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Energy-efficient signal acquisition in wireless sensor networks: a compressive sensing framework

Energy-efficient signal acquisition in wireless sensor networks: a compressive sensing framework

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The sampling rate of the sensors in wireless sensor networks (WSNs) determines the rate of its energy consumption, since most of the energy is used in sampling and transmission. To save the energy in WSNs and thus prolong the network lifetime, the authors present a novel approach based on the compressive sensing (CS) framework to monitor 1-D environmental information in WSNs. The proposed technique is based on CS theory to minimise the number of samples taken by sensor nodes. An innovative feature of the proposed approach is a new random sampling scheme that considers the causality of sampling, hardware limitations and the trade-off between the randomisation scheme and computational complexity. In addition, a sampling rate indicator feedback scheme is proposed to enable the sensor to adjust its sampling rate to maintain an acceptable reconstruction performance while minimising the number of samples. A significant reduction in the number of samples required to achieve acceptable reconstruction error is demonstrated using real data gathered by a WSN located in the Hessle Anchorage of the Humber Bridge.

References

    1. 1)
      • Pati, Y., Rezaiifar, R., Krishnaprasad, P.: `Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition', Record of the 27th Asilomar Conf. on Signals, Systems and