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distributed consensus filtering Over Sensor Networks With Asynchronous Measurements

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2025年第1期39卷 101-115页

作者： Hu, Yanyan Lin, Xufeng Peng, Kaixiang Univ Sci & Technol Beijing Sch Intelligence Sci & Technol Beijing Peoples R China Univ Sci & Technol Beijing Key Lab Intelligent Bion Unmanned Syst Minist Educ Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

In practical sensor networks, sensor nodes may operate with different sampling periods and initial sampling time instants, and their observations may also be nonuniform. Unfortunately, the research on distributed state estimation problems over such asynchronous sensor networks is very limited. Thus, this article focuses on the distributed consensus filtering problem over sensor networks with asynchronous measurements. First, the asynchronous measurement from each sensor is synchronized by the continuous-time state evolution equation to a unified filtering fusion time instant within a given filtering period. After measurement synchronization, the statistical characteristics of measurement noise change. The cross-correlations between the converted measurement noises are analyzed, as well as one-step correlations between the converted measurement noises and the process noise. Second, an optimal asynchronous distributed consensus filter is designed based on synchronization measurements under the criterion of minimum mean-square error with the above correlations between various types of noises taken into account. Meanwhile, a suboptimal distributed consensus filtering algorithm is further proposed to reduce computational complexity. Finally, based on the Lyapunov function method, the stability of the estimation error is theoretically demonstrated with an appropriate selection of consensus filtering gain and validated through simulations. image Actually, in sensor networks, different sensor nodes may operate with different sampling periods and initial sampling time instants, and their observations may also be non-uniform. To deal with this problem, this paper contributes by designing a novel asynchronous distributed consensus filtering algorithm. A suboptimal consensus filtering algorithm is further proposed to reduce computational complexity. The estimation error is theoretically demonstrated and validated through a target tracking simulation experiment.

关键词： asynchronous sensor networks distributed consensus filtering measurement synchronization

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Event-Triggered distributed consensus filtering Based on Interval Analysis for Heterogeneous Multienergy Networks With Coupling Output Constraints

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第8期70卷 8296-8304页

作者： Su, Lei Zhao, Jun Wang, Wei Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equip Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China

This article addresses the distributed consensus filtering problem of heterogeneous multienergy networks, where subnetworks exhibit different dynamics and are coupled by output constraints. The global consensus is reached by propagating constraints between subnetworks in the form of interval vectors. Guaranteed intervals satisfying given constraints are returned, which are then used to correct the local states through the probability density function truncation. Given the dependence and wrapping effects during constraint propagation, a hybrid Newton forward-backward propagation (FBP) contractor is proposed for avoiding overly pessimistic estimates. It extends the FBP contractor by replacing the natural inclusion function with centered forms. In addition, an eventtriggered mechanism is designed to enhance the efficiency of constraint propagation, which allows the threshold values to be adaptively adjusted according to interval widths. Finally, the proposal is assessed in a steel industrial gaselectricity network with 25 nodes, and the results verify the performance in estimation accuracy, especially for coupling nodes.

关键词： Couplings filtering Probability density function Pipelines Energy conversion Convex functions Convergence distributed consensus filtering event-triggered heterogeneous interval analysis multienergy network (MENs)

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Security Analysis of distributed consensus filtering Under Replay Attacks

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第6期54卷 3526-3539页

作者： Huang, Jiahao Yang, Wen Ho, Daniel W. C. Li, Fangfei Tang, Yang East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China City Univ Hong Kong Dept Math Hong Kong Peoples R China East China Univ Sci & Technol Dept Math Shanghai 200237 Peoples R China

This work studies the security of consensus-based distributed filtering under the replay attack, which can freely select a part of sensors and modify their measurements into previously recorded ones. We analyze the performance degradation of distributed estimation caused by the replay attack, and utilize the Kullback-Leibler (K-L) divergence to quantify the attack stealthiness. Specifically, for a stable system, we prove that under any replay attack, the estimation error is not only bounded, but also can re-enter the steady state. In that case, we prove that the replay attack is ? -stealthy, where ? can be calculated based on two Lyapunov equations. On the other hand, for an unstable system, we prove that the trace of estimation error covariance is lower bounded by an exponential function, which indicates that the estimation error may diverge due to the attack. In view of this, we provide a sufficient condition to ensure that any replay attack is detectable. Furthermore, we analyze the case that the adversary starts to attack only if the current measurement is close to a previously recorded one. Finally, we verify the theoretical results via several numerical simulations.

关键词： Sensors Security Estimation error Control systems Covariance matrices Detectors Wireless sensor networks Cyber security cyber-physical systems (CPSs) distributed consensus filtering replay attack

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Security Analysis of distributed consensus filtering under Replay Attacks

Security Analysis of Distributed Consensus Filtering under R...

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第32届中国过程控制会议(CPCC2021)

作者： Jiahao Huang Yang Tang Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology

In this work,we consider a security problem of consensus-based distributed *** if without the knowledge of system model,the replay attack can arbitrarily determine a part of sensors to falsify their measurements by repeating the previously recorded sensor *** is proven that for a stable system,the estimation error covariance under any given attack strategy not only is bounded,but also can re-enter into the steady *** Kullback-Leibler(K-L) divergence that describes the detection performance is upper bounded as well,so that the attack *** the other hand,when the system is unstable,we prove that the trace of estimation error covariance is lower bounded by an exponential function,which implies that the adversary may severely damage the estimation *** guarantee that all of the replay attacks are detectable,we propose a criteria to design the system ***,we analyze the case that the adversary waits to attack until the current measurement is close to the initial part of the *** simulations are provided to illustrate the theoretical results in the end.

