Fault tolerant ability is an important aspect for overall evaluation of distributed system(DS). This paper discusses three measures for the evaluation: node/edge connectivity, number of spanning trees and synthetic co...Fault tolerant ability is an important aspect for overall evaluation of distributed system(DS). This paper discusses three measures for the evaluation: node/edge connectivity, number of spanning trees and synthetic connectivity. A numerical example for illustration and analysis is given, and the synthetic connectivity measure presented by this paper is proved to be rational and satisfactory.展开更多
Dynamic voltage scaling (DVS), supported by many DVS-enabled processors, is an efficient technique for energy-efficient embedded systems. Many researchers work on DVS and have presented various DVS algorithms, some wi...Dynamic voltage scaling (DVS), supported by many DVS-enabled processors, is an efficient technique for energy-efficient embedded systems. Many researchers work on DVS and have presented various DVS algorithms, some with quite good results. However, the previous algorithms either have a large time complexity or obtain results sensitive to the count of the voltage modes. Fine-grained voltage modes lead to optimal results, but coarse-grained voltage modes cause less optimal one. A new algorithm is presented, which is based on ant colony optimization, called ant colony optimization voltage and task scheduling (ACO-VTS) with a low time complexity implemented by parallelizing and its linear time approximation algorithm. Both of them generate quite good results, saving up to 30% more energy than that of the previous ones under coarse-grained modes, and their results don’t depend on the number of modes available.展开更多
Distributed System Course is a professional computer course of Harbin Institute of Technology.With the guidance of the education strategy under the background of New Engineering,we adhere to the concept of cultivating...Distributed System Course is a professional computer course of Harbin Institute of Technology.With the guidance of the education strategy under the background of New Engineering,we adhere to the concept of cultivating diverse and innovative outstanding engineering and technology talents.This course conducts research on the teaching content,teaching mode,and evaluation system.It combines the traditional teaching mode with the online teaching mode,as well as problem-driven theories with practice,and adopts a diversified evaluation system.The research of the course has fully mobilized students’learning enthusiasm,improved teaching quality,and achieved significant teaching results.展开更多
Numerous edge-chasing deadlock detection algonthms were developed lor the cycle detection in distributed systems, but their detections had the n steps speed limitation and n （ n- 1） overhead limitation to detect a c...Numerous edge-chasing deadlock detection algonthms were developed lor the cycle detection in distributed systems, but their detections had the n steps speed limitation and n （ n- 1） overhead limitation to detect a cycle of size n under the one-resource request model. Since fast deadlock detection is critical, this paper proposed a new algorithm to speed up the detection process. In our algorithm, when the running of a transaction node is blocked, the being requested resource nodes reply it with the waiting or being waited message simultaneously, so the blocked node knows both its predecessors and successors, which helps it detecting a cycle of size 2 directly and locally. For the cycle of size n （ n 〉 2）, a special probe is produced which has the predecessors information of its originator, so the being detected nodes know their indirect predecessors and direct successors, and can detect the cycle within n - 2 steps. The proposed algorithm is formally proved to be correct by the invariant verification method. Performance evaluation shows that the message overhead of our detection is （ n^2 - n - 2）/2, hence both the detection speed and message cost of the proposed algorithm are better than that of the existing al gorithms.展开更多
This paper investigates large-scale distributed system design. It looks at features, main design considerations and provides the Netflix API, Cassandra and Oracle as examples of such systems. Moreover, the paper inves...This paper investigates large-scale distributed system design. It looks at features, main design considerations and provides the Netflix API, Cassandra and Oracle as examples of such systems. Moreover, the paper investigates the challenges of designing, developing, deploying, and maintaining such systems, in regard to the features presented. Finally, the paper discusses aspects of available solutions and current practices to challenges that large-scale distributed systems face.展开更多
In developing distributed systems, conformance testing is required to determine whether an implementation under test （IUT） conforms to its specification. With distributed test architectures involving multiple remote...In developing distributed systems, conformance testing is required to determine whether an implementation under test （IUT） conforms to its specification. With distributed test architectures involving multiple remote testers, testing approaches may become more complicated because of issues known as controllability and observability problems. Based on a finite state machine （FSM） representation of the system＇s specification, this paper proposes a new method to generate a test sequence utilizing multiple UIO sequences. The method is essentially guided by the way of minimizing the use of external coordination messages and input/output operations. Experiments are given to evaluate the proposed method.展开更多
The use of technology has increased vastly and today computer systems are interconnected via different communication medium. The use of distributed systems in our day to day activities has solely improved with data di...The use of technology has increased vastly and today computer systems are interconnected via different communication medium. The use of distributed systems in our day to day activities has solely improved with data distributions. This is because distributed systems enable nodes to organise and allow their resources to be used among the connected systems or devices that make people to be integrated with geographically distributed computing facilities. The distributed systems may lead to lack of service availability due to multiple system failures on multiple failure points. This article highlights the different fault tolerance mechanism in distributed systems used to prevent multiple system failures on multiple failure points by considering replication, high redundancy and high availability of the distributed services.展开更多
Consensus control of multi-agent systems is an innovative paradigm for the development of intelligent distributed systems.This has fascinated numerous scientific groups for their promising applications as they have th...Consensus control of multi-agent systems is an innovative paradigm for the development of intelligent distributed systems.This has fascinated numerous scientific groups for their promising applications as they have the freedom to achieve their local and global goals and make their own decisions.Network communication topologies based on graph and matrix theory are widely used in a various real-time applications ranging from software agents to robotics.Therefore,while sustaining the significance of both directed and undirected graphs,this research emphases on the demonstration of a distributed average consensus algorithm.It uses the harmonic mean in the domain of multi-agent systems with directed and undirected graphs under static topologies based on a control input scheme.The proposed agreement protocol focuses on achieving a constant consensus on directional and undirected graphs using the exchange of information between neighbors to update their status values and to be able to calculate the total number of agents that contribute to the communication network at the same time.The proposed method is implemented for the identical networks that are considered under the directional and non-directional communication links.Two different scenarios are simulated and it is concluded that the undirected approach has an advantage over directed graph communication in terms of processing time and the total number of iterations required to achieve convergence.The same network parameters are introduced for both orientations of the communication graphs.In addition,the results of the simulation and the calculation of various matrices are provided at the end to validate the effectiveness of the proposed algorithm to achieve consensus.展开更多
In parallel and distributed computing, development of an efficient static task scheduling algorithm for directed acyclic graph (DAG) applications is an important problem. The static task scheduling problem is NP-compl...In parallel and distributed computing, development of an efficient static task scheduling algorithm for directed acyclic graph (DAG) applications is an important problem. The static task scheduling problem is NP-complete in its general form. The complexity of the problem increases when task scheduling is to be done in a heterogeneous environment, consisting of processors with varying processing capabilities and network links with varying bandwidths. List scheduling algorithms are generally preferred since they generate good quality schedules with less complexity. But these list algorithms leave a lot of room for improvement, especially when these algorithms are used in specialized heterogeneous environments This paper presents an hybrid genetic task scheduling algorithm for the tasks run on the network of heterogeneous systems and represented by Directed Acyclic Graphs (DAGs). First, the algorithm assigns a coupling factor to each task to present the tasks should be scheduled onto the same processor by avoiding the large communication time. Second, the algorithm generate some high quality initial solution by scheduling the tasks which are strongly coupled with each other onto the same processor, and improve the quality of the solution by using coupling initial solutions, random solution, near optimal solutions obtained by the list scheduling algorithm in the crossover and mutation operator. The performance of the algorithm is illustrated by comparing with the existing effectively scheduling algorithms.展开更多
This paper presents a study on protection coordination of over current relays (OCRs) in a distributed system by considering its different operating modes. Two different case studies which are considered in present wor...This paper presents a study on protection coordination of over current relays (OCRs) in a distributed system by considering its different operating modes. Two different case studies which are considered in present work for protection coordination include: (i) DG interfaced distribution system in grid connected mode and (ii) DG interfaced distribution system in islanded mode of operation. The proposed approach is tested on the Canadian urban benchmark distribution system consisting of 9 buses. On the occurrence of fault, level of fault current changes which in turn changes the operating time of various OCRs. Therefore, it is important to calculate and suggest method of the relay setting in order to minimize the operating time of relays and also to avoid its mal-operation. In this paper, the protection scheme is optimally designed by taking into account the above mentioned conditions. The operating time of relays can be decreased and, at the same time, coordination can be maintained by considering the optimum values of time dial setting (TDS). Genetic Algorithm (GA) has been used for determining the optimum values of TDS and hence operating time.展开更多
In this paper we report on a work in progress assessing the faults observed and reported in a distributed, safety-critical, largely embedded system with both electrical and mechanical components. We illustrate why sta...In this paper we report on a work in progress assessing the faults observed and reported in a distributed, safety-critical, largely embedded system with both electrical and mechanical components. We illustrate why standard software testing techniques are not sufficient and indicate some of the technical and non-technical problems encountered in examining the faults and the initial results obtained. While the application domain is elevator operation, the techniques described here are general enough to apply to many other domains. Much of the data analyzed here would be considered imprecise in the software industry if it were used in software testing or to help increase fault tolerance. The paper includes a discussion of the use of multiple views of data, assessment of missing data, and analysis of informal information to produce its conclusions about fault avoidance and fault tolerance.展开更多
This paper introduces an architecture of distributed systems that facilitates the implementation of a substantial range of dependable system properties, i.e., properties that span an entire system, or a set of compone...This paper introduces an architecture of distributed systems that facilitates the implementation of a substantial range of dependable system properties, i.e., properties that span an entire system, or a set of components dispersed throughout it. This architecture, called GDS, for governed distributed system, governs the system by controlling the flow of messages between its actors, independently of the internals of the interacting actors. This governance is done via an enforced collection of interaction laws organized into a modular and conflict free hierarchical ensemble. This ensemble of laws is sensitive to the history of interaction;and it is enforced in a decentralized manner, and is thus scalable. The dependable system properties that can be implemented under GDS can have the following beneficial consequences, among others: a) the ability to establish regularities over the system, rendering it more coherent, and easier to reason about;b) the ability to provide a degree of trust among the disparate actor of the system;and c) the ability to ensure compliance with interaction protocols that are essential for distributed computing. Consequently, the GDS architecture can have a significant impact on the following important system qualities: security, fault tolerance, auditability, and manageability.展开更多
Harvesting energy for execution from the environment (e.g., solar, wind energy) has recently emerged as a feasible solution for low-cost and low-power distributed systems. When real-time responsiveness of a given appl...Harvesting energy for execution from the environment (e.g., solar, wind energy) has recently emerged as a feasible solution for low-cost and low-power distributed systems. When real-time responsiveness of a given application has to be guaranteed, the recharge rate of obtaining energy inevitably affects the task scheduling. This paper extends our previous works in?[1] [2] to explore the real-time task assignment problem on an energy-harvesting distributed system. The solution using Ant Colony Optimization (ACO) and several significant improvements are presented. Simulations compare the performance of the approaches, which demonstrate the solutions effectiveness and efficiency.展开更多
The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in...The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingthe significance of using cutting-edge technology to meet rising privacy issues in digital identity.展开更多
Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell t...Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.展开更多
We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that prov...We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.展开更多
This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation.For the distributed estimati...This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation.For the distributed estimation structure under consideration,an estimation center is not necessary,and the estimator derives its information from itself and neighboring nodes,which fuses the state vector and the measurement vector.In an effort to cut down data conflicts in communication networks,the stochastic communication protocol(SCP)is employed so that the output signals from sensors can be selected.Additionally,a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data.On this basis,sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error.Finally,a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.展开更多
The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed wo...The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.展开更多
To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on th...To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on the target detection performance,firstly,a spatial detection model of distributed high-frequency surface wave radar(distributed-HFSWR)is established in this paper.In this model,a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio(SNR),and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error.The direct wave component is extracted from the spectrum,the range estimation error and Doppler estimation error are reduced by the method of curve fitting,and the fitting accuracy of the parameters is improved.Then,the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed.The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given.Finally,the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting,the experimental data is compensated to correct the shift of the target,and finally the correct target parameter information is obtained.Simulations and experimental results demonstrate the superiority and correctness of the proposed method,theoretical derivation and detection model proposed in this paper.展开更多
Artificial intelligence(AI)plays a critical role in signal recognition of distributed sensor systems(DSS),boosting its applications in multiple monitoring fields.Due to the domain differences between massive sensors i...Artificial intelligence(AI)plays a critical role in signal recognition of distributed sensor systems(DSS),boosting its applications in multiple monitoring fields.Due to the domain differences between massive sensors in signal acquisition conditions,such as manufacturing process,deployment,and environments,current AI schemes for signal recognition of DSS frequently encounter poor generalization performance.In this paper,an adaptive decentralized artificial intelligence(ADAI)method for signal recognition of DSS is proposed,to improve the entire generalization performance.By fine-tuning pre-trained model with the unlabeled data in each domain,the ADAI scheme can train a series of adaptive AI models for all target domains,significantly reducing the false alarm rate(FAR)and missing alarm rate(MAR)induced by domain differences.The field tests about intrusion signal recognition with distributed optical fiber sensors system demonstrate the efficacy of the ADAI scheme,showcasing a FAR of merely 4.3%and 0%,along with a MAR of only 1.4%and 2.7%within two specific target domains.The ADAI scheme is expected to offer a practical paradigm for signal recognition of DSS in multiple application fields.展开更多
文摘Fault tolerant ability is an important aspect for overall evaluation of distributed system(DS). This paper discusses three measures for the evaluation: node/edge connectivity, number of spanning trees and synthetic connectivity. A numerical example for illustration and analysis is given, and the synthetic connectivity measure presented by this paper is proved to be rational and satisfactory.
