System identification is a data-driven modeling technique that originates from the control field.It constructs models from data to mimic the behavior of dynamic systems.However,in the network era,scenarios such as sen...System identification is a data-driven modeling technique that originates from the control field.It constructs models from data to mimic the behavior of dynamic systems.However,in the network era,scenarios such as sensor malfunctions,packet loss,cyber-attacks,and big data affect the quality,integrity,and security of the data.These data issues pose significant challenges to traditional system identification methods.This paper presents a comprehensive survey of the emergent challenges and advances in system identification in the network era.It explores cutting-edge methodologies to address data issues such as data loss,outliers,noise and nonlinear system identification for complex systems.To tackle the data loss,the methods based on imputation and likelihood-based inference(e.g.,expectation maximization)have been employed.For outliers and noise,methods like robust regression(e.g.,least median of squares,least trimmed squares)and lowrank matrix decomposition show progress in maintaining data integrity.Nonlinear system identification has advanced through kernel-based methods and neural networks,which can model complex data patterns.Finally,this paper provides valuable insights into potential directions for future research.展开更多
Data-flow errors are prevalent in cyber-physical systems(CPS).Although various approaches based on business process modeling notation(BPMN)have been devised for CPS modeling,the absence of formal specifications compli...Data-flow errors are prevalent in cyber-physical systems(CPS).Although various approaches based on business process modeling notation(BPMN)have been devised for CPS modeling,the absence of formal specifications complicates the verification of data-flow.Formal techniques such as Petri nets are popularly used for identifying data-flow errors.However,due to their interleaving semantics,they suffer from the state-space explosion problem.As an unfolding method for Petri nets,the merged process(MP)technique can well represent concurrency relationships and thus be used to address this issue.Yet generating MP is complex and incurs substantial overhead.By designing and applyingα-deletion rules for Petri nets with data(PNDs),this work simplifies MP,thus resulting in simplified MP(SMP)that is then used to identify data-flow errors.Our approach involves converting a BPMN into a PND and then constructing its SMP.The algorithms are developed to identify data-flow errors,e.g.,redundantdata and lost-data ones.The proposed method enhances the efficiency and effectiveness of identifying data-flow errors in CPS.It is expected to prevent the problems caused by data-flow errors,e.g.,medical malpractice and economic loss in some practical CPS.Its practicality and efficiency of the proposed method through several CPS.Its significant advantages over the state of the art are demonstrated.展开更多
Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system ...Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.展开更多
The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging t...The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.展开更多
By analyzing existed Internet of Things＇ system security vulnerabilities, a security architecture on trusting one is constructed. In the infrastructure, an off-line identity authentication based on the combined publi...By analyzing existed Internet of Things＇ system security vulnerabilities, a security architecture on trusting one is constructed. In the infrastructure, an off-line identity authentication based on the combined public key （CPK） mechanism is proposed, which solves the problems about a mass amount of authentications and the cross-domain authentication by integrating nodes＇ validity of identity authentication and uniqueness of identification. Moreover, the proposal of constructing nodes＇ authentic identification, valid authentication and credible communication connection at the application layer through the perception layer impels the formation of trust chain and relationship among perceptional nodes. Consequently, a trusting environment of the Internet of Things is built, by which a guidance of designing the trusted one would be provided.展开更多
BGP monitors are currently the main data resource of AS-level topology measurement,and the integrity of measurement result is limited to the location of such BGP monitors.However,there is currently no work to conduct ...BGP monitors are currently the main data resource of AS-level topology measurement,and the integrity of measurement result is limited to the location of such BGP monitors.However,there is currently no work to conduct a comprehensive study of the range of measurement results for a single BGP monitor.In this paper,we take the first step to describe the observed topology of each BGP monitor.To that end,we first investigate the construction and theoretical up-limit of the measured topology of a BGP monitor based on the valley-free model,then we evaluate the individual parts of the measured topology by comparing such theoretical results with the actually observed data.We find that:1)for more than 90%of the monitors,the actually observed peer-peer links merely takes a small part of all theoretical visible links;2)increasing the BGP monitors in the same AS may improve the measurement result,but with limited improvement;and 3)deploying multiple BGP monitors in different ASs can significantly improve the measurement results,but non-local BGP monitors can hardly replace the local AS BGP monitors.We also propose a metric for monitor selection optimization,and prove its effectiveness with experiment evaluation.展开更多
The Metaverse depicts a parallel digitalized world where virtuality and reality are fused.It has economic and social systems like those in the real world and provides intelligent services and applications.In this pape...The Metaverse depicts a parallel digitalized world where virtuality and reality are fused.It has economic and social systems like those in the real world and provides intelligent services and applications.In this paper,we introduce the Metaverse from a new technology perspective,including its essence,corresponding technical framework,and potential technical challenges.Specifically,we analyze the essence of the Metaverse from its etymology and point out breakthroughs promising to be made in time,space,and contents of the Metaverse by citing Maslow's Hierarchy of Needs.Subsequently,we conclude four pillars of the Metaverse,named ubiquitous connections,space convergence,virtuality and reality interaction,and human-centered communication,and establish a corresponding technical framework.Additionally,we envision open issues and challenges of the Metaverse in the technical aspect.The work proposes a new technology perspective of the Metaverse and will provide further guidance for its technology development in the future.展开更多
