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VSSB-Raft: A Secure and Efficient Zero Trust Consensus Algorithm for Blockchain

ACM Transactions on Sensor Networks 2024年第2期20卷 1-22页

作者： Tian, Siben Bai, Fenhua Shen, Tao Zhang, Chi Gong, Bei Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technology Applications China Faculty of Information Technology Beijing University of Technology China

To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm (VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and secret sharing algorithm without the invisible trust between nodes required by the algorithm. Meanwhile, the VSSB-Raft consensus algorithm uses the SM2 signature algorithm to realize the characteristics of zero trust requiring authentication before data use. In addition, by introducing the NDN network, we redesign the communication between nodes and guarantee the communication quality among nodes. The VSSB-Raft consensus algorithm proposed in this paper can make the algorithm Byzantine fault tolerant by setting a threshold for secret sharing while maintaining the algorithm's complexity to be O(n). Experiments show that the VSSB-Raft consensus algorithm is secure and efficient with high throughput and low consensus latency. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blockchain

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FSIW Cavity-Based Self-Packaging Filtering Slot Antennas with Wide-Stopband and High Frequency Selectivity

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3503-3516页

作者： Zhou, Xin Zhang, Gang Tam, Kam-Weng Pang, Ziyu Zhang, Zhuowei Lin, Huawei Hong, Jiasheng University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China Nanjing Normal University School of Electrical and Automation Engineering Nanjing210046 China Nanjing Normal University Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing School of Electrical and Automation Engineering Nanjing210046 China Heriot-Watt University School of Engineering and Physical Sciences Department of Electrical Electronic and Computer Engineering EdinburghEH14 4AS United Kingdom

This article proposes a novel design approach for miniaturized, highly selective, self-packaged, and wide-stopband filtering slot antennas based on C- and T-type folded substrate integrated waveguide (C-/T-FSIW) cavities. Firstly, two forms of FSIW cavity are explored and analyzed. A new coupled C-type half-mode FSIW (C-HMFSIW) cavities are developed. A filtering antenna is proposed, employing a C-HMFSIW cavity for feeding and a slot-loaded C-FSIW cavity for radiation. By introducing cross-coupling between different resonators, a radiation null (RN) is achieved. To further enhance bandwidth and frequency selectivity, a new antenna configuration is developed using a C-HMFSIW cavity for feeding and a slot-loaded T-FSIW cavity for radiation. After loading the radiation slot, the T-FSIW cavity serves as two parts: one as the main radiator and the other as a parasitic radiator opposite to it. This configuration increases antenna bandwidth and generates an additional RN. Interestingly, adjusting the position of the radiation slot allows the RN to switch between the upper and lower stopbands. The antennas proposed in this work exhibit good stopband performance and compact size. Three filtering antenna prototypes are designed, fabricated, and measured, showing consistent results between measurements and simulations. © 1963-2012 IEEE.

关键词： Radiators

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Fixed-time-synchronized control: a system-dimension-categorized approach

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Science China(Information Sciences) 2023年第7期66卷 177-194页

作者： Wanyue JIANG Shuzhi Sam GE Dongyu LI Institute for Future & Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Department of Electrical and Computer Engineering National University of Singapore School of Cyber Science and Technology Beihang University Shanghai Institute of Satellite Engineering

This study addresses the fixed-time-synchronized control problem of perturbed multi-input multioutput(MIMO) systems. In the task of fixed-time-synchronized control, different dimensions of the output signal in MIMO systems are required to reach the desired value simultaneously within a fixed time *** MIMO system is categorized into two cases: the input-dimension-dominant and the state-dimensiondominant cases. The classification is defined according to the dimension of system signals and, more importantly, the capability of converging at the same time. For each kind of MIMO system, sufficient Lyapunov conditions for fixed-time-synchronized convergence are explored, and the corresponding robust sliding mode controllers are designed. Moreover, perturbations are compensated using the super-twisting technique. The brake control of the vertical takeoff and landing aircraft is considered to verify the proposed method for the input-dimension-dominant case, which shows the essential advantages of decreasing the energy consumption and the output trajectory length. Furthermore, comparative numerical simulations are performed to show the semi-time-synchronized property for the state-dimension-dominant case.

关键词： fixed-time-synchronized convergence sliding mode control perturbed MIMO systems

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Feedback Optimization of Nonlinear Strict-Feedback Systems

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Journal of Systems Science & Complexity 2025年第2期38卷 717-738页

作者： LIU Tong LIU Tengfei JIANG Zhong-Ping Department of Electrical and Computer Engineering New York University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Feedback optimization aims at regulating the output of a dynamical system to a value that minimizes a cost function. This problem is beyond the reach of the traditional output regulation theory, because the desired value is generally unknown and the reference signal evolves according to a gradient flow using the system's real-time output. This paper complements the output regulation theory with the nonlinear small-gain theory to address this challenge. Specifically, the authors assume that the cost function is strongly convex and the nonlinear dynamical system is in lower triangular form and is subject to parametric uncertainties and a class of external disturbances. An internal model is used to compensate for the effects of the disturbances while the cyclic small-gain theorem is invoked to address the coupling between the reference signal, the compensators, and the physical system. The proposed solution can guarantee the boundedness of the closed-loop signals and regulate the output of the system towards the desired minimizer in a global sense. Two numerical examples illustrate the effectiveness of the proposed method.