基金the National"973"Basic Research Programof China (2004CB318202)
文摘Dynamic voltage scaling (DVS), supported by many DVS-enabled processors, is an efficient technique for energy-efficient embedded systems. Many researchers work on DVS and have presented various DVS algorithms, some with quite good results. However, the previous algorithms either have a large time complexity or obtain results sensitive to the count of the voltage modes. Fine-grained voltage modes lead to optimal results, but coarse-grained voltage modes cause less optimal one. A new algorithm is presented, which is based on ant colony optimization, called ant colony optimization voltage and task scheduling (ACO-VTS) with a low time complexity implemented by parallelizing and its linear time approximation algorithm. Both of them generate quite good results, saving up to 30% more energy than that of the previous ones under coarse-grained modes, and their results don’t depend on the number of modes available.
文摘Distributed System Course is a professional computer course of Harbin Institute of Technology.With the guidance of the education strategy under the background of New Engineering,we adhere to the concept of cultivating diverse and innovative outstanding engineering and technology talents.This course conducts research on the teaching content,teaching mode,and evaluation system.It combines the traditional teaching mode with the online teaching mode,as well as problem-driven theories with practice,and adopts a diversified evaluation system.The research of the course has fully mobilized students’learning enthusiasm,improved teaching quality,and achieved significant teaching results.
文摘Numerous edge-chasing deadlock detection algonthms were developed lor the cycle detection in distributed systems, but their detections had the n steps speed limitation and n （ n- 1） overhead limitation to detect a cycle of size n under the one-resource request model. Since fast deadlock detection is critical, this paper proposed a new algorithm to speed up the detection process. In our algorithm, when the running of a transaction node is blocked, the being requested resource nodes reply it with the waiting or being waited message simultaneously, so the blocked node knows both its predecessors and successors, which helps it detecting a cycle of size 2 directly and locally. For the cycle of size n （ n 〉 2）, a special probe is produced which has the predecessors information of its originator, so the being detected nodes know their indirect predecessors and direct successors, and can detect the cycle within n - 2 steps. The proposed algorithm is formally proved to be correct by the invariant verification method. Performance evaluation shows that the message overhead of our detection is （ n^2 - n - 2）/2, hence both the detection speed and message cost of the proposed algorithm are better than that of the existing al gorithms.
文摘This paper investigates large-scale distributed system design. It looks at features, main design considerations and provides the Netflix API, Cassandra and Oracle as examples of such systems. Moreover, the paper investigates the challenges of designing, developing, deploying, and maintaining such systems, in regard to the features presented. Finally, the paper discusses aspects of available solutions and current practices to challenges that large-scale distributed systems face.
基金Project supported by the National Natural Science Foundation of China （Grant No.60673115）, and the Open Foundation of State Key Laboratory of Software Engineering （Grant No.SKLSE05-13）
文摘In developing distributed systems, conformance testing is required to determine whether an implementation under test （IUT） conforms to its specification. With distributed test architectures involving multiple remote testers, testing approaches may become more complicated because of issues known as controllability and observability problems. Based on a finite state machine （FSM） representation of the system＇s specification, this paper proposes a new method to generate a test sequence utilizing multiple UIO sequences. The method is essentially guided by the way of minimizing the use of external coordination messages and input/output operations. Experiments are given to evaluate the proposed method.