In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements o...In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling.Then,a Bi-LSTM-based model is proposed to predict the trajectories of vehicles.The service area is divided into several equal-sized grids.If the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory prediction.Moreover,we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction.Considering the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading.Simulation results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.展开更多
This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certifie...This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control.Specifically,an extended state observer is designed by leveraging historical and real-time data for concurrent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart.A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints.To ensure safety,highorder discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved.A recedinghorizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks.The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.展开更多
In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are qu...In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.展开更多
Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remed...Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remedy this issue,a large body of research has been performed in recent years and many new algorithms have been proposed.This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts.We start with a brief introduction to the basic concepts,followed by a summary of the benchmark test problems with irregular problems,an analysis of the causes of the irregularity,and real-world optimization problems with irregular Pareto fronts.Then,a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses.Finally,open challenges are pointed out and a few promising future directions are suggested.展开更多
Fault detection and diagnosis(FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy(AR), Principal component analysis(PCA), Discrete event system(DES)...Fault detection and diagnosis(FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy(AR), Principal component analysis(PCA), Discrete event system(DES) model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system parameters.Due to various reasons like faults, communication errors etc.,sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc.,have mechanisms to handle measurement inconsistency, however,they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent(MI)parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system(MIDES) framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state(s) the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pumpvalve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol(DHCP) server attack by mapping the attack to a fault in the DES framework.展开更多
Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks...Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks. In this paper, we aim to minimize the transmission delay in the MIMO-MEC in order to improve the spectral efficiency, energy efficiency, and data rate of MEC offloading. Dinkelbach transform and generalized singular value decomposition (GSVD) method are used to solve the delay minimization problem. Analytical results are provided to evaluate the performance of the proposed Hybrid-NOMA-MIMO-MEC system. Simulation results reveal that the H-NOMA-MIMO-MEC system can achieve better delay performance and lower energy consumption compared to OMA.展开更多
In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating c...In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)technologies.In this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use cases.Next,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration approach.We review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration approaches.We also highlight the need for intelligence in resources integration.Then,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various classes.Finally,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.展开更多
Deadlock resolution strategies based on siphon control are widely investigated.Their computational efficiency largely depends on siphon computation.Mixed-integer programming(MIP)can be utilized for the computation of ...Deadlock resolution strategies based on siphon control are widely investigated.Their computational efficiency largely depends on siphon computation.Mixed-integer programming(MIP)can be utilized for the computation of an emptiable siphon in a Petri net(PN).Based on it,deadlock resolution strategies can be designed without requiring complete siphon enumeration that has exponential complexity.Due to this reason,various MIP methods are proposed for various subclasses of PNs.This work proposes an innovative MIP method to compute an emptiable minimal siphon(EMS)for a subclass of PNs named S^(4)PR.In particular,many particular structural characteristics of EMS in S4 PR are formalized as constraints,which greatly reduces the solution space.Experimental results show that the proposed MIP method has higher computational efficiency.Furthermore,the proposed method allows one to determine the liveness of an ordinary S^(4)PR.展开更多
In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world.As a part of the IoT ecosystem,task assignment has b...In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world.As a part of the IoT ecosystem,task assignment has become an important goal of the research community.Existing task assignment algorithms can be categorized as offline(performs better with datasets but struggles to achieve good real-life results)or online(works well with real-life input but is difficult to optimize regarding in-depth assignments).This paper proposes a Cross-regional Online Task(CROT)assignment problem based on the online assignment model.Given the CROT problem,an Online Task Assignment across Regions based on Prediction(OTARP)algorithm is proposed.OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments.The first stage uses historical data to make offline predictions,with a graph-driven method for offline bipartite graph matching.The second stage uses a bipartite graph to complete the online task assignment process.This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies.To encourage crowd workers to complete crowd tasks across regions,an incentive strategy is designed to encourage crowd workers’movement.To avoid the idle problem in the process of crowd worker movement,a drop-by-rider problem is used to help crowd workers accept more crowd tasks,optimize the number of assignments,and increase utility.Finally,through comparison experiments on real datasets,the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.展开更多
Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features ma...Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. In this paper, we propose a new unsupervised quick reduct （QR） algorithm using rough set theory. The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool. The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.展开更多
It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the process.The proliferation of industrial sensors and the availability of ...It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the process.The proliferation of industrial sensors and the availability of thickening-system data make this possible.However,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive models.To address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening systems.Using a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental results.The results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system trajectories.The proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.展开更多
基金supported in part by the National Natural Science Foundation of China(62373060)the BNU Talent seed fund,and the Guangdong Provincial Key Laboratory IRADS for Data Science(2022B1212010006)Recommended by Associate Editor Zhengcai Cao.(Corresponding author:Liang Zhang.)。
文摘System identification is a data-driven modeling technique that originates from the control field.It constructs models from data to mimic the behavior of dynamic systems.However,in the network era,scenarios such as sensor malfunctions,packet loss,cyber-attacks,and big data affect the quality,integrity,and security of the data.These data issues pose significant challenges to traditional system identification methods.This paper presents a comprehensive survey of the emergent challenges and advances in system identification in the network era.It explores cutting-edge methodologies to address data issues such as data loss,outliers,noise and nonlinear system identification for complex systems.To tackle the data loss,the methods based on imputation and likelihood-based inference(e.g.,expectation maximization)have been employed.For outliers and noise,methods like robust regression(e.g.,least median of squares,least trimmed squares)and lowrank matrix decomposition show progress in maintaining data integrity.Nonlinear system identification has advanced through kernel-based methods and neural networks,which can model complex data patterns.Finally,this paper provides valuable insights into potential directions for future research.
基金supported by the National Natural Science Foundation of China(62402415)and in part by the Natural Science Foundation of Shandong Province of China(ZR2024MF129)in part by State Key Laboratory of Massive Personalized Customization System and Technology(No.H&C-MPC-2023-02-03).
文摘Data-flow errors are prevalent in cyber-physical systems(CPS).Although various approaches based on business process modeling notation(BPMN)have been devised for CPS modeling,the absence of formal specifications complicates the verification of data-flow.Formal techniques such as Petri nets are popularly used for identifying data-flow errors.However,due to their interleaving semantics,they suffer from the state-space explosion problem.As an unfolding method for Petri nets,the merged process(MP)technique can well represent concurrency relationships and thus be used to address this issue.Yet generating MP is complex and incurs substantial overhead.By designing and applyingα-deletion rules for Petri nets with data(PNDs),this work simplifies MP,thus resulting in simplified MP(SMP)that is then used to identify data-flow errors.Our approach involves converting a BPMN into a PND and then constructing its SMP.The algorithms are developed to identify data-flow errors,e.g.,redundantdata and lost-data ones.The proposed method enhances the efficiency and effectiveness of identifying data-flow errors in CPS.It is expected to prevent the problems caused by data-flow errors,e.g.,medical malpractice and economic loss in some practical CPS.Its practicality and efficiency of the proposed method through several CPS.Its significant advantages over the state of the art are demonstrated.
文摘Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.
基金supported in part by the National Natural Science Foundation of China(92167201,62273264,61933007)。
文摘The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.
基金supported by the 863 Program under Grant No. 2008AA04A107
文摘By analyzing existed Internet of Things＇ system security vulnerabilities, a security architecture on trusting one is constructed. In the infrastructure, an off-line identity authentication based on the combined public key （CPK） mechanism is proposed, which solves the problems about a mass amount of authentications and the cross-domain authentication by integrating nodes＇ validity of identity authentication and uniqueness of identification. Moreover, the proposal of constructing nodes＇ authentic identification, valid authentication and credible communication connection at the application layer through the perception layer impels the formation of trust chain and relationship among perceptional nodes. Consequently, a trusting environment of the Internet of Things is built, by which a guidance of designing the trusted one would be provided.
基金This work was supported in part by the Guangdong Province Key Research and Development Plan(Grant No.2019B010137004)the National Key research and Development Plan(Grant No.2018YFB0803504).
