access icon free Power allocation scheme for distributed filtering over wireless sensor networks

In this work, distributed filtering over wireless sensor networks with limited energy is considered, where each sensor sends its local state estimate to its adjacent sensors through an unreliable wireless channel, which introduces random data packet drops. The packet drop rate depends on the power allocated by the sensor under an energy constraint. Several offline power scheduling strategies are introduced to distribute the power of sensors. A sufficient condition is provided to guarantee the convergence of network estimation error covariance. Further, an online power scheduling strategy is proposed, where each sensor utilises its real-time information to distribute energy for communications. The filtering performance of different power scheduling strategies are compared to show the influence of power distributed scheme on the expected state estimation error covariance.

Inspec keywords: resource allocation; scheduling; filtering theory; wireless sensor networks; wireless channels; covariance analysis

Other keywords: online power scheduling strategy; power distributed scheme; distributed filtering; packet drop rate; offline power scheduling strategies; network estimation error covariance; wireless sensor networks; state estimation error covariance; wireless channel

Subjects: Wireless sensor networks; Filtering methods in signal processing; Other topics in statistics

References

    1. 1)
      • 26. Brixius, N.W., Anstreicher, K.M.: ‘Solving quadratic assignment problems using convex quadratic programming relaxations’. Technical Report, Dept. of Management Sciences, Dept. of Management Sciences, University of Iowa, 2000.
    2. 2)
    3. 3)
    4. 4)
      • 7. Olfati-Saber, R.: ‘Distributed Kalman filtering for sensor networks’. Proc. IEEE Conf. Decision and Control, 2007, pp. 54925498.
    5. 5)
    6. 6)
      • 8. Olfati-Saber, R.: ‘Kalman-consensus filter: optimality, stability, and performance’. Proc. IEEE Conf. Decision and Control, 2009, pp. 70367042.
    7. 7)
      • 5. Spanos, D.P., Saber, R.O., Murray, R.M.: ‘Approximate distributed Kalman filtering in sensor networks with quantifiable performance’. Fourth Int. Symp. on Information Processing in Sensor Networks, 2005, pp. 133139.
    8. 8)
      • 24. Anderson, B.D.O., Moore, J.B.: ‘Optimal filtering’ (Prentice-Hall, New York, 1979).
    9. 9)
    10. 10)
    11. 11)
      • 4. Spanos, D.P., Saber, R.O., Murray, R.M.: ‘Dynamic consensus on mobile networks. The 16th IFAC World Congress, Prague, Czech, 2005.
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
      • 27. Newman, M.E.J.: ‘Networks: an introduction’ (Oxford University Press, UK, 2010).
    19. 19)
    20. 20)
      • 23. Li, Y., Quevedo, D.E., Lau, V.K.N., Shi, L.: ‘Online sensor transmission power schedule for remote state estimation’. Proc. IEEE Conf. Decision and Control, Firenze, Italy, December 2013.
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
      • 6. Olfati-Saber, R., Shamma, J.S.: ‘Consensus filters for sensor networks and distributed sensor fusion’. Proc. IEEE Conf. Decision and Control, 2005, pp. 66986703.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2014.0494
Loading

Related content

content/journals/10.1049/iet-cta.2014.0494
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading