With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT termi...With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.展开更多
The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expo...The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistentmalware attacks.These adaptive and stealthy threats can evade conventional detection,establish remote control,propagate across devices,exfiltrate sensitive data,and compromise network integrity.This study presents a Software-Defined Internet of Things(SD-IoT)control-plane-based,AI-driven framework that integrates Gated Recurrent Units(GRU)and Long Short-TermMemory(LSTM)networks for efficient detection of evolving multi-vector,malware-driven botnet attacks.The proposed CUDA-enabled hybrid deep learning(DL)framework performs centralized real-time detection without adding computational overhead to IoT nodes.A feature selection strategy combining variable clustering,attribute evaluation,one-R attribute evaluation,correlation analysis,and principal component analysis(PCA)enhances detection accuracy and reduces complexity.The framework is rigorously evaluated using the N_BaIoT dataset under k-fold cross-validation.Experimental results achieve 99.96%detection accuracy,a false positive rate(FPR)of 0.0035%,and a detection latency of 0.18 ms,confirming its high efficiency and scalability.The findings demonstrate the framework’s potential as a robust and intelligent security solution for next-generation IoT ecosystems.展开更多
Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Lever...Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
The rapid evolution of satellite constellation projects(e.g.,SpaceX)and the standardization of 3rd Generation Partnership Project(3GPP)non-terrestrial networks(NTNs)have positioned satellite Internet networking(SIN)as...The rapid evolution of satellite constellation projects(e.g.,SpaceX)and the standardization of 3rd Generation Partnership Project(3GPP)non-terrestrial networks(NTNs)have positioned satellite Internet networking(SIN)as a cornerstone of future communication systems.The demand for ubiquitous connectivity,resilient infrastructures,and intelligent network services has never been greater,driven by applications ranging from global broadband access to emergency response and space-air-ground integration.展开更多
Background Ongoing debates question the harm of internet use with the evolving technology,as many individuals transition from regular to problematic internet use(PIU).The habenula(Hb),located between the thalamus and ...Background Ongoing debates question the harm of internet use with the evolving technology,as many individuals transition from regular to problematic internet use(PIU).The habenula(Hb),located between the thalamus and the third ventricle,is implicated in various psychiatric disorders.In addition,personality features have been suggested to play a role in the pathophysiology of PIU.Aims This study aimed to investigate Hb volumetry in individuals with subclinical PIU and the mediating effect of personality traits on this relationship.Methods 110 healthy adults in this cross-sectional study underwent structural magnetic resonance imaging.Hb segmentation was performed using a deep learning technique.The Internet Addiction Test(IAT)and the NEO Five-Factor Inventory were used to assess the PIU level and personality,respectively.Partial Spearman's correlation analyses were performed to explore the reiationships between Hb volumetry,IAT and NEO.Multiple regression analysis was applied to identify personality traits that predict IAT scores.The significant trait was then treated as a mediator between Hb volume and IAT correlation in mediation analysis with a bootstrap value of 5000.Results Relative Hb volume was negatively correlated with IAT scores(partial rho=-0.142,p=0.009).The IAT score was positively correlated with neuroticism(partial rho=0.430,p<0.001)and negatively correlated with extraversion,agreeableness and conscientiousness(partial rho=-0.213,p<0.001;partial rho=-0.279,p<0.001;and partial rho=-0.327,p<0.001).There was a significant indirect effect of Hb volume on this model(β=-0.061,p=0.048,boot 95%confidence interval:-0.149 to-0.001).Conclusions This study uncovered a crucial link between reduced Hb volume and heightened PIU.Our findings highlight neuroticism as a key risk factor for developing PIU.Moreover,neuroticism was shown to mediate the relationship between Hb volume and PIU tendency,offering valuable insight into the complexities of this interaction.展开更多
Driven by the wave of informatization and intelligence,the smart city has become a new trend of global urban development.The intelligent transformation of energy systems is of great importance to a smart city.The Inte...Driven by the wave of informatization and intelligence,the smart city has become a new trend of global urban development.The intelligent transformation of energy systems is of great importance to a smart city.The Internet helps the sustainable development of smart cities by optimizing resource allocation,improving utilization efficiency,and promoting market competition.This study analyzes the current situation and problems of energy Internet supporting smart cities and finds that policy environment,technology maturity,market demand,and industrial chain integration have a significant positive impact on its development.Based on this,relevant strategies are proposed to provide theoretical and practical guidance for the integrated development of smart cities and the energy Internet.展开更多
