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Set-valued Kalman filtering: Event triggered communication with quantized measurements

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Abstract

This paper is concerned with remote state estimation problem with bandwidth and energy constrained wireless sensor networks(WSNs). To improve the estimation quality under these constrains in addressed system, measurement quantization and event-triggered communication strategies are adopted in WSN. Specifically, quantization strategy and event-triggering mechanism are introduced to describe the set region of original measurements, and a closest ellipsoid approximation of measurement sets method is presented. Subsequently, set-valued Kalman filter based on quantization and event is designed by utilizing the quantizer and trigger information. Finally, an illustrative example is employed to demonstrate the advantages and effectiveness of the proposed methods.

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References

  1. Chen B, Zhang W, Yu L (2014) Distributed finite-horizon fusion Kalman filtering for bandwidth and energy constrained wireless sensor networks. IEEE Trans Signal Process 62:797–812

    Article  MathSciNet  MATH  Google Scholar 

  2. Hadjidj A, Souil et al (2013) Wireless sensor networks for rehabilitation applications: Challenges and opportunities. J Net Computer Appl 36:1–15

    Article  Google Scholar 

  3. Yang G, He S, Shi Z, Chen J (2017) Promoting cooperation by the social incentive mechanism in mobile crowdsensing. IEEE Commun Mag 55(3):86–92

    Article  Google Scholar 

  4. Chen B, Ho DW, Zhang W, Yu L (2017) Networked fusion estimation with bounded noises. IEEE Trans Autom Control 62:5415–5421

    Article  MathSciNet  MATH  Google Scholar 

  5. Duan X, Zhao C, He S, Cheng P, Zhang J (2017) Distributed algorithms to compute walrasian equilibrium in mobile crowdsensing. IEEE Trans Ind Electron 64(5):4048–4057

    Article  Google Scholar 

  6. Zhang H, Meng W, Qi J, Wang X, Zheng W (2018) Distributed load sharing under false data injection attack in inverter-based microgrid. IEEE Trans Ind Electron. https://doi.org/10.1109/TIE.2018.2793241

  7. Zhu Y, Zhong Z, Zheng W, Zhou D (2017) HMM-based \(H_{\infty }\) filtering for discrete-time Markov jump LPV systems over unreliable communication channels. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/TSMC.2017.2723038

  8. Sui T, You K, Fu M (2015) Optimal sensor scheduling for state estimation over lossy channel. IET Control Theory Appl 9:2458–2465

    Article  MathSciNet  Google Scholar 

  9. Yang G, He S, Shi Z (2017) Leveraging crowdsourcing for efficient malicious users detection in large-scale social networks. IEEE Internet Things J 4(2):330–339

    Article  Google Scholar 

  10. Zhang H, Zheng WX (2018) Denial-of-Service power dispatch against linear quadratic control via a fading channel. IEEE Trans Autom Control. https://doi.org/10.1109/TAC.2018.2789479

  11. Zhou H, Xu S, Ren D, Huang C, Zhang H (2017) Analysis of event-driven warning message propagation in vehicular ad hoc networks. Ad Hoc Netw 55:87–96

    Article  Google Scholar 

  12. He S, Shin D, Zhang J, Chen J, Sun Y (2016) Full-view area coverage in camera sensor networks: Dimension reduction and near-optimal solution. IEEE Trans Veh Technol 65(9):7448–7461

    Article  Google Scholar 

  13. Zhu Y, Zhang L, Zheng W (2016) Distributed \(H_{\infty }\) filtering for a class of discrete-time Markov jump Lur’e systems with redundant channels. IEEE Trans Ind Electron 63(3):1876–1885

    Article  Google Scholar 

  14. Chen J, Hu K, Wang Q, Sun Y, Shi Z, He S (2017) Narrow-band internet of things: Implementations and applications. IEEE Internet Things J 4(6):2309–2314

    Article  Google Scholar 

  15. Luo Z (2005) Universal decentralized estimation in a bandwidth constrained sensor network. IEEE Trans Info Theory 51:2210–2219

    Article  MathSciNet  MATH  Google Scholar 

  16. Wen C, Ge Q, Tang X (2009) Kalman filtering in a band-width constrained sensor network. Chinese J Electron 18:713–718

    Google Scholar 

  17. Xiao J, Cui S, Luo Z (2006) Power scheduling of universal decentralized estimation in sensor networks. IEEE Trans Signal Process 54:413–422

    Article  MATH  Google Scholar 

  18. Dong H, Wang Z, Ding S et al (2015) Finite-horizon reliable control with randomly occurring uncertainties and nonlinearities subject to output quantization. Automatica 52:355–362