文摘BGP monitors are currently the main data resource of AS-level topology measurement,and the integrity of measurement result is limited to the location of such BGP monitors.However,there is currently no work to conduct a comprehensive study of the range of measurement results for a single BGP monitor.In this paper,we take the first step to describe the observed topology of each BGP monitor.To that end,we first investigate the construction and theoretical up-limit of the measured topology of a BGP monitor based on the valley-free model,then we evaluate the individual parts of the measured topology by comparing such theoretical results with the actually observed data.We find that:1)for more than 90%of the monitors,the actually observed peer-peer links merely takes a small part of all theoretical visible links;2)increasing the BGP monitors in the same AS may improve the measurement result,but with limited improvement;and 3)deploying multiple BGP monitors in different ASs can significantly improve the measurement results,but non-local BGP monitors can hardly replace the local AS BGP monitors.We also propose a metric for monitor selection optimization,and prove its effectiveness with experiment evaluation.
文摘The Metaverse depicts a parallel digitalized world where virtuality and reality are fused.It has economic and social systems like those in the real world and provides intelligent services and applications.In this paper,we introduce the Metaverse from a new technology perspective,including its essence,corresponding technical framework,and potential technical challenges.Specifically,we analyze the essence of the Metaverse from its etymology and point out breakthroughs promising to be made in time,space,and contents of the Metaverse by citing Maslow's Hierarchy of Needs.Subsequently,we conclude four pillars of the Metaverse,named ubiquitous connections,space convergence,virtuality and reality interaction,and human-centered communication,and establish a corresponding technical framework.Additionally,we envision open issues and challenges of the Metaverse in the technical aspect.The work proposes a new technology perspective of the Metaverse and will provide further guidance for its technology development in the future.
基金supported in part by the National Science Foundation of China(Grant No.62172450)the Key R&D Plan of Hunan Province(Grant No.2022GK2008)the Nature Science Foundation of Hunan Province(Grant No.2020JJ4756)。
文摘In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling.Then,a Bi-LSTM-based model is proposed to predict the trajectories of vehicles.The service area is divided into several equal-sized grids.If the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory prediction.Moreover,we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction.Considering the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading.Simulation results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.
基金supported in part by the National Science and Technology Major Project(2022ZD0119902)the National Natural Science Foundation of China(52471372,623B2018,62203015,62233001)+4 种基金the Liaoning Revitalization Leading Talents Program(XLYC2402054)the Key Basic Research of Dalian(2023JJ11CG008)the Fundamental Research Funds for the Central Universities(3132023508)the Collaborative Research Fund of Hong Kong Research Grants Council(C1013-24G)the Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University(2023YBPY005).
文摘This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control.Specifically,an extended state observer is designed by leveraging historical and real-time data for concurrent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart.A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints.To ensure safety,highorder discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved.A recedinghorizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks.The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.
基金supported in part by the National Natural Science Foundation of China(61933007,62273087,62273088,U21A2019)the Shanghai Pujiang Program of China(22PJ1400400)+2 种基金the Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105)the Royal Society of U.K.the Alexander von Humboldt Foundation of Germany
文摘In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.
基金supported in part by the National Natural Science Foundation of China(61806051,61903078)Natural Science Foundation of Shanghai(20ZR1400400)+2 种基金Agricultural Project of the Shanghai Committee of Science and Technology(16391902800)the Fundamental Research Funds for the Central Universities(2232020D-48)the Project of the Humanities and Social Sciences on Young Fund of the Ministry of Education in China(Research on swarm intelligence collaborative robust optimization scheduling for high-dimensional dynamic decisionmaking system(20YJCZH052))。
文摘Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remedy this issue,a large body of research has been performed in recent years and many new algorithms have been proposed.This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts.We start with a brief introduction to the basic concepts,followed by a summary of the benchmark test problems with irregular problems,an analysis of the causes of the irregularity,and real-world optimization problems with irregular Pareto fronts.Then,a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses.Finally,open challenges are pointed out and a few promising future directions are suggested.
基金supported by TATA Consultancy Services(TCS),India through TCS Research Fellowship Program
文摘Fault detection and diagnosis(FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy(AR), Principal component analysis(PCA), Discrete event system(DES) model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system parameters.Due to various reasons like faults, communication errors etc.,sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc.,have mechanisms to handle measurement inconsistency, however,they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent(MI)parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system(MIDES) framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state(s) the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pumpvalve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol(DHCP) server attack by mapping the attack to a fault in the DES framework.
基金supported by Republic of Turkey Ministry of National Education
文摘Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks. In this paper, we aim to minimize the transmission delay in the MIMO-MEC in order to improve the spectral efficiency, energy efficiency, and data rate of MEC offloading. Dinkelbach transform and generalized singular value decomposition (GSVD) method are used to solve the delay minimization problem. Analytical results are provided to evaluate the performance of the proposed Hybrid-NOMA-MIMO-MEC system. Simulation results reveal that the H-NOMA-MIMO-MEC system can achieve better delay performance and lower energy consumption compared to OMA.