With the rapid development of the industrial Internet,the network security environment has become increasingly complex and variable.Intrusion detection,a core technology for ensuring the security of industrial control...With the rapid development of the industrial Internet,the network security environment has become increasingly complex and variable.Intrusion detection,a core technology for ensuring the security of industrial control systems,faces the challenge of unbalanced data samples,particularly the low detection rates for minority class attack samples.Therefore,this paper proposes a data enhancement method for intrusion detection in the industrial Internet based on a Self-Attention Wasserstein Generative Adversarial Network(SA-WGAN)to address the low detection rates of minority class attack samples in unbalanced intrusion detection scenarios.The proposed method integrates a selfattention mechanism with a Wasserstein Generative Adversarial Network(WGAN).The self-attention mechanism automatically learns important features from the input data and assigns different weights to emphasize the key features related to intrusion behaviors,providing strong guidance for subsequent data generation.The WGAN generates new data samples through adversarial training to expand the original dataset.In the SA-WGAN framework,the WGAN directs the data generation process based on the key features extracted by the self-attention mechanism,ensuring that the generated samples exhibit both diversity and similarity to real data.Experimental results demonstrate that the SA-WGAN-based data enhancement method significantly improves detection performance for attack samples from minority classes,addresses issues of insufficient data and category imbalance,and enhances the generalization ability and overall performance of the intrusion detection model.展开更多
The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent require...The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent requirements for ultra-low latency,high reliability,and robust privacy present significant challenges.Conventional centralized Federated Learning(FL)architectures struggle with latency and privacy constraints,while fully distributed FL(DFL)faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous.To address these limitations,we propose a Clustered Distributed Federated Learning(CDFL)architecture tailored for a 6G-enabled TIoT environment.Clients are grouped into clusters based on data similarity and/or geographical proximity,enabling local intra-cluster aggregation before inter-cluster model sharing.This hierarchical,peer-to-peer approach reduces communication overhead,mitigates non-IID effects,and eliminates single points of failure.By offloading aggregation to the network edge and leveraging dynamic clustering,CDFL enhances both computational and communication efficiency.Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows.Specifically,CDFL demonstrates up to a 30%reduction in training time under highly heterogeneous data distributions,indicating faster convergence.It also reduces communication overhead by approximately 40%compared to DFL.These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments.These results validate CDFL as a scalable,privacy-preserving solution for next-generation TIoT applications.展开更多
Satellite Internet(SI)provides broadband access as a critical information infrastructure in 6G.However,with the integration of the terrestrial Internet,the influx of massive terrestrial traffic will bring significant ...Satellite Internet(SI)provides broadband access as a critical information infrastructure in 6G.However,with the integration of the terrestrial Internet,the influx of massive terrestrial traffic will bring significant threats to SI,among which DDoS attack will intensify the erosion of limited bandwidth resources.Therefore,this paper proposes a DDoS attack tracking scheme using a multi-round iterative Viterbi algorithm to achieve high-accuracy attack path reconstruction and fast internal source locking,protecting SI from the source.Firstly,to reduce communication overhead,the logarithmic representation of the traffic volume is added to the digests after modeling SI,generating the lightweight deviation degree to construct the observation probability matrix for the Viterbi algorithm.Secondly,the path node matrix is expanded to multi-index matrices in the Viterbi algorithm to store index information for all probability values,deriving the path with non-repeatability and maximum probability.Finally,multiple rounds of iterative Viterbi tracking are performed locally to track DDoS attack based on trimming tracking results.Simulation and experimental results show that the scheme can achieve 96.8%tracking accuracy of external and internal DDoS attack at 2.5 seconds,with the communication overhead at 268KB/s,effectively protecting the limited bandwidth resources of SI.展开更多
Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These...Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems.This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services.The proposed method employs AWS Lambda functions with Amazon Relational Database Service(RDS)to facilitate the transmission of data collected from temperature and humidity sensors to the RDS database.In contrast,the conventional method utilizes AmazonDynamoDB for storing the same sensor data.Data were collected from 01 April 2022,to 26 August 2022,in Tokyo,Japan,focusing on temperature and relative humiditywitha resolutionof oneminute.The efficiency of the twomethods—conventional andproposed—was assessed in terms of both time and cost metrics,with a particular focus on data retrieval.The conventional method exhibited linear time complexity,leading to longer data retrieval times as the dataset grew,mainly due to DynamoDB’s pagination requirements and the parsing of payload data during the reading process.In contrast,the proposed method significantly reduced retrieval times for larger datasets by parsing payload data before writing it to the RDS database.Cost analysis revealed a savings of$1.56 per month with the adoption of the proposed approach for a 20-gigabyte database.展开更多
With the advent of the digital economy,there has been a rapid proliferation of small-scale Internet data centers(SIDCs).By leveraging their spatiotemporal load regulation potential through data workload balancing,aggr...With the advent of the digital economy,there has been a rapid proliferation of small-scale Internet data centers(SIDCs).By leveraging their spatiotemporal load regulation potential through data workload balancing,aggregated SIDCs have emerged as promising demand response(DR)resources for future power distribution systems.This paper presents an innovative framework for assessing capacity value(CV)by aggregating SIDCs participating in DR programs(SIDC-DR).Initially,we delineate the concept of CV tailored for aggregated SIDC scenarios and establish a metric for the assessment.Considering the effects of the data load dynamics,equipment constraints,and user behavior,we developed a sophisticated DR model for aggregated SIDCs using a data network aggregation method.Unlike existing studies,the proposed model captures the uncertainties associated with end tenant decisions to opt into an SIDC-DR program by utilizing a novel uncertainty modeling approach called Z-number formulation.This approach accounts for both the uncertainty in user participation intentions and the reliability of basic information during the DR process,enabling high-resolution profiling of the SIDC-DR potential in the CV evaluation.Simulation results from numerical studies conducted on a modified IEEE-33 node distribution system confirmed the effectiveness of the proposed approach and highlighted the potential benefits of SIDC-DR utilization in the efficient operation of future power systems.展开更多
Under the current background of an information society,the digital transformation of enterprises has become a necessary means to enhance the competitiveness of enterprises.This article is based on the industrial Inter...Under the current background of an information society,the digital transformation of enterprises has become a necessary means to enhance the competitiveness of enterprises.This article is based on the industrial Internet platform,the digital planning and architecture of enterprises research.First,we analyze the current challenges of digital transformation and the development opportunities brought by the industrial Internet.Then,we propose a digital planning method based on the industrial Internet platform,which takes the full connectivity of people,machine and things and intelligent decision making as the core,takes data collection,processing,analysis and application as the main line,and finally forms the top-level design of the digital transformation of enterprises.At the same time,we also built an industrial Internet platform architecture model,including the previous end perception layer,network transmission layer,platform service layer,and application innovation layer for four levels,to support enterprises in innovative applications and decision support under the industrial Internet environment.Research shows that this kind of enterprise digital planning and architecture based on an industrial Internet platform can effectively promote enterprises to achieve business model innovation,system innovation,and strengthen the flexibility and agility of enterprises to respond to market changes.The results of this research not only have important theoretical and practical significance for guiding enterprises to carry out digital planning and build an industrial Internet platform,but also provide useful reference for relevant policy formulation.展开更多
In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and ot...In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.展开更多
In the Satellite-integrated Internet of Things(S-IoT),data freshness in the time-sensitive scenarios could not be guaranteed over the timevarying topology with current distribution strategies aiming to reduce the tran...In the Satellite-integrated Internet of Things(S-IoT),data freshness in the time-sensitive scenarios could not be guaranteed over the timevarying topology with current distribution strategies aiming to reduce the transmission delay.To address this problem,in this paper,we propose an age-optimal caching distribution mechanism for the high-timeliness data collection in S-IoT by adopting a freshness metric,as called age of information(AoI)through the caching-based single-source multidestinations(SSMDs)transmission,namely Multi-AoI,with a well-designed cross-slot directed graph(CSG).With the proposed CSG,we make optimizations on the locations of cache nodes by solving a nonlinear integer programming problem on minimizing Multi-AoI.In particular,we put up forward three specific algorithms respectively for improving the Multi-AoI,i.e.,the minimum queuing delay algorithm(MQDA)based on node deviation from average level,the minimum propagation delay algorithm(MPDA)based on the node propagation delay reduction,and a delay balanced algorithm(DBA)based on node deviation from average level and propagation delay reduction.The simulation results show that the proposed mechanism can effectively improve the freshness of information compared with the random selection algorithm.展开更多