关键词： Feedback optimization internal model output regulation small-gain theorem

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Object Helps U-Net Based Change Detectors

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 548-550页

作者： Lan Yan Qiang Li Kenli Li College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter focuses on leveraging the object information in images to improve the performance of the U-Net based change *** detection is fundamental to many computer vision *** existing solutions based on ... 详细信息

关键词： Net computer networks

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T-SNVAE: Deep probabilistic learning with local and global structure for industrial process monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1603-1613页

作者： Huang, Jian Liu, Zizhuo Yang, Xu Liu, Yupeng Lv, Zhaomin Peng, Kaixiang Ersoy, Okan K. University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering China University of Science and Technology Beijing Shunde Innovation School Foshan China Shanghai University of Engineering Science School of Urban Railway Transportation China Purdue University Electrical and Computer Engineering West Lafayette United States

Variational autoencoder is a generative deep learning model with a probabilistic structure, which makes it tolerant to process uncertainties and more suitable for process monitoring. However, the probabilistic model may disrupt the topological structure of data and lead to the loss of neighborhood information. To address this issue, a process monitoring approach based on t-distributed stochastic neighbor variational autoencoder (t- SNVAE) is proposed to capture probabilistic features that elucidate both local and global structures within the raw data. Specifically, the distances between neighboring data points are transformed into joint probabilities by using t-SN Embedding. Through minimizing the Kullback-Leibler divergence of joint probabilities between the original data and the reconstructed data, variational autoencoder learns Gaussian features containing both local and global neighborhood information. Finally, monitoring statistics are constructed for monitoring. The efficiency of the proposed approach is verified on a multiphase flow facility and a waste-water treatment process. © 2020 IEEE.

关键词： Embeddings

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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An LSTM-based Path Switching Approach for Edge Gateways in Industrial Heterogeneous Networks 4

An LSTM-based Path Switching Approach for Edge Gateways in I...

引用

4th IEEE International Conference on Data Science and computer Application, ICDSCA 2024

作者： Chen, Zheyi Du, Dajun Hu, Huosheng Shanghai Key Laboratory of Power Station Automation Technology School of Mechanical Engineering and Automation Shanghai University 200072 China School of Computer Science and Electronic Engineering University of Essex Essex United Kingdom

ISBN: (纸本)9798350368239

Traditional path-switching methods are primarily designed for static or conventional network environments, rendering them inadequate for the dynamic nature of heterogeneous industrial networks. This paper aims to tackle the dual challenges of efficient path switching and accurate traffic prediction in such environments. First, an LSTM-based predictive model is developed to forecast critical network performance metrics, such as delay and load, facilitating proactive path adjustments. Second, a dynamic path-switching strategy is introduced, taking advantage of these predictions to minimize path oscillations and maintain stable network performance under varying traffic conditions. Finally, the performance of the proposed method is evaluated using key metrics, including latency, load distribution, and path stability. Simulation and experimental results confirm the effectiveness of the approach, demonstrating significant improvements in latency reduction, load balance, and overall network efficiency within industrial heterogeneous networks. ©2024 IEEE.

关键词： Gateways (computer networks)

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A Review on Cybersecurity Analysis,Attack Detection,and Attack Defense Methods in Cyber-physical Power Systems

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Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 727-743页

作者： Dajun Du Minggao Zhu Xue Li Minrui Fei Siqi Bu Lei Wu Kang Li Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and AutomationShanghai UniversityShanghaiChina Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA School of Electronic and Electrical Engineering University of LeedsLeeds LS29JTUK IEEE

Potential malicious cyber-attacks to power systems which are connected to a wide range of stakeholders from the top to tail will impose significant societal risks and *** timely detection and defense are of crucial importance for safe and reliable operation of cyber-physical power systems(CPPSs).This paper presents a comprehensive review of some of the latest attack detection and defense ***,the vulnerabilities brought by some new information and communication technologies(ICTs)are analyzed,and their impacts on the security of CPPSs are *** malicious cyber-attacks on cyber and physical layers are then analyzed within CPPSs framework,and their features and negative impacts are ***,two current mainstream attack detection methods including state estimation based and machine learning based methods are analyzed,and their benefits and drawbacks are ***,two current mainstream attack defense methods including active defense and passive defense methods are comprehensively ***,the trends and challenges in attack detection and defense strategies in CPPSs are provided.

关键词： Cyber-physical power systems security threat attack detection attack defense state estimation machine learning

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Blockchain based federated learning for intrusion detection for Internet of Things

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Frontiers of computer Science 2024年第5期18卷 109-119页

作者： Nan SUN Wei WANG Yongxin TONG Kexin LIU School of Cyber Science and Technology Beihang UniversityBeijing 100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100094China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

In Internet of Things(loT),data sharing among different devices can improve manufacture efficiency and reduce workload,and yet make the network systems be more vulnerable to various intrusion *** has been realistic demand to develop an efficient intrusion detection algorithm for connected *** of existing intrusion detection methods are trained in a centralized manner and are incapable to identify new unlabeled attack *** this paper,a distributed federated intrusion detection method is proposed,utilizing the information contained in the labeled data as the prior knowledge to discover new unlabeled attack ***,the blockchain technique is introduced in the federated learning process for the consensus of the entire *** results are provided to show that our approach can identify the malicious entities,while outperforming the existing methods in discovering new intrusion attack types.

关键词： intrusion detection federated learning new attacks discovering blockchain

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