基金supported in part by National Key R&D Program of China(2019YFE0196400)Key Research and Development Program of Shaanxi(2022KWZ09)+4 种基金National Natural Science Foundation of China(61771358,61901317,62071352)Fundamental Research Funds for the Central Universities(JB190104)Joint Education Project between China and Central-Eastern European Countries(202005)the 111 Project(B08038)。
文摘In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)technologies.In this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use cases.Next,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration approach.We review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration approaches.We also highlight the need for intelligence in resources integration.Then,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various classes.Finally,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.
基金supported by the Research Grants Council of the Hong Kong Special Administrative Region,China(416811,416812)National Natural Science Foundation of China(61573003)part by the Scientific Research Fund of Hunan Provincial Education Department of China(15k026)
基金supported in part by Zhejiang Provincial Key Research and Development Program(2018C01084)Zhejiang Natural Science Foundation(LQ20F020009)Zhejiang Gongshang University,Zhejiang Provincial Key Laboratory of New Network Standards and Technologies(2013E10012)。
文摘Deadlock resolution strategies based on siphon control are widely investigated.Their computational efficiency largely depends on siphon computation.Mixed-integer programming(MIP)can be utilized for the computation of an emptiable siphon in a Petri net(PN).Based on it,deadlock resolution strategies can be designed without requiring complete siphon enumeration that has exponential complexity.Due to this reason,various MIP methods are proposed for various subclasses of PNs.This work proposes an innovative MIP method to compute an emptiable minimal siphon(EMS)for a subclass of PNs named S^(4)PR.In particular,many particular structural characteristics of EMS in S4 PR are formalized as constraints,which greatly reduces the solution space.Experimental results show that the proposed MIP method has higher computational efficiency.Furthermore,the proposed method allows one to determine the liveness of an ordinary S^(4)PR.
基金supported in part by the National Natural Science Foundation of China under Grant 62072392,Grant 61822602,Grant 61772207,Grant 61802331,Grant 61602399,Grant 61702439,Grant 61773331,and Grant 62062034the China Postdoctoral Science Foundation under Grant 2019T120732 and Grant 2017M622691+2 种基金the Natural Science Foundation of Shandong Province under Grant ZR2016FM42the Major scientific and technological innovation projects of Shandong Province under Grant 2019JZZY020131the Key projects of Shandong Natural Science Foundation under Grant ZR2020KF019.
文摘In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world.As a part of the IoT ecosystem,task assignment has become an important goal of the research community.Existing task assignment algorithms can be categorized as offline(performs better with datasets but struggles to achieve good real-life results)or online(works well with real-life input but is difficult to optimize regarding in-depth assignments).This paper proposes a Cross-regional Online Task(CROT)assignment problem based on the online assignment model.Given the CROT problem,an Online Task Assignment across Regions based on Prediction(OTARP)algorithm is proposed.OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments.The first stage uses historical data to make offline predictions,with a graph-driven method for offline bipartite graph matching.The second stage uses a bipartite graph to complete the online task assignment process.This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies.To encourage crowd workers to complete crowd tasks across regions,an incentive strategy is designed to encourage crowd workers’movement.To avoid the idle problem in the process of crowd worker movement,a drop-by-rider problem is used to help crowd workers accept more crowd tasks,optimize the number of assignments,and increase utility.Finally,through comparison experiments on real datasets,the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.
基金supported by the UGC, SERO, Hyderabad under FDP during XI plan periodthe UGC, New Delhi for financial assistance under major research project Grant No. F-34-105/2008
文摘Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. In this paper, we propose a new unsupervised quick reduct （QR） algorithm using rough set theory. The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool. The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.
基金supported by National Key Research and Development Program of China(2019YFC0605300)the National Natural Science Foundation of China(61873299,61902022,61972028)+2 种基金Scientific and Technological Innovation Foundation of Shunde Graduate School,University of Science and Technology Beijing(BK21BF002)Macao Science and Technology Development Fund under Macao Funding Scheme for Key R&D Projects(0025/2019/AKP)Macao Science and Technology Development Fund(0015/2020/AMJ)。
文摘It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the process.The proliferation of industrial sensors and the availability of thickening-system data make this possible.However,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive models.To address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening systems.Using a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental results.The results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system trajectories.The proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.
