Event-triggered distributed filtering over sensor networks with deception attacks and partial measurements

Bu, Xianye; Dong, Hongli; Han, Fei; Li, Gongfa

doi:10.1080/03081079.2018.1462353

Cited by 40 publications

(13 citation statements)

References 21 publications

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“…The main purpose of feature extraction is to map complex signals from high dimensional space to low dimensional space by using signal characteristics, and make classification simple and intuitive in data background [47][48][49]. Therefore, after a long period of exploration and research, many related researchers have divided the feature extraction into three main directions: time domain analysis, frequency domain analysis, time frequency analysis [50][51].…”

Section: Feature Extraction Of Surface Emg Signalmentioning

confidence: 99%

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Jiang

et al. 2018

Neural Comput & Applic

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107

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In the current development and design of sports rehabilitation equipment or biomimetic prostheses, in addition to pay attention to the development and design of the structure, the more core is how to realize the accurate and effective control of the rehabilitation equipment or intelligent prosthesis, and the current research is based on data process and pattern recognition. This paper designs 9 kinds of actions that can react effectively to the function of the hand and extracts the original EMG signals, which are based on the sEMG of the forearm muscles of human hand movement, and uses the 20 order comb filter and wavelet threshold to preprocess the signal, and uses the root mean square, wavelength and nonlinear characteristics sample entropy in time domain as three eigenvalues to construct the input feature vectors of the subsequent action classifier. Finally, the recognition of the hand movements is realized successfully through GRNN and SVM. The recognition rate is 98.64% in SVM classifier and 96.27% in GRNN classifier. Experimental results show that the SVM classifier is better than the GRNN classifier.

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Section: Feature Extraction Of Surface Emg Signalmentioning

confidence: 99%

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Jiang

et al. 2018

Neural Comput & Applic

Self Cite

107

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“…More recently, surveys on deception attacks began to appear, see [34][35][36][37][38][39]. For instance, in [34], the most general model of sensor attack resilient has been proposed, which can allow any signal to be injected through the compromised sensor.…”

Section: Related Workmentioning

confidence: 99%

“…The effect of stealthy integrity attacks on Networked physical systems has been analyzed in [36], where the target system was a Stochastic Linear Time-Invariant (LTI) system with a linear estimator, a linear feedback controller and a χ 2 fault detector. In [35,37,38], the distributed state estimator, which was used to defend against false data injection attacks over sensor networks, has been embedded with event-triggering transmission scheme. Based on clustering technology, which can remove bad data and/or inaccurate estimates, a distributed Kalman filtering approach with trust-based dynamic combination strategy has been proposed in [39] to enhance the robustness against cyber attacks.…”

Section: Related Workmentioning

confidence: 99%

“…Unfortunately, the first factor prevents existing methods (Kalman filtering, extended Kalman filtering, see [11,14,19,39]) from being applied to distributed state estimation problems for sensor networked systems with unknown interference. On the other hand, the second factor is ignored by many existing H ∞ methods, see [16,38,42,43], which often focus on designing distributed full-order filters over WSNs.…”

Section: Simulation Examplementioning

confidence: 99%

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Distributed Robust Filtering for Wireless Sensor Networks with Markov Switching Topologies and Deception Attacks

Zhu

Liu

Wen

et al. 2020

Sensors

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This paper is concerned with the distributed full- and reduced-order l 2 - l ∞ state estimation issue for a class of discrete time-invariant systems subjected to both randomly occurring switching topologies and deception attacks over wireless sensor networks. Firstly, a switching topology model is proposed which uses homogeneous Markov chain to reflect the change of filtering networks communication modes. Then, the sector-bound deception attacks among the communication channels are taken into consideration, which could better characterize the filtering network communication security. Additionally, a random variable obeying the Bernoulli distribution is used to describe the phenomenon of the randomly occurring deception attacks. Furthermore, through an adjustable parameter E, we can obtain full- and reduced-order l 2 - l ∞ state estimator over sensor networks, respectively. Sufficient conditions are established for the solvability of the addressed switching topology-dependent distributed filtering design in terms of certain convex optimization problem. The purpose of solving the problem is to design a distributed full- and reduced-order filter such that, in the presence of deception attacks, stochastic external interference and switching topologies, the resulting filtering dynamic system is exponentially mean-square stable with prescribed l 2 - l ∞ performance index. Finally, a simulation example is provided to show the effectiveness and flexibility of the designed approach.

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“…In networked control systems (NCS) it is a viable solution either to avoid delays (Cuenca et al 2011) or to save network load by sending less information across the shared medium. Due to the current importance of the NCS applications, other non-conventional sampling schemes (like DRS) for dealing with communication bandwidth restrictions, such as event-triggered sampling, have been lately proposed (Hou et al 2018;Bu et al 2018;Zhao et al 2018; CONTACT Author Julián Salt. ORCID: 0000-0002-9640-2658.…”

Section: Motivationmentioning

confidence: 99%

Dual-rate sampled-data systems. Some interesting consequences from its frequency response analysis

Salt

Alcaína

2019

International Journal of General Systems

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The main goal of this contribution is to introduce a new procedure in order to analyze properly SISO dual-rate systems (DRS) and to provide straightforward answers to some common general questions about this kind of systems. Frequency response analysis based on DRS lifting modeling can lead to interesting results about stability margins or performance prediction. As a novelty, it is explained how to understand DRS frequency response and how to handle it for an easy computation of magnitude and phase margins keeping classical frequency domain methods. There are also some repetitive questions about DRS that can be analyzed and answered properly using the results from this contribution: what the optimum relation between sampling periods is or what effects does delay have in a DRS . Every step is illustrated with examples that should clarify the understanding of the text.

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Event-triggered distributed filtering over sensor networks with deception attacks and partial measurements

Cited by 40 publications

References 21 publications

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Distributed Robust Filtering for Wireless Sensor Networks with Markov Switching Topologies and Deception Attacks

Dual-rate sampled-data systems. Some interesting consequences from its frequency response analysis

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