With the introduction of 5G,users and devices can access the industrial network from anywhere in the world.Therefore,traditional perimeter-based security technologies for industrial networks can no longer work well.To...With the introduction of 5G,users and devices can access the industrial network from anywhere in the world.Therefore,traditional perimeter-based security technologies for industrial networks can no longer work well.To solve this problem,a new security model called Zero Trust(ZT)is desired,which believes in“never trust and always verify”.Every time the asset in the industrial network is accessed,the subject is authenticated and its trustworthiness is assessed.In this way,the asset in industrial network can be well protected,whether the subject is in the internal network or the external network.However,in order to construct the zero trust model in the 5G Industrial Internet collaboration system,there are still many problems to be solved.In this paper,we first introduce the security issues in the 5G Industrial Internet collaboration system,and illustrate the zero trust architecture.Then,we analyze the gap between existing security techniques and the zero trust architecture.Finally,we discuss several potential security techniques that can be used to implement the zero trust model.The purpose of this paper is to point out the further direction for the realization of the Zero Trust Architecture(ZTA)in the 5G Industrial Internet collaboration system.展开更多
The present paper explores the ideological and political education work of university counselors in Xinjiang under the mobile Internet environment.It elaborates on the significant value of this work in adapting to con...The present paper explores the ideological and political education work of university counselors in Xinjiang under the mobile Internet environment.It elaborates on the significant value of this work in adapting to contemporary development trends,promoting cultural integration,and safeguarding regional stability.The study analyzes various challenges encountered,including difficulties in information screening,limitations in online communication,and insufficient digital literacy.Corresponding countermeasures are proposed with the aim of enhancing the effectiveness of ideological and political education in Xinjiang s higher education institutions,fostering healthy student development,and facilitating regional progress.展开更多
基金supported by National Key R&D Program of China(No.2022YFB3105101).
文摘With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting ProjectNumber(PNURSP2025R97),PrincessNourah bint AbdulrahmanUniversity,Riyadh,Saudi Arabia.
文摘The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistentmalware attacks.These adaptive and stealthy threats can evade conventional detection,establish remote control,propagate across devices,exfiltrate sensitive data,and compromise network integrity.This study presents a Software-Defined Internet of Things(SD-IoT)control-plane-based,AI-driven framework that integrates Gated Recurrent Units(GRU)and Long Short-TermMemory(LSTM)networks for efficient detection of evolving multi-vector,malware-driven botnet attacks.The proposed CUDA-enabled hybrid deep learning(DL)framework performs centralized real-time detection without adding computational overhead to IoT nodes.A feature selection strategy combining variable clustering,attribute evaluation,one-R attribute evaluation,correlation analysis,and principal component analysis(PCA)enhances detection accuracy and reduces complexity.The framework is rigorously evaluated using the N_BaIoT dataset under k-fold cross-validation.Experimental results achieve 99.96%detection accuracy,a false positive rate(FPR)of 0.0035%,and a detection latency of 0.18 ms,confirming its high efficiency and scalability.The findings demonstrate the framework’s potential as a robust and intelligent security solution for next-generation IoT ecosystems.
基金supported in part by National key R&D projects(2024YFB4207203)National Natural Science Foundation of China(52401376)+3 种基金the Zhejiang Provincial Natural Science Foundation of China under Grant(No.LTGG24F030004)Hangzhou Key Scientific Research Plan Project(2024SZD1A24)“Pioneer”and“Leading Goose”R&DProgramof Zhejiang(2024C03254,2023C03154)Jiangxi Provincial Gan-Po Elite Support Program(Major Academic and Technical Leaders Cultivation Project,20243BCE51180).
文摘Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
文摘The rapid evolution of satellite constellation projects(e.g.,SpaceX)and the standardization of 3rd Generation Partnership Project(3GPP)non-terrestrial networks(NTNs)have positioned satellite Internet networking(SIN)as a cornerstone of future communication systems.The demand for ubiquitous connectivity,resilient infrastructures,and intelligent network services has never been greater,driven by applications ranging from global broadband access to emergency response and space-air-ground integration.
