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Multi-Sensor Estimation for Unreliable Wireless Networks with Contention-Based Protocols

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Abstract

The state estimation plays an irreplaceable role in many real applications since it lays the foundation for decision-making and control. This paper studies the multi-sensor estimation problem for a contention-based unreliable wireless network. At each time step, no more than one sensor can communicate with the base station due to the potential contention and collision. In addition, data packets may be lost during transmission since wireless channels are unreliable. A novel packet arrival model is proposed which simultaneously takes into account the above two issues. Two scenarios of wireless sensor networks (WSNs) are considered: the sensors transmit the raw measurements directly and the sensors send the local estimation instead. Based on the obtained packet arrival model, necessary and sufficient stability conditions of the estimation at the base station side are provided for both network scenarios. In particular, all offered stability conditions are expressed by simple inequalities in terms of the packet arrival rates and the spectral radius of the system matrix. Their relationships with existing related results are also discussed. Finally, the proposed results are demonstrated by simulation examples and an environment monitoring prototype system.

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References

  1. Long Z, Zheng Y, Li C, He Y. Wire loss monitoring in ultrasonic wedge bonding using the Kalman filter algorithm. IEEE Trans. Components, Packaging and Manufacturing Technology, 2016, 6(1): 153-160.

    Article  Google Scholar 

  2. Bagheri A, Mardaneh M, Rajaei A, Rahideh A. Detection of grid voltage fundamental and harmonic components using Kalman filter and generalized averaging method. IEEE Trans. Power Electronics, 2016, 31(2): 1064-1073.

    Article  Google Scholar 

  3. Yang G, Yin J, Huang D, Jin L, Zhou H. A Kalman filterbased blind adaptive multi-user detection algorithm for underwater acoustic networks. IEEE Sensors Journal, 2016, 16(11): 4023-4033.

    Article  Google Scholar 

  4. Chen P, Ma H, Gao S, Huang Y. Modified extended Kalman filtering for tracking with insufficient and intermittent observations. Mathematical Problems in Engineering, 2015, Article ID. 981727.

  5. Liu X, Goldsmith A. Kalman filtering with partial observation losses. In Proc. the 43rd IEEE Conference on Decision and Control, December 2004, pp.4180-4186.

  6. Wang B F, Guo G. Kalman filtering with partial Markovian packet losses. International Journal of Automation and Computing, 2009, 6(4): 395-400.

    Article  Google Scholar 

  7. Sui T, You K, Fu M. Stability conditions for multi-sensor state estimation over a lossy network. Automatica, 2015, 53: 1-9.

    Article  MathSciNet  Google Scholar 

  8. Gao S, Chen P, Huang D, Niu Q. Stability analysis of multisensor Kalman filtering over lossy networks. Sensors, 2016, 16(4): Article No. 566.

    Article  Google Scholar 

  9. Yang Y, Hao J, Luo J. CeilingTalk: Lightweight indoor broadcast through LED-camera communication. IEEE Trans. Mobile Computing, 2017, 16(12): 3308-3319.

    Article  Google Scholar 

  10. Hao J, Yang Y, Luo J. CeilingCast: Energy efficient and location-bound broadcast through LED-camera communication. In Proc. the 35th Annual IEEE International Conference on Computer Communications, April 2016.

  11. Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan M I, Sastry S S. Kalman filtering with intermittent observations. IEEE Trans. Automatic Control, 2004, 49(9): 1453-1464.

    Article  MathSciNet  Google Scholar 

  12. Plarre K, Bullo F. On Kalman filtering for detectable systems with intermittent observations. IEEE Trans. Automatic Control, 2009, 54(2): 386-390.

    Article  MathSciNet  Google Scholar 

  13. Mo Y, Sinopoli B. Towards finding the critical value for Kalman filtering with intermittent observations. arXiv: 1005.2442, 2010. http://arxiv.org/abs/1005.2442, Nov. 2017.

  14. Huang M, Dey S. Stability of Kalman filtering with Markovian packet losses. Automatica, 2007, 43(4): 598-607.

    Article  MathSciNet  Google Scholar 

  15. You K, Fu M, Xie L. Mean square stability for Kalman filtering with Markovian packet losses. Automatica, 2011, 47(12): 2647-2657.