    Article  MathSciNet  MATH  Google Scholar 

  19. Sun S, Lin J et al (2007) Quantized Kalman filtering. IEEE Int Conf Intelligent Control, 7–12

  20. Zhou Y, Huang C, Jiang T et al (2013) Wireless sensor networks and the internet of things: Optimal estimation with nonuniform quantization and bandwidth allocation. EEE Trans Sensors J 13:3568–3574

    Article  Google Scholar 

  21. Shi D (2014) Event-based state estimation in cyber-physical systems. PhD thesis, Alberta University

  22. Wu J, Jia Q, Johansson K et al (2013) Event-based sensor data scheduling: Trade-off between communication rate and estimation quality. IEEE Trans Autom Control 58:1041–1045

    Article  MathSciNet  MATH  Google Scholar 

  23. Chen B, Zhang W, Yu L (2014) Distributed fusion estimation with missing measurements, random transmission delays and packet dropouts. IEEE Trans Autom Control 59:1961–1967

    Article  MathSciNet  MATH  Google Scholar 

  24. Tang Z, Ju H, Feng J (2017) Impulsive effects on quasi-synchronization of neural networks with parameter mismatches and time-varying delay. IEEE Trans Neural Netw Learn Syst

  25. Tang Z, Ju H, Feng J (2018) Novel approaches to pin cluster synchronization on complex dynamical networks in Lure forms. Commun Nonl Science Numer Simul 57:422–438

    Article  Google Scholar 

  26. Miskowicz M (2016) Send-on-delta concept: An event-based data reporting strategy. Sensors 6:49–63

    Article  Google Scholar 

  27. Chen B, Hu G, Zhang W et al (2014) Distributed mixed \( H_{2}/H_{\infty } \) fusion estimation with limited communication capacity. IEEE Trans Autom Control 61:805–810

    Article  MATH  Google Scholar 

  28. Zhang H, Qi Y, Zhou H et al (2017) Testing and defending methods against DOS attack in state estimation. Asian J Control 19:1295–1305

    Article  MathSciNet  MATH  Google Scholar 

  29. Zhang H, Qi Y, Wu J et al DoS attack energy management against remote state estimation. IEEE Trans Control Netw Syst. https://doi.org/10.1109/TCNS.2016.2614099

  30. Morrell D, Stirling W (1991) Set-valued filtering and smoothing. IEEE Trans Syst Man Cyber 21:184–193

    Article  MathSciNet  MATH  Google Scholar 

  31. Noack B, Klumpp V, Hanebeck U (2009) State estimation with sets of densities considering stochastic and systematic errors. In: Proc Int Conf Information Fusion. Seattle, pp 1751–1758

  32. Shi D, Chen T, Shi L (2015) On set-valued Kalman filtering and its application to event-based state estimation. IEEE Trans Autom Control 60:1275–1290

    Article  MathSciNet  MATH  Google Scholar 

  33. Zhang H, Cheng P, Shi L, Chen J (2015) Optimal denial-of-service attack scheduling with energy constraint. IEEE Trans Autom Control 60(11):3023–3028

    Article  MathSciNet  MATH  Google Scholar 

  34. Zhang H, Cheng P, Shi L, Chen J (2016) Optimal DoS attack scheduling in wireless networked control system. IEEE Trans Control Syst Technol 24(3):843–852

    Article  Google Scholar 

  35. Krasnopeev A, Xiao J, Luo Z (2005) Minimum energy decentralized estimation in a wireless sensor network with correlated sensor noises. EURASIP J Wirel Commun and Netw 4:473–482

    MATH  Google Scholar 

  36. Wagdy MF, Ng WM (1989) Validity of uniform quantization error model for sinusoidal signals without and with dither. IEEE Trans Instrum Meas 38:718–722

    Article  Google Scholar 

  37. Proakis JG (2001) Companders. Wiley

  38. Kurzhanski, Vlyi A (1997) Ellipsoidal calculus for estimation and control. Nelson Thornes

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Acknowledgments

This work was supported by the NSFC under Grant U1709213, 61673351, 61503147, 61403229, the National Nature Science Foundation of Jiangsu Province under Grant BK20171264, and the Zhejiang Provincial Natural Science Foundation of China under Grant LZ15F030003.

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Correspondence to Heng Zhang.

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This article is part of the Topical Collection: Special Issue on Network Coverage

Guest Editors: Shibo He, Dong-Hoon Shin, and Yuanchao Shu

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Xu, D., Qin, Y., Zhang, H. et al. Set-valued Kalman filtering: Event triggered communication with quantized measurements. Peer-to-Peer Netw. Appl. 12, 677–688 (2019). https://doi.org/10.1007/s12083-018-0657-x

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