基金funded by a Grant-in-Aid for Scientific Research(B)(Japan Society for The Promotion of Science,21H02849)Grant-in-Aid for Scientific Research(C)(Japan Society for The Promotion of Science,23K07013)+2 种基金Grant-in-Aid for Transformative Research Areas(A)(Japan Society for The Promotion of Science,JP21H05173)Grant-in-Aid by the Smoking Research FoundationGrant-in-Aid by the Telecommunications Advancement Foundation.
文摘Background Ongoing debates question the harm of internet use with the evolving technology,as many individuals transition from regular to problematic internet use(PIU).The habenula(Hb),located between the thalamus and the third ventricle,is implicated in various psychiatric disorders.In addition,personality features have been suggested to play a role in the pathophysiology of PIU.Aims This study aimed to investigate Hb volumetry in individuals with subclinical PIU and the mediating effect of personality traits on this relationship.Methods 110 healthy adults in this cross-sectional study underwent structural magnetic resonance imaging.Hb segmentation was performed using a deep learning technique.The Internet Addiction Test(IAT)and the NEO Five-Factor Inventory were used to assess the PIU level and personality,respectively.Partial Spearman's correlation analyses were performed to explore the reiationships between Hb volumetry,IAT and NEO.Multiple regression analysis was applied to identify personality traits that predict IAT scores.The significant trait was then treated as a mediator between Hb volume and IAT correlation in mediation analysis with a bootstrap value of 5000.Results Relative Hb volume was negatively correlated with IAT scores(partial rho=-0.142,p=0.009).The IAT score was positively correlated with neuroticism(partial rho=0.430,p<0.001)and negatively correlated with extraversion,agreeableness and conscientiousness(partial rho=-0.213,p<0.001;partial rho=-0.279,p<0.001;and partial rho=-0.327,p<0.001).There was a significant indirect effect of Hb volume on this model(β=-0.061,p=0.048,boot 95%confidence interval:-0.149 to-0.001).Conclusions This study uncovered a crucial link between reduced Hb volume and heightened PIU.Our findings highlight neuroticism as a key risk factor for developing PIU.Moreover,neuroticism was shown to mediate the relationship between Hb volume and PIU tendency,offering valuable insight into the complexities of this interaction.
基金Research and Innovation Team Building Project of Qingdao City University(QCU23TDKJO1)。
文摘Driven by the wave of informatization and intelligence,the smart city has become a new trend of global urban development.The intelligent transformation of energy systems is of great importance to a smart city.The Internet helps the sustainable development of smart cities by optimizing resource allocation,improving utilization efficiency,and promoting market competition.This study analyzes the current situation and problems of energy Internet supporting smart cities and finds that policy environment,technology maturity,market demand,and industrial chain integration have a significant positive impact on its development.Based on this,relevant strategies are proposed to provide theoretical and practical guidance for the integrated development of smart cities and the energy Internet.
基金supported by the National Natural Science Foundation of China(62473341)Key Technologies R&D Program of Henan Province(242102211071,252102211086,252102210166).
文摘With the rapid development of the industrial Internet,the network security environment has become increasingly complex and variable.Intrusion detection,a core technology for ensuring the security of industrial control systems,faces the challenge of unbalanced data samples,particularly the low detection rates for minority class attack samples.Therefore,this paper proposes a data enhancement method for intrusion detection in the industrial Internet based on a Self-Attention Wasserstein Generative Adversarial Network(SA-WGAN)to address the low detection rates of minority class attack samples in unbalanced intrusion detection scenarios.The proposed method integrates a selfattention mechanism with a Wasserstein Generative Adversarial Network(WGAN).The self-attention mechanism automatically learns important features from the input data and assigns different weights to emphasize the key features related to intrusion behaviors,providing strong guidance for subsequent data generation.The WGAN generates new data samples through adversarial training to expand the original dataset.In the SA-WGAN framework,the WGAN directs the data generation process based on the key features extracted by the self-attention mechanism,ensuring that the generated samples exhibit both diversity and similarity to real data.Experimental results demonstrate that the SA-WGAN-based data enhancement method significantly improves detection performance for attack samples from minority classes,addresses issues of insufficient data and category imbalance,and enhances the generalization ability and overall performance of the intrusion detection model.
