Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Distributed event-triggered state estimators design for sensor networked systems with deception attacks

  • Author(s): Jinliang Liu 1, 2 ; Lili Wei 3 ; Xiangpeng Xie 4 ; Dong Yue 4
    • View affiliations
  • Source: Volume 13, Issue 17, 26 November 2019, p. 2783 – 2791
    DOI:  10.1049/iet-cta.2018.5868 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received 06/08/2018, Accepted 01/11/2018, Revised 17/10/2018, Published 02/11/2018

This study addresses the issue of distributed event-triggered state estimators subject to deception attacks for sensor networked systems. A decentralised event-triggered scheme (ETS) is introduced to determine whether the sampling data of each sensor is transmitted or not, respectively. In this scheme, each sensor node is independent to decide to deliver the local measurement output through the corresponding ETS. Due to the insertion of the network, the effect of the deception attacks along with time delay and packet dropouts are considered in this study. A novel estimator network is established to realise the estimation of the decoupling output measurements and coupling intercommunication measurements. Firstly, a distributed event-triggered estimating system with deception attacks is constructed in a mathematical model. Secondly, sufficient conditions are derived, which can ensure the stability of the designed estimating error systems and the related parameters of the desired distributed estimators are presented in an accurate form. Finally, a simulated example is given to demonstrate the effectiveness of the designed event-triggered distributed state estimator systems under the deception attacks.

Inspec keywords: delays; asymptotic stability; state estimation; wireless sensor networks; decentralised control; H∞ control; networked control systems; distributed control; control system synthesis

Other keywords: sensor node; H∞ estimating error system; decentralised event-triggered scheme; ETS; intercommunication measurement; mathematical model; local measurement output; time delay; packet dropouts; stability; decoupling output measurements; sensor networked systems; deception attacks; distributed event-triggered state estimators design

Subjects: Control system analysis and synthesis methods; Distributed parameter control systems; Stability in control theory; Filtering methods in signal processing; Multivariable control systems; Simulation, modelling and identification; Optimal control; Wireless sensor networks