    Article  MathSciNet  Google Scholar 

  16. Xie L, Xie L. Stability of a random Riccati equation with Markovian binary switching. IEEE Trans. Automatic Control, 2008, 53(7): 1759-1764.

    Article  MathSciNet  Google Scholar 

  17. Rohr E R, Marelli D, Fu M. Kalman filtering with intermittent observations: On the boundedness of the expected error covariance. IEEE Trans. Automatic Control, 2014, 59(10): 2724-2738.

    Article  MathSciNet  Google Scholar 

  18. Schenato L. Optimal estimation in networked control systems subject to random delay and packet drop. IEEE Trans. Automatic Control, 2008, 53(5): 1311-1317.

    Article  MathSciNet  Google Scholar 

  19. Shi L, Epstein M, Murray R M. Kalman filtering over a packet-dropping network: A probabilistic perspective. IEEE Trans. Automatic Control, 2010, 55(3): 594-604.

    Article  MathSciNet  Google Scholar 

  20. Cheng P, Qi Y, Xin K, Chen J, Xie L. Energy-efficient data forwarding for state estimation in multi-hop wireless sensor networks. IEEE Trans. Automatic Control, 2016, 61(5): 1322-1327.

    Article  MathSciNet  Google Scholar 

  21. Shi L, Johansson K H, Murray R M. Estimation over wireless sensor networks: Tradeoff between communication, computation and estimation. In Proc. the 17th International Federation of Automatic Control World Congress, July 2008, pp. 605-611.

  22. Yang M, Chen L, Xiong W. Compression/transmission power allocation in multimedia wireless sensor networks. In Proc. International Conference on Computing, Networking and Communications, February 2014, pp.1103-1107.

  23. Rajagopalan R, Varshney P K. Data-aggregation techniques in sensor networks: A survey. IEEE Communications Surveys & Tutorials, 2006, 8(4): 48-63.

    Article  Google Scholar 

  24. Shi L, Cheng P, Chen J. Optimal periodic sensor scheduling with limited resources. IEEE Trans. Automatic Control, 2011, 56(9): 2190-2195.

    Article  MathSciNet  Google Scholar 

  25. Shi L, Zhang H. Scheduling two Gauss-Markov systems: An optimal solution for remote state estimation under bandwidth constraint. IEEE Trans. Signal Process, 2012, 60(4): 2038-2042.

    Article  MathSciNet  Google Scholar 

  26. Lin Z, Wang C. Scheduling parallel Kalman filters for multiple processes. Automatica, 2013, 49(1): 9-16.

    Article  MathSciNet  Google Scholar 

  27. Li C, Elia N. Stochastic sensor scheduling via distributed convex optimization. Automatica, 2015, 58: 173-182.

    Article  MathSciNet  Google Scholar 

  28. Han D, Wu J, Zhang H, Shi L. Optimal sensor scheduling for multiple linear dynamical systems. Automatica, 2017, 75: 260-270.

    Article  MathSciNet  Google Scholar 

  29. You K, Sui T, Fu M. Kalman filtering over lossy networks under switching sensors. Asian Journal of Control, 2015, 17(1): 45-54.

    Article  MathSciNet  Google Scholar 

  30. Shi L, Cheng P, Chen J. Sensor data scheduling for optimal state estimation with communication energy constraint. Automatica, 2011, 47(8): 1693-1698.

    Article  MathSciNet  Google Scholar 

  31. Gajic Z, Qureshi M T J. Lyapunov Matrix Equation in System Stability and Control (1st edition). Dover Publications, 1995.

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Authors and Affiliations

  1. Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology, Xuzhou, 221116, China

    Shou-Wan Gao

  2. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Peng-Peng Chen, Xu Yang & Qiang Niu

Authors
  1. Shou-Wan Gao
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  2. Peng-Peng Chen
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  3. Xu Yang
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  4. Qiang Niu
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Corresponding author

Correspondence to Peng-Peng Chen.

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Gao, SW., Chen, PP., Yang, X. et al. Multi-Sensor Estimation for Unreliable Wireless Networks with Contention-Based Protocols. J. Comput. Sci. Technol. 33, 1072–1085 (2018). https://doi.org/10.1007/s11390-018-1862-z

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  • DOI: https://doi.org/10.1007/s11390-018-1862-z

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