关键词： Cyber-Physical Systems Cyber Security Replay Attack distributed consensus filtering

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distributed Matrix-Weighted Fusion consensus filtering With Two-Stage filtering for Sensor Networks

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IEEE SENSORS JOURNAL 2023年第5期23卷 5003-5013页

作者： Wang, Ruixi Sun, Shuli Heilongjiang Univ Sch Elect Engn Harbin Peoples R China

This article is concerned with a distributed consensus filtering problem in a sensor network (SN). In the SN, each sensor node plays a local fusion center. By diffusion and fusion of information among sensor nodes, estimates of all nodes in the SN tend to consensus. At each sensor node, a distributed consensus filtering algorithm with a two-stage filtering structure is devised. At each time, the first stage filtering is that each sensor node produces its local filter estimate based on its own measurement and previous fused estimate, and then transmits it to its neighbor nodes;and the second stage filtering is that each sensor node produces a fused estimate by using a matrix-weighted optimal fusion algorithm in the linear unbiased minimum variance (LUMV) criterion to iteratively fuse estimates of sensor itself and neighbor nodes, where cross-covariance matrices (CCMs) between sensor nodes at different fusion times are derived. Its estimation accuracy is gradually improved with the increase of fusion times and approximates a centralized fusion filter (CFF). To avoid calculation of CCMs between sensor nodes, a suboptimal distributed consensus filtering algorithm is also presented by minimizing an upper bound of the fusion filtering error covariance at each fusion in the second stage. It has reduced computational cost at the expense of accuracy loss. The performance of the proposed distributed consensus filters is analyzed. A tracking system is used to verify the effectiveness of the algorithms.

关键词： Sensors Sensor fusion Estimation filtering algorithms Information filters Kalman filters Covariance matrices Cross-covariance matrix (CCM) distributed consensus filtering linear unbiased minimum variance (LUMV) matrix-weighted fusion sensor network (SN)

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Self-Triggered consensus filtering over Asynchronous Communication Sensor Networks

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Computer Modeling in Engineering & Sciences 2023年第2期134卷 857-871页

作者： Huiwen Xue Jiwei Wen Akshya Kumar Swain Xiaoli Luan Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan UniversityWuxi214000China Department of Electrical Computer and Software EngineeringUniversity of AucklandAuckland0632New Zealand

In this paper,a self-triggered consensus filtering is developed for a class of discrete-time distributed filtering *** from existing event-triggered filtering,the self-triggered one does not require to continuously judge the trigger condition at each sampling instant and can save computational burden while achieving good state *** triggering policy is presented for pre-computing the next execution time for measurements according to the filter’s own data and the latest released data of its neighbors at the current ***,a challenging problem is that data will be asynchronously transmitted within the filtering network because each node self-triggers ***,a co-design of the self-triggered policy and asynchronous distributed filter is developed to ensure consensus of the state ***,a numerical example is given to illustrate the effectiveness of the consensus filtering approach.

关键词： Self-triggered policy sensor networks distributed consensus filtering

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Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2015年第3期12卷 901-921页

作者： Chen, Jie Low, Kian Hsiang Yao, Yujian Jaillet, Patrick Singapore MIT Alliance Res & Technol Singapore 138602 Singapore Natl Univ Singapore Dept Comp Sci Singapore 119077 Singapore MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA

Mobility-on-demand (MoD) systems have recently emerged as a promising paradigm of one-way vehicle sharing for sustainable personal urban mobility in densely populated cities. We assume the capability of a MoD system to be enhanced by deploying robotic shared vehicles that can autonomously cruise the streets to be hailed by users. A key challenge of the MoD system is that of real-time, fine-grained mobility demand and traffic flow sensing and prediction. This paper presents novel Gaussian process (GP) decentralized data fusion and active sensing algorithms for real-time, fine-grained traffic modeling and prediction with a fleet of MoD vehicles. The predictive performance of our decentralized data fusion algorithms are theoretically guaranteed to be equivalent to that of sophisticated centralized sparse GP approximations. We derive consensus filtering variants requiring only local communication between neighboring vehicles. We theoretically guarantee the performance of our decentralized active sensing algorithms. When they are used to gather informative data for mobility demand prediction, they can achieve a dual effect of fleet rebalancing to service mobility demands. Empirical evaluation on real-world datasets shows that our algorithms are significantly more time-efficient and scalable in the size of data and fleet while achieving predictive performance comparable to that of state-of-the-art algorithms. Note to Practitioners-Knowing, understanding, and predicting spatiotemporally varying traffic phenomena in real time has become increasingly important to the goal of achieving smooth-flowing, congestion-free traffic in densely populated urban cities, which motivates our work here. This paper addresses the following fundamental problem of data fusion and active sensing: How can a fleet of autonomous robotic vehicles or mobile probes actively cruise a road network to gather and assimilate the most informative data for predicting a spatiotemporally varying traffic pheno

关键词： Active learning adaptive sampling decentralized active sensing decentralized/distributed data fusion distributed consensus filtering environmental sensing and monitoring Gaussian process log-Gaussian process mobility demand prediction relational Gaussian process spatiotemporal modeling traffic flow forecasting vehicular sensor network

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