基金supported by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant No.GPIP:2040-611-2024。
文摘The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent requirements for ultra-low latency,high reliability,and robust privacy present significant challenges.Conventional centralized Federated Learning(FL)architectures struggle with latency and privacy constraints,while fully distributed FL(DFL)faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous.To address these limitations,we propose a Clustered Distributed Federated Learning(CDFL)architecture tailored for a 6G-enabled TIoT environment.Clients are grouped into clusters based on data similarity and/or geographical proximity,enabling local intra-cluster aggregation before inter-cluster model sharing.This hierarchical,peer-to-peer approach reduces communication overhead,mitigates non-IID effects,and eliminates single points of failure.By offloading aggregation to the network edge and leveraging dynamic clustering,CDFL enhances both computational and communication efficiency.Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows.Specifically,CDFL demonstrates up to a 30%reduction in training time under highly heterogeneous data distributions,indicating faster convergence.It also reduces communication overhead by approximately 40%compared to DFL.These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments.These results validate CDFL as a scalable,privacy-preserving solution for next-generation TIoT applications.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1005000)the National Natural Science Foundation of China(Grant No.62025110 and 62101308).
文摘Satellite Internet(SI)provides broadband access as a critical information infrastructure in 6G.However,with the integration of the terrestrial Internet,the influx of massive terrestrial traffic will bring significant threats to SI,among which DDoS attack will intensify the erosion of limited bandwidth resources.Therefore,this paper proposes a DDoS attack tracking scheme using a multi-round iterative Viterbi algorithm to achieve high-accuracy attack path reconstruction and fast internal source locking,protecting SI from the source.Firstly,to reduce communication overhead,the logarithmic representation of the traffic volume is added to the digests after modeling SI,generating the lightweight deviation degree to construct the observation probability matrix for the Viterbi algorithm.Secondly,the path node matrix is expanded to multi-index matrices in the Viterbi algorithm to store index information for all probability values,deriving the path with non-repeatability and maximum probability.Finally,multiple rounds of iterative Viterbi tracking are performed locally to track DDoS attack based on trimming tracking results.Simulation and experimental results show that the scheme can achieve 96.8%tracking accuracy of external and internal DDoS attack at 2.5 seconds,with the communication overhead at 268KB/s,effectively protecting the limited bandwidth resources of SI.
文摘Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems.This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services.The proposed method employs AWS Lambda functions with Amazon Relational Database Service(RDS)to facilitate the transmission of data collected from temperature and humidity sensors to the RDS database.In contrast,the conventional method utilizes AmazonDynamoDB for storing the same sensor data.Data were collected from 01 April 2022,to 26 August 2022,in Tokyo,Japan,focusing on temperature and relative humiditywitha resolutionof oneminute.The efficiency of the twomethods—conventional andproposed—was assessed in terms of both time and cost metrics,with a particular focus on data retrieval.The conventional method exhibited linear time complexity,leading to longer data retrieval times as the dataset grew,mainly due to DynamoDB’s pagination requirements and the parsing of payload data during the reading process.In contrast,the proposed method significantly reduced retrieval times for larger datasets by parsing payload data before writing it to the RDS database.Cost analysis revealed a savings of$1.56 per month with the adoption of the proposed approach for a 20-gigabyte database.
基金supported in part by the National Natural Science Foundation of China under Grant 52177082in part by the Beijing Nova Program under Grant 20220484007.
文摘With the advent of the digital economy,there has been a rapid proliferation of small-scale Internet data centers(SIDCs).By leveraging their spatiotemporal load regulation potential through data workload balancing,aggregated SIDCs have emerged as promising demand response(DR)resources for future power distribution systems.This paper presents an innovative framework for assessing capacity value(CV)by aggregating SIDCs participating in DR programs(SIDC-DR).Initially,we delineate the concept of CV tailored for aggregated SIDC scenarios and establish a metric for the assessment.Considering the effects of the data load dynamics,equipment constraints,and user behavior,we developed a sophisticated DR model for aggregated SIDCs using a data network aggregation method.Unlike existing studies,the proposed model captures the uncertainties associated with end tenant decisions to opt into an SIDC-DR program by utilizing a novel uncertainty modeling approach called Z-number formulation.This approach accounts for both the uncertainty in user participation intentions and the reliability of basic information during the DR process,enabling high-resolution profiling of the SIDC-DR potential in the CV evaluation.Simulation results from numerical studies conducted on a modified IEEE-33 node distribution system confirmed the effectiveness of the proposed approach and highlighted the potential benefits of SIDC-DR utilization in the efficient operation of future power systems.
