guide future research by discussing existing challenges and opportunities in applying 2D materials for smart wearable biodevices.We begin with an in-depth analysis of the advantages,sensing mechanisms,and potential applications of 2D materials in wearable biodevice fabrication.Following this,we systematically discuss state-of-the-art biodevices based on 2D materials for monitoring various physiological signals within the human body.Special attention is given to showcasing the integration of multi-functionality in 2D smart devices,mainly including self-power supply,integrated diagnosis/treatment,and human–machine interaction.Finally,the review concludes with a concise summary of existing challenges and prospective solutions concerning the utilization of2D materials for advanced biodevices.展开更多
The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability an...The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.展开更多
Precision actuation is a foundational technology in high-end equipment domains,where stroke,velocity,and accuracy are critical for processing and/or detection quality,precision in spacecraft flight trajectories,and ac...Precision actuation is a foundational technology in high-end equipment domains,where stroke,velocity,and accuracy are critical for processing and/or detection quality,precision in spacecraft flight trajectories,and accuracy in weapon system strikes.Piezoelectric actuators(PEAs),known for their nanometer-level precision,flexible stroke,resistance to electromagnetic interference,and scalable structure,have been widely adopted across various fields.Therefore,this study focuses on extreme scenarios involving ultra-high precision(micrometer and beyond),minuscule scales,and highly complex operational conditions.It provides a comprehensive overview of the types,working principles,advantages,and disadvantages of PEAs,along with their potential applications in piezo-actuated smart mechatronic systems(PSMSs).To address the demands of extreme scenarios in high-end equipment fields,we have identified five representative application areas:positioning and alignment,biomedical device configuration,advanced manufacturing and processing,vibration mitigation,micro robot system.Each area is further divided into specific subcategories,where we explore the underlying relationships,mechanisms,representative schemes,and characteristics.Finally,we discuss the challenges and future development trends related to PEAs and PSMSs.This work aims to showcase the latest advancements in the application of PEAs and provide valuable guidance for researchers in this field.展开更多
Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and ...Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.展开更多
This paper focuses on the study of mechanical design and control measures in smart home systems.First of all,it elaborates on the theoretical foundation of mechatronics technology,including its multidisciplinary integ...This paper focuses on the study of mechanical design and control measures in smart home systems.First of all,it elaborates on the theoretical foundation of mechatronics technology,including its multidisciplinary integration characteristics,system design principles,and constituent elements.It then reviews the research progress in this field,followed by a detailed analysis of mechatronics design in systems such as smart lighting and smart security,as well as the application of control algorithms and communication protocols in smart homes.Finally,it discusses challenges such as system compatibility and data security risks,proposing corresponding solutions to provide theoretical and practical references for the development of smart home systems.展开更多
Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace.Network intrusion detection is usually regarded as an efficient means of dealing with security attacks.Many ways have been...Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace.Network intrusion detection is usually regarded as an efficient means of dealing with security attacks.Many ways have been presented,utilizing various strategies and focusing on different types of visitors.Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development.Despite extensive research on anomaly-based network detection,there is still a lack of comprehensive literature reviews covering current methodologies and datasets.Despite the substantial research into anomaly-based network intrusion detection algorithms,there is a dearth of a research evaluation of new methodologies and datasets.We explore and evaluate 50 highest publications on anomaly-based intrusion detection using an in-depth review of related literature techniques.Our work thoroughly explores the technological environment of the subject in order to help future research in this sector.Our examination is carried out from the relevant angles:application areas,data preprocessing and threat detection approaches,assessment measures,and datasets.We select unresolved research difficulties and underexplored research areas from every viewpoint recommendation of the study.Finally,we outline five potentially increased research areas for the future.展开更多
Currently,most conventional street lighting systems use a constant light mode throughout the entire night,fromsunset to sunrise,which results in high energy consumption and maintenance costs.Furthermore,scientific res...Currently,most conventional street lighting systems use a constant light mode throughout the entire night,fromsunset to sunrise,which results in high energy consumption and maintenance costs.Furthermore,scientific research predicts that energy consumption for street lighting will increase in the coming years due to growing demand and rising electricity prices.The dimming strategy is a current trend and a key concept in smart street lighting systems.It involves turning on the road lights only when a vehicle or pedestrian is detected;otherwise,the control system reduces the light intensity of the lamps.Power control is generally implemented using artificial intelligence algorithms such as fuzzy logic,artificial neural networks,or swarm intelligence