References

    1. 1)
      • 31. Patil, S., Chaudhari, S.: ‘Dos attack prevention technique in wireless sensor networks’, Procedia Comput. Sci., 2016, 79, pp. 715–721.
    2. 2)
      • 39. Guan, Y., Ge, X.: ‘Distributed attack detection and secure estimation of networked cyber-physical systems against false data injection attacks and jamming attacks’, IEEE Trans. Signal Inf. Process., 2018, 4, (1), pp. 48–59.
    3. 3)
      • 34. Ma, L., Wang, Z., Han, Q.L., et al: ‘Variance-constrained distributed filtering for time-varying systems with multiplicative noises and deception attacks over sensor networks’, IEEE Sensors J., 2017, 17, (7), pp. 2279–2288.
    4. 4)
      • 13. He, S.: ‘Fault detection filter design for a class of nonlinear Markovian jumping systems with mode-dependent time-varying delays’, Nonlinear Dyn., 2018, 91, (3), pp. 1871–1884.
    5. 5)
      • 30. Yang, C., Yang, W., Shi, H.: ‘DoS attack in centralised sensor network against state estimation’, IET Control Theory Appl., 2018, 12, (9), pp. 1244–1253.
    6. 6)
      • 24. Li, Y., Yang, G.H., Tong, S.: ‘Fuzzy adaptive distributed event-triggered consensus control of uncertain nonlinear multiagent systems’, IEEE Trans. Syst. Man Cybern., Syst., 2018, doi: 10.1109/TSMC.2018.2812216.
    7. 7)
      • 35. Liu, J., Gu, Y., Xie, X., et al: ‘Hybrid-driven-based H∞ control for networked cascade control systems with actuator saturations and stochastic cyber attacks’, IEEE Trans. Syst. Man Cybern., Syst., 2018, available at https://doi.org/10.1109/TSMC.2018.2875484.
    8. 8)
      • 49. Zhang, W.A., Dong, H., Guo, G., et al: ‘Distributed sampled-data H∞ filtering for sensor networks with nonuniform sampling periods’, IEEE Trans Ind. Inf., 2014, 10, (2), pp. 871–881.
    9. 9)
      • 20. Peng, C., Ma, S., Xie, X.: ‘Observer-based non-PDC control for networked T–S fuzzy systems with an event-triggered communication’, IEEE Trans. Cybern., 2017, 99, pp. 1–9.
    10. 10)
      • 45. Park, P.G., Ko, J.W., Jeong, C.: ‘Reciprocally convex approach to stability of systems with time-varying delays’, Automatica, 2011, 47, (1), pp. 235–238.
    11. 11)
      • 7. Wu, D., Yang, T., Anton, A.S., et al: ‘Distributed optimal coordination for distributed energy resources in power systems’, IEEE Trans. Autom. Sci. Eng., 2017, 14, (2), pp. 414–424.
    12. 12)
      • 8. Zhang, W., Tang, Y., Huang, T., et al: ‘Sampled-data consensus of linear multi-agent systems with packet losses’, IEEE Trans. Neural Netw. Learning Syst., 2017, 28, (11), pp. 2516–2527.
    13. 13)
      • 47. Tian, E., Yue, D.: ‘Reliable H∞ filter design for T–S fuzzy model-based networked control systems with random sensor failure’, Int. J. Robust Nonlin., 2013, 23, (1), pp. 15–32.
    14. 14)
      • 48. Chen, B., Hu, G., Ho, D.W.C., et al: ‘Distributed robust fusion estimation with application to state monitoring systems’, IEEE Trans. Syst. Man Cybern., 2017, 47, (11), pp. 2994–3005.
    15. 15)
      • 1. Ma, Y., Zhao, J.: ‘Distributed event-triggered consensus using only triggered information for multi-agent systems under fixed and switching topologies’, IET Control Theory Appl., 2018, 12, (9), pp. 1357–1365.
    16. 16)
      • 22. Li, C., Yu, X., Yu, W., et al: ‘Distributed event-triggered scheme for economic dispatch in smart grids’, IEEE Trans Ind. Inf., 2016, 12, (5), pp. 1775–1785.
    17. 17)
      • 41. Yue, D., Han, Q.L., Lam, J.: ‘Network-based robust H∞ control of systems with uncertainty’, Automatica, 2005, 41, pp. 999–1007.
    18. 18)
      • 5. Ji, H., Lewis, F.L., Hou, Z., et al: ‘Distributed information-weighted Kalman consensus filter for sensor networks’, Automatica, 2017, 77, pp. 18–30.
    19. 19)
      • 12. He, S., Song, J., Liu, F.: ‘Robust finite-time bounded controller design of time-delay conic nonlinear systems using sliding mode control strategy’, IEEE Trans. Syst. Man Cybernet. Syst., 2018, 48, (11), pp. 1863–1873.
    20. 20)
      • 40. Ding, L., Guo, G.: ‘Distributed event-triggered H∞ consensus filtering in sensor networks’, Signal Process., 2015, 108, pp. 365–375.
    21. 21)
      • 38. Yang, W., Lei, L., Yang, C.: ‘Event-based distributed state estimation under deception attack’, Neurocomputing, 2017, 270, pp. 145–151.
    22. 22)
      • 2. Liu, J., Tian, E., Xie, X., et al: ‘Distributed event-triggered control for networked control systems with stochastic cyber-attacks’, J. Frankl. Inst., 2018, available at https://doi.org/10.1016/j.jfranklin.2018.01.048.
    23. 23)
      • 28. Ding, D., Wei, G., Zhang, S., et al: ‘On scheduling of deception attacks for discrete-time networked systems equipped with attack detectors’, Neurocomputing, 2017, 219, (5), pp. 99–106.
    24. 24)
      • 33. Khandaker, A.R., Kiran, S.B., Vir, V.P.: ‘Snoop-forge-replay attacks on continuous verification with keystrokes’, IEEE Trans. Inf. Forensics Sec., 2013, 8, (3), pp. 528–541.
    25. 25)
      • 36. Yu, Z.H., Chin, W.L.: ‘Blind false data injection attack using PCA approximation method in smart grid’, IEEE Trans. Smart Grid, 2015, 6, (3), pp. 1219–1226.
