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Volume 33 Issue 5
September  2018
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Shou-Wan Gao, Peng-Peng Chen, Xu Yang, Qiang Niu. Multi-Sensor Estimation for Unreliable Wireless Networks with Contention-Based Protocols[J]. Journal of Computer Science and Technology, 2018, 33(5): 1072-1085. DOI: 10.1007/s11390-018-1862-z
Citation: Shou-Wan Gao, Peng-Peng Chen, Xu Yang, Qiang Niu. Multi-Sensor Estimation for Unreliable Wireless Networks with Contention-Based Protocols[J]. Journal of Computer Science and Technology, 2018, 33(5): 1072-1085. DOI: 10.1007/s11390-018-1862-z
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Multi-Sensor Estimation for Unreliable Wireless Networks with Contention-Based Protocols

  • 1 Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology Xuzhou 221116, China;
    2 School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China

Funds: This work was supported by the National Natural Science Foundation of China under Grant No. 51774282, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20160274, and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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  • Corresponding author:

    Peng-Peng Chen,E-mail:chenp@cumt.edu.cn

  • Received Date: November 18, 2017
  • Revised Date: May 23, 2018
  • Published Date: September 16, 2018
    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 (31)
  • [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.
    [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.
    [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.
    [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.
    [7]
    Sui T, You K, Fu M. Stability conditions for multi-sensor state estimation over a lossy network. Automatica, 2015, 53:1-9.
    [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.
    [9]
    Yang Y, Hao J, Luo J. CeilingTalk:Lightweight indoor broadcast through LED-camera communication. IEEE Trans. Mobile Computing, 2017, 16(12):3308-3319.
    [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.
    [12]
    Plarre K, Bullo F. On Kalman filtering for detectable systems with intermittent observations. IEEE Trans. Automatic Control, 2009, 54(2):386-390.
    [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.
    [15]
    You K, Fu M, Xie L. Mean square stability for Kalman filtering with Markovian packet losses. Automatica, 2011, 47(12):2647-2657.
    [16]
    Xie L, Xie L. Stability of a random Riccati equation with Markovian binary switching. IEEE Trans. Automatic Control, 2008, 53(7):1759-1764.
    [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.
    [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.
    [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.
    [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.
    [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.
    [24]
    Shi L, Cheng P, Chen J. Optimal periodic sensor scheduling with limited resources. IEEE Trans. Automatic Control, 2011, 56(9):2190-2195.
    [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.
    [26]
    Lin Z, Wang C. Scheduling parallel Kalman filters for multiple processes. Automatica, 2013, 49(1):9-16.
    [27]
    Li C, Elia N. Stochastic sensor scheduling via distributed convex optimization. Automatica, 2015, 58:173-182.
    [28]
    Han D, Wu J, Zhang H, Shi L. Optimal sensor scheduling for multiple linear dynamical systems. Automatica, 2017, 75:260-270.
    [29]
    You K, Sui T, Fu M. Kalman filtering over lossy networks under switching sensors. Asian Journal of Control, 2015, 17(1):45-54.
    [30]
    Shi L, Cheng P, Chen J. Sensor data scheduling for optimal state estimation with communication energy constraint. Automatica, 2011, 47(8):1693-1698.
    [31]
    Gajic Z, Qureshi M T J. Lyapunov Matrix Equation in System Stability and Control (1st edition). Dover Publications, 1995.
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