to manage different events.In our project,the dimming strategy was utilized to reduce costs and energy consumption by at least 48%.This research proposes a standalone photovoltaic plant(SPP)to power a smart street lighting system,which can replace individual solar street lights in a small neighborhood in southwestern Algeria.This design offers advantages such as lower costs and the option to add additional loads a DC pump,a domestic power supply,or technical services to operate the photovoltaic system during the day after charging the storage batteries,especially when there is monthly energy surplus.The energy surplus analyzed in our project ranges from 804 W in January to 4 kW in June,generated by the entire PV installation.To demonstrate the feasibility and reliability of this system,we studied the implementation of the standalone photovoltaic plant,energymanagement,and economic analysis using the Particle SwarmOptimization technique under MATLAB software.展开更多
The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.Thi...The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.展开更多
Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity...Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid.The design of any DSM system using a wireless network must consider the wireless link impairments,which is missing in existing literature.In this paper,we propose a DSM system using a Real-Time Pricing(RTP)mechanism and a wireless Neighborhood Area Network(NAN)with data transfer uncertainty.A Zigbee-based Internet of Things(IoT)model is considered for the communication infrastructure of the NAN.A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link.The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users,decision-makers,and energy providers.A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices.Simulation results indicate that the proposed system benefits users and energy providers.Furthermore,experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN,which can substantially impact the performance of the proposed DSM system.Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price,user welfare,and provider welfare.展开更多
文摘Each morning at Yangluo Port in Wuhan,Hubei Province,the all-electric cargo vessel Huahang Xinneng No.1 completes a battery swap in under 10 minutes before returning to service with nearly 8,000 kWh of power onboard。
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The Smart Era urgently demands small-size, low-energy consuming and multi-functional devices which can satisfy versatile application scenarios, including autonomous systems, wireless sensor networks,biomedical equipment, wearable gadgets, and the Internet of Things.This overwhelming trend has drawn much attention and stimulates intensive collaborative efforts spanning diverse fundamental and applied research related to energy generation-harvesting-storage-managementapplications at the small scale. For instance, on one hand.
基金supported by the National Research Foundation(NRF),Republic of Korea,under project BK21 FOUR(4299990213939).
文摘The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats,making intrusion detection a critical aspect of ensuring their secure operation.Traditional intrusion detection systems have limitations in terms of centralized architecture,lack of transparency,and vulnerability to single points of failure.This is where the integration of blockchain technology with signature-based intrusion detection can provide a robust and decentralized solution for securing smart transportation systems.This study tackles the issue of database manipulation attacks in smart transportation networks by proposing a signaturebased intrusion detection system.The introduced signature facilitates accurate detection and systematic classification of attacks,enabling categorization according to their severity levels within the transportation infrastructure.Through comparative analysis,the research demonstrates that the blockchain-based IDS outperforms traditional approaches in terms of security,resilience,and data integrity.
文摘Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring the security of Industrial Control Production Systems(ICPSs)has become a critical challenge.These systems are highly vulnerable to attacks such as denial-of-service(DoS),eclipse,and Sybil attacks,which can significantly disrupt industrial operations.This work proposes an effective protection strategy using an Artificial Intelligence(AI)-enabled Smart Contract(SC)framework combined with the Heterogeneous Barzilai-Borwein Support Vector(HBBSV)method for industrial-based CPS environments.The approach reduces run time and minimizes the probability of attacks.Initially,secured ICPSs are achieved through a comprehensive exchange of views on production plant strategies for condition monitoring using SC and blockchain(BC)integrated within a BC network.The SC executes the HBBSV strategy to verify the security consensus.The Barzilai-Borwein Support Vectorized algorithm computes abnormal attack occurrence probabilities to ensure that components operate within acceptable production line conditions.When a component remains within these conditions,no security breach occurs.Conversely,if a component does not satisfy the condition boundaries,a security lapse is detected,and those components are isolated.The HBBSV method thus strengthens protection against DoS,eclipse,and Sybil attacks.Experimental results demonstrate that the proposed HBBSV approach significantly improves security by enhancing authentication accuracy while reducing run time and authentication time compared to existing techniques.
基金supported by the Natural Science Foundation of Shandong Province under Grant ZR2024MF062the open research fund of National Mobile Communications Research Laboratory,Southeast University under Grants 2025D03+1 种基金the Future Plan Program for Young Scholars of Shandong University,and the Innovation and Technology Support Program for Young Scholars of Colleges and Universities in Shandong Province under Grant 2022KJ009The B6G R&D Group in Shandong University is greatly thanked for channel measurements.