文摘The use of technology has increased vastly and today computer systems are interconnected via different communication medium. The use of distributed systems in our day to day activities has solely improved with data distributions. This is because distributed systems enable nodes to organise and allow their resources to be used among the connected systems or devices that make people to be integrated with geographically distributed computing facilities. The distributed systems may lead to lack of service availability due to multiple system failures on multiple failure points. This article highlights the different fault tolerance mechanism in distributed systems used to prevent multiple system failures on multiple failure points by considering replication, high redundancy and high availability of the distributed services.
文摘Consensus control of multi-agent systems is an innovative paradigm for the development of intelligent distributed systems.This has fascinated numerous scientific groups for their promising applications as they have the freedom to achieve their local and global goals and make their own decisions.Network communication topologies based on graph and matrix theory are widely used in a various real-time applications ranging from software agents to robotics.Therefore,while sustaining the significance of both directed and undirected graphs,this research emphases on the demonstration of a distributed average consensus algorithm.It uses the harmonic mean in the domain of multi-agent systems with directed and undirected graphs under static topologies based on a control input scheme.The proposed agreement protocol focuses on achieving a constant consensus on directional and undirected graphs using the exchange of information between neighbors to update their status values and to be able to calculate the total number of agents that contribute to the communication network at the same time.The proposed method is implemented for the identical networks that are considered under the directional and non-directional communication links.Two different scenarios are simulated and it is concluded that the undirected approach has an advantage over directed graph communication in terms of processing time and the total number of iterations required to achieve convergence.The same network parameters are introduced for both orientations of the communication graphs.In addition,the results of the simulation and the calculation of various matrices are provided at the end to validate the effectiveness of the proposed algorithm to achieve consensus.
文摘In parallel and distributed computing, development of an efficient static task scheduling algorithm for directed acyclic graph (DAG) applications is an important problem. The static task scheduling problem is NP-complete in its general form. The complexity of the problem increases when task scheduling is to be done in a heterogeneous environment, consisting of processors with varying processing capabilities and network links with varying bandwidths. List scheduling algorithms are generally preferred since they generate good quality schedules with less complexity. But these list algorithms leave a lot of room for improvement, especially when these algorithms are used in specialized heterogeneous environments This paper presents an hybrid genetic task scheduling algorithm for the tasks run on the network of heterogeneous systems and represented by Directed Acyclic Graphs (DAGs). First, the algorithm assigns a coupling factor to each task to present the tasks should be scheduled onto the same processor by avoiding the large communication time. Second, the algorithm generate some high quality initial solution by scheduling the tasks which are strongly coupled with each other onto the same processor, and improve the quality of the solution by using coupling initial solutions, random solution, near optimal solutions obtained by the list scheduling algorithm in the crossover and mutation operator. The performance of the algorithm is illustrated by comparing with the existing effectively scheduling algorithms.
文摘This paper presents a study on protection coordination of over current relays (OCRs) in a distributed system by considering its different operating modes. Two different case studies which are considered in present work for protection coordination include: (i) DG interfaced distribution system in grid connected mode and (ii) DG interfaced distribution system in islanded mode of operation. The proposed approach is tested on the Canadian urban benchmark distribution system consisting of 9 buses. On the occurrence of fault, level of fault current changes which in turn changes the operating time of various OCRs. Therefore, it is important to calculate and suggest method of the relay setting in order to minimize the operating time of relays and also to avoid its mal-operation. In this paper, the protection scheme is optimally designed by taking into account the above mentioned conditions. The operating time of relays can be decreased and, at the same time, coordination can be maintained by considering the optimum values of time dial setting (TDS). Genetic Algorithm (GA) has been used for determining the optimum values of TDS and hence operating time.
文摘In this paper we report on a work in progress assessing the faults observed and reported in a distributed, safety-critical, largely embedded system with both electrical and mechanical components. We illustrate why standard software testing techniques are not sufficient and indicate some of the technical and non-technical problems encountered in examining the faults and the initial results obtained. While the application domain is elevator operation, the techniques described here are general enough to apply to many other domains. Much of the data analyzed here would be considered imprecise in the software industry if it were used in software testing or to help increase fault tolerance. The paper includes a discussion of the use of multiple views of data, assessment of missing data, and analysis of informal information to produce its conclusions about fault avoidance and fault tolerance.