文摘Under the current background of an information society,the digital transformation of enterprises has become a necessary means to enhance the competitiveness of enterprises.This article is based on the industrial Internet platform,the digital planning and architecture of enterprises research.First,we analyze the current challenges of digital transformation and the development opportunities brought by the industrial Internet.Then,we propose a digital planning method based on the industrial Internet platform,which takes the full connectivity of people,machine and things and intelligent decision making as the core,takes data collection,processing,analysis and application as the main line,and finally forms the top-level design of the digital transformation of enterprises.At the same time,we also built an industrial Internet platform architecture model,including the previous end perception layer,network transmission layer,platform service layer,and application innovation layer for four levels,to support enterprises in innovative applications and decision support under the industrial Internet environment.Research shows that this kind of enterprise digital planning and architecture based on an industrial Internet platform can effectively promote enterprises to achieve business model innovation,system innovation,and strengthen the flexibility and agility of enterprises to respond to market changes.The results of this research not only have important theoretical and practical significance for guiding enterprises to carry out digital planning and build an industrial Internet platform,but also provide useful reference for relevant policy formulation.
基金supported by National Natural Science Foundation of China(12174350)Science and Technology Project of State Grid Henan Electric Power Company(5217Q0240008).
文摘In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.
基金supports from the Major Key Project of PCL (PCL2021A031)Shenzhen Science Technology Program (GXWD20201230155427003-20200824093323001)
文摘In the Satellite-integrated Internet of Things(S-IoT),data freshness in the time-sensitive scenarios could not be guaranteed over the timevarying topology with current distribution strategies aiming to reduce the transmission delay.To address this problem,in this paper,we propose an age-optimal caching distribution mechanism for the high-timeliness data collection in S-IoT by adopting a freshness metric,as called age of information(AoI)through the caching-based single-source multidestinations(SSMDs)transmission,namely Multi-AoI,with a well-designed cross-slot directed graph(CSG).With the proposed CSG,we make optimizations on the locations of cache nodes by solving a nonlinear integer programming problem on minimizing Multi-AoI.In particular,we put up forward three specific algorithms respectively for improving the Multi-AoI,i.e.,the minimum queuing delay algorithm(MQDA)based on node deviation from average level,the minimum propagation delay algorithm(MPDA)based on the node propagation delay reduction,and a delay balanced algorithm(DBA)based on node deviation from average level and propagation delay reduction.The simulation results show that the proposed mechanism can effectively improve the freshness of information compared with the random selection algorithm.
基金supported by the National Natural Science Foundation of China(U22B2026)the ZTE Industry-Academia-Research Project(HC-CN-20221029003,IA20230628015)。
文摘With the introduction of 5G,users and devices can access the industrial network from anywhere in the world.Therefore,traditional perimeter-based security technologies for industrial networks can no longer work well.To solve this problem,a new security model called Zero Trust(ZT)is desired,which believes in“never trust and always verify”.Every time the asset in the industrial network is accessed,the subject is authenticated and its trustworthiness is assessed.In this way,the asset in industrial network can be well protected,whether the subject is in the internal network or the external network.However,in order to construct the zero trust model in the 5G Industrial Internet collaboration system,there are still many problems to be solved.In this paper,we first introduce the security issues in the 5G Industrial Internet collaboration system,and illustrate the zero trust architecture.Then,we analyze the gap between existing security techniques and the zero trust architecture.Finally,we discuss several potential security techniques that can be used to implement the zero trust model.The purpose of this paper is to point out the further direction for the realization of the Zero Trust Architecture(ZTA)in the 5G Industrial Internet collaboration system.
基金Supported by Key Program of Natural Science Foundation of Xinjiang Hetian College"Spatial Heterogeneity of Soil Organic Carbon and Carbon Sequestration Potential in Hotan Farmland"(2025ZR003).
文摘The present paper explores the ideological and political education work of university counselors in Xinjiang under the mobile Internet environment.It elaborates on the significant value of this work in adapting to contemporary development trends,promoting cultural integration,and safeguarding regional stability.The study analyzes various challenges encountered,including difficulties in information screening,limitations in online communication,and insufficient digital literacy.Corresponding countermeasures are proposed with the aim of enhancing the effectiveness of ideological and political education in Xinjiang s higher education institutions,fostering healthy student development,and facilitating regional progress.