文摘The smart meter communication system has substantial application value for the construction and upgrading of the entire power system.The deployment of the transmitter(Tx)of the smart meter system in the residential scenarios is vexed by the need for more theoretical support.This paper mainly studies the communication channel between the Tx at semibasement and receiver(Rx)at outdoor.The design of an effective communication system relies on an accurate understanding of channel characteristics.Channel measurements and ray-tracing channel modeling are conducted to obtain channel data.The influence of different positions at same semi-basement is studied.Typical channel characteristics are analyzed,such as power delay profile(PDP),power angular profile(PAP),root-mean-square(RMS)delay spread(DS),channel capacity,received power,and path loss.The influence of different semi-basement placements and different floor heights is also compared.Besides,the channel measurements and simulation data fit well,which can illustrate the validity and reliability of the acquired channel data.This paper can provide theoretical support for the design and optimization of smart meter communication systems in semi-basement scenarios.
文摘As urbanization continues to accelerate,the challenges associated with managing transportation in metropolitan areas become increasingly complex.The surge in population density contributes to traffic congestion,impacting travel experiences and posing safety risks.Smart urban transportation management emerges as a strategic solution,conceptualized here as a multidimensional big data problem.The success of this strategy hinges on the effective collection of information from diverse,extensive,and heterogeneous data sources,necessitating the implementation of full⁃stack Information and Communication Technology(ICT)solutions.The main idea of the work is to investigate the current technologies of Intelligent Transportation Systems(ITS)and enhance the safety of urban transportation systems.Machine learning models,trained on historical data,can predict traffic congestion,allowing for the implementation of preventive measures.Deep learning architectures,with their ability to handle complex data representations,further refine traffic predictions,contributing to more accurate and dynamic transportation management.The background of this research underscores the challenges posed by traffic congestion in metropolitan areas and emphasizes the need for advanced technological solutions.By integrating GPS and GIS technologies with machine learning algorithms,this work aims to pay attention to the development of intelligent transportation systems that not only address current challenges but also pave the way for future advancements in urban transportation management.
文摘Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.
基金supported by the National Key R&D Program from the Ministry of Science and Technology of China(Grant No.2024YFB3211902)the National Natural Science Foundation of China(Grant No.52173274).
文摘The rise of wearable electronics and intelligent robotics has created an urgent demand for tactile sensors that are soft,biocompatible,and responsive.Hydrogels,with high water content and mechanical compliance such as biological tissues,provide a unique platform for constructing next-generation tactile sensors that mimic human skin’s sensory functions.This paper surveys recent progress in smart hydrogel tactile sensors and systems from fundamental concepts to practical applications.Beyond molecular structural design and material selection,we focus on the discussion and summary of the key sensing mechanisms,including triboelectric,piezoresistive,piezoelectric,piezoionic,and piezocapacitive modes.We also discuss material innovations such as ionic hydrogels,dual-conductive networks,zwitterionic matrices,and nanocomposite reinforcement,highlighting strategies to improve sensitivity,durability,and multifunctionality.Finally,the challenges and possible future directions for smart hydrogel tactile systems are outlined.
基金the support from the National Natural Science Foundation of China(22272004,62272041)the Fundamental Research Funds for the Central Universities(YWF-22-L-1256)+1 种基金the National Key R&D Program of China(2023YFC3402600)the Beijing Institute of Technology Research Fund Program for Young Scholars(No.1870011182126)。
文摘The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an overwhelming tendency,providing powerful tools for remote health monitoring and personal health management.Among many candidates,two-dimensional(2D)materials stand out due to several exotic mechanical,electrical,optical,and chemical properties that can be efficiently integrated into atomic-thin films.While previous reviews on 2D materials for biodevices primarily focus on conventional configurations and materials like graphene,the rapid development of new 2D materials with exotic properties has opened up novel applications,particularly in smart interaction and integrated functionalities.This review aims to consolidate recent progress,highlight the unique advantages of 2D materials,and guide future research by discussing existing challenges and opportunities in applying 2D materials for smart wearable biodevices.We begin with an in-depth analysis of the advantages,sensing mechanisms,and potential applications of 2D materials in wearable biodevice fabrication.Following this,we systematically discuss state-of-the-art biodevices based on 2D materials for monitoring various physiological signals within the human body.Special attention is given to showcasing the integration of multi-functionality in 2D smart devices,mainly including self-power supply,integrated diagnosis/treatment,and human–machine interaction.Finally,the review concludes with a concise summary of existing challenges and prospective solutions concerning the utilization of2D materials for advanced biodevices.
基金supported by National Natural Science Foundation of China under Grant No.62372110Fujian Provincial Natural Science of Foundation under Grants 2023J02008,2024H0009.
文摘The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.