文摘This paper introduces an architecture of distributed systems that facilitates the implementation of a substantial range of dependable system properties, i.e., properties that span an entire system, or a set of components dispersed throughout it. This architecture, called GDS, for governed distributed system, governs the system by controlling the flow of messages between its actors, independently of the internals of the interacting actors. This governance is done via an enforced collection of interaction laws organized into a modular and conflict free hierarchical ensemble. This ensemble of laws is sensitive to the history of interaction;and it is enforced in a decentralized manner, and is thus scalable. The dependable system properties that can be implemented under GDS can have the following beneficial consequences, among others: a) the ability to establish regularities over the system, rendering it more coherent, and easier to reason about;b) the ability to provide a degree of trust among the disparate actor of the system;and c) the ability to ensure compliance with interaction protocols that are essential for distributed computing. Consequently, the GDS architecture can have a significant impact on the following important system qualities: security, fault tolerance, auditability, and manageability.
文摘Harvesting energy for execution from the environment (e.g., solar, wind energy) has recently emerged as a feasible solution for low-cost and low-power distributed systems. When real-time responsiveness of a given application has to be guaranteed, the recharge rate of obtaining energy inevitably affects the task scheduling. This paper extends our previous works in?[1] [2] to explore the real-time task assignment problem on an energy-harvesting distributed system. The solution using Ant Colony Optimization (ACO) and several significant improvements are presented. Simulations compare the performance of the approaches, which demonstrate the solutions effectiveness and efficiency.
文摘The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingthe significance of using cutting-edge technology to meet rising privacy issues in digital identity.
基金the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number 223202.
文摘Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.
基金Project supported by the National Natural Science Foundation of China (Grant No.62073045)。
文摘We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.
基金supported in part by the National Natural Science Foundation of China(62073189,62173207)the Taishan Scholar Project of Shandong Province(tsqn202211129)。
文摘This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation.For the distributed estimation structure under consideration,an estimation center is not necessary,and the estimator derives its information from itself and neighboring nodes,which fuses the state vector and the measurement vector.In an effort to cut down data conflicts in communication networks,the stochastic communication protocol(SCP)is employed so that the output signals from sensors can be selected.Additionally,a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data.On this basis,sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error.Finally,a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.
文摘The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.
基金supported by the National Natural Science Foundation of China(61701140).
文摘To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on the target detection performance,firstly,a spatial detection model of distributed high-frequency surface wave radar(distributed-HFSWR)is established in this paper.In this model,a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio(SNR),and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error.The direct wave component is extracted from the spectrum,the range estimation error and Doppler estimation error are reduced by the method of curve fitting,and the fitting accuracy of the parameters is improved.Then,the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed.The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given.Finally,the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting,the experimental data is compensated to correct the shift of the target,and finally the correct target parameter information is obtained.Simulations and experimental results demonstrate the superiority and correctness of the proposed method,theoretical derivation and detection model proposed in this paper.
基金financial supports from the National Natural Science Foundation of China(NSFC)(No.61922033&U22A20206)Zhejiang Provincial Market Supervision Bureau Young Eagle Plan project under Grant CY2022228.
文摘Artificial intelligence(AI)plays a critical role in signal recognition of distributed sensor systems(DSS),boosting its applications in multiple monitoring fields.Due to the domain differences between massive sensors in signal acquisition conditions,such as manufacturing process,deployment,and environments,current AI schemes for signal recognition of DSS frequently encounter poor generalization performance.In this paper,an adaptive decentralized artificial intelligence(ADAI)method for signal recognition of DSS is proposed,to improve the entire generalization performance.By fine-tuning pre-trained model with the unlabeled data in each domain,the ADAI scheme can train a series of adaptive AI models for all target domains,significantly reducing the false alarm rate(FAR)and missing alarm rate(MAR)induced by domain differences.The field tests about intrusion signal recognition with distributed optical fiber sensors system demonstrate the efficacy of the ADAI scheme,showcasing a FAR of merely 4.3%and 0%,along with a MAR of only 1.4%and 2.7%within two specific target domains.The ADAI scheme is expected to offer a practical paradigm for signal recognition of DSS in multiple application fields.