    26. 26)
      • 21. Wang, H., Shi, P., Zhang, J.: ‘Event-triggered fuzzy filtering for a class of nonlinear networked control systems’, Signal Process., 2017, 113, pp. 159–168.
    27. 27)
      • 27. Zha, L., Tian, E., Xie, X., et al: ‘Decentralized event-triggered H-infinity control for neural networks subject to cyber-attacks’, Inf. Sci., 2018, 457–458, pp. 141–155.
    28. 28)
      • 43. Liu, J., Xia, J., Tian, E., et al: ‘Hybrid-driven-based H-infinity filter design for neural networks subject to deception attacks’, Appl. Math. Comput., 2018, 320, pp. 158–174.
    29. 29)
      • 37. Ding, D., Wang, Z., Ho, D.W.C., et al: ‘Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks’, Automatica, 2017, 78, pp. 231–240.
    30. 30)
      • 15. Jan, L., Daniel, L.: ‘A state-feedback approach to event-based control’, Automatica, 2010, 46, (1), pp. 211–215.
    31. 31)
      • 9. Zhang, B., Han, Q.L.: ‘Network-based modeling and active control for offshore steel jacket platform with TMD mechanisms’, J. Sound Vib., 2014, 333, (25), pp. 6796–6814.
    32. 32)
      • 11. Zhang, B., Han, Q.L., Zhang, X.M., et al: ‘Sliding mode control with mixed current and delayed states for offshore steel jacket platforms’, IEEE Trans. Control Syst. Technol., 2014, 22, (5), pp. 1769–1783.
    33. 33)
      • 29. Ding, D., Wang, Z., Han, Q.L., et al: ‘Security control for discrete-time stochastic nonlinear systems subject to deception attacks’, IEEE Trans. Syst. Man Cybern., Syst., 2106, 99, pp. 1–11.
    34. 34)
      • 23. Weng, S., Yue, D., Dou, C., et al: ‘Distributed event-triggered cooperative control for frequency and voltage stability and power sharing in isolated inverter-based microgrid’, IEEE Trans. Cybern., 2018, doi: 10.1109/TCYB.2018.2803754.
    35. 35)
      • 17. Zhang, W., Tang, Y., Liu, Y., et al: ‘Event-triggering containment control for a class of multi-agent networks with fixed and switching topologies’, IEEE Trans. Circuits Syst. I, Reg. Papers, 2017, 64, (3), pp. 619–629.
    36. 36)
      • 42. Liu, J., Wei, L., Xie, X., et al: ‘Quantized stabilization for T–S fuzzy systems with hybrid-triggered mechanism and stochastic cyber attacks’, IEEE Trans. Fuzzy Syst., 2018, 26, (6), pp. 3820–3834.
    37. 37)
      • 6. Zhang, Y., Li, F., Chen, Y.: ‘Leader-following-based distributed Kalman filtering in sensor networks with communication delay’, J. Frankl. Inst., 2017, 354, pp. 7504–7520.
    38. 38)
      • 16. Dong, Y., Tian, E., Han, Q.L.: ‘A delay system method for designing event-triggered controllers of networked control systems’, IEEE Trans. Autom. Control, 2013, 58, (2), pp. 475–481.
    39. 39)
      • 44. Yue, D., Tian, E., Zhang, Y., et al: ‘Delay-distribution-dependent stability and stabilization of T–S fuzzy systems with probabilistic interval delay’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2009, 39, (2), pp. 503–516.
    40. 40)
      • 10. Zhang, B., Ma, L., Han, Q.L.: ‘Sliding mode H∞ control for offshore steel jacket platforms subject to nonlinear self-excited wave force and external disturbance’, Nonlinear Dyn., Real World Appl., 2013, 14, (1), pp. 163–178.
    41. 41)
      • 3. Liu, J., Gu, Y., Cao, J., et al: ‘Distributed event-triggered H-infinity filtering over sensor networks with sensor saturations and cyber-attacks’, ISA Trans., 2018, 81, pp. 63–75.
    42. 42)
      • 14. Tabuada, P.: ‘Event-triggered real-time scheduling of stabilizing control tasks’, IEEE Trans. Autom. Control, 2007, 52, (9), pp. 1680–1685.
    43. 43)
      • 18. Zhang, B., Han, Q.L., Zhang, X.M.: ‘Event-triggered reliable control for offshore structures in network environments’, J. Sound Vib., 2016, 368, pp. 1–21.
    44. 44)
      • 46. Liu, J., Yue, D.: ‘Event-triggering in networked systems with probabilistic sensor and actuator faults’, Inf. Sci., 2013, 240, (10), pp. 145–160.
    45. 45)
      • 25. Liu, J., Zha, L., Xie, X., et al: ‘Resilient observer-based control for networked nonlinear T–S fuzzy systems with hybrid-triggered scheme’, Nonlinear Dyn., 2018, 91, (3), pp. 2049–2061.
    46. 46)
      • 32. Chen, B., Ho, D.W.C., Hu, G., et al: ‘Secure fusion estimation for bandwidth constrained cyber-physical systems under replay attacks’, IEEE Trans. Cybern., 2018, 48, (6), pp. 1862–1876.
    47. 47)
      • 26. Liu, J., Wei, L., Cao, J., et al: ‘Hybrid-driven H-infinity filter design for T–S fuzzy systems with quantization’, Nonlinear Anal., Hybrid Syst., 2019, 31, pp. 135–152.
    48. 48)
      • 19. Zha, L., Fang, J., Liu, J., et al: ‘Event-triggered non-fragile state estimation for delayed neural networks with randomly occurring sensor nonlinearity’, Neurocomputing, 2018, 273, pp. 1–8.
    49. 49)
      • 4. Ding, D., Wang, Z., Shen, B., et al: ‘Event-triggered distributed H∞ state estimation with packet dropouts through sensor networks’, IET Control Theory Appl., 2015, 9, (13), pp. 1948–1955.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2018.5868
Loading

Related content

content/journals/10.1049/iet-cta.2018.5868
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address