基金financially supported by the National Key R&D Program of China(Grant No.2022YFC2204203)the National Natural Science Foundation of China(Grant No.52305107)。
文摘Precision actuation is a foundational technology in high-end equipment domains,where stroke,velocity,and accuracy are critical for processing and/or detection quality,precision in spacecraft flight trajectories,and accuracy in weapon system strikes.Piezoelectric actuators(PEAs),known for their nanometer-level precision,flexible stroke,resistance to electromagnetic interference,and scalable structure,have been widely adopted across various fields.Therefore,this study focuses on extreme scenarios involving ultra-high precision(micrometer and beyond),minuscule scales,and highly complex operational conditions.It provides a comprehensive overview of the types,working principles,advantages,and disadvantages of PEAs,along with their potential applications in piezo-actuated smart mechatronic systems(PSMSs).To address the demands of extreme scenarios in high-end equipment fields,we have identified five representative application areas:positioning and alignment,biomedical device configuration,advanced manufacturing and processing,vibration mitigation,micro robot system.Each area is further divided into specific subcategories,where we explore the underlying relationships,mechanisms,representative schemes,and characteristics.Finally,we discuss the challenges and future development trends related to PEAs and PSMSs.This work aims to showcase the latest advancements in the application of PEAs and provide valuable guidance for researchers in this field.
基金supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number(R.G.P.2/93/45).
文摘Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.
文摘This paper focuses on the study of mechanical design and control measures in smart home systems.First of all,it elaborates on the theoretical foundation of mechatronics technology,including its multidisciplinary integration characteristics,system design principles,and constituent elements.It then reviews the research progress in this field,followed by a detailed analysis of mechatronics design in systems such as smart lighting and smart security,as well as the application of control algorithms and communication protocols in smart homes.Finally,it discusses challenges such as system compatibility and data security risks,proposing corresponding solutions to provide theoretical and practical references for the development of smart home systems.
文摘Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace.Network intrusion detection is usually regarded as an efficient means of dealing with security attacks.Many ways have been presented,utilizing various strategies and focusing on different types of visitors.Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development.Despite extensive research on anomaly-based network detection,there is still a lack of comprehensive literature reviews covering current methodologies and datasets.Despite the substantial research into anomaly-based network intrusion detection algorithms,there is a dearth of a research evaluation of new methodologies and datasets.We explore and evaluate 50 highest publications on anomaly-based intrusion detection using an in-depth review of related literature techniques.Our work thoroughly explores the technological environment of the subject in order to help future research in this sector.Our examination is carried out from the relevant angles:application areas,data preprocessing and threat detection approaches,assessment measures,and datasets.We select unresolved research difficulties and underexplored research areas from every viewpoint recommendation of the study.Finally,we outline five potentially increased research areas for the future.
文摘Currently,most conventional street lighting systems use a constant light mode throughout the entire night,fromsunset to sunrise,which results in high energy consumption and maintenance costs.Furthermore,scientific research predicts that energy consumption for street lighting will increase in the coming years due to growing demand and rising electricity prices.The dimming strategy is a current trend and a key concept in smart street lighting systems.It involves turning on the road lights only when a vehicle or pedestrian is detected;otherwise,the control system reduces the light intensity of the lamps.Power control is generally implemented using artificial intelligence algorithms such as fuzzy logic,artificial neural networks,or swarm intelligence to manage different events.In our project,the dimming strategy was utilized to reduce costs and energy consumption by at least 48%.This research proposes a standalone photovoltaic plant(SPP)to power a smart street lighting system,which can replace individual solar street lights in a small neighborhood in southwestern Algeria.This design offers advantages such as lower costs and the option to add additional loads a DC pump,a domestic power supply,or technical services to operate the photovoltaic system during the day after charging the storage batteries,especially when there is monthly energy surplus.The energy surplus analyzed in our project ranges from 804 W in January to 4 kW in June,generated by the entire PV installation.To demonstrate the feasibility and reliability of this system,we studied the implementation of the standalone photovoltaic plant,energymanagement,and economic analysis using the Particle SwarmOptimization technique under MATLAB software.
文摘The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.
文摘Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid.The design of any DSM system using a wireless network must consider the wireless link impairments,which is missing in existing literature.In this paper,we propose a DSM system using a Real-Time Pricing(RTP)mechanism and a wireless Neighborhood Area Network(NAN)with data transfer uncertainty.A Zigbee-based Internet of Things(IoT)model is considered for the communication infrastructure of the NAN.A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link.The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users,decision-makers,and energy providers.A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices.Simulation results indicate that the proposed system benefits users and energy providers.Furthermore,experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN,which can substantially impact the performance of the proposed DSM system.Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price,user welfare,and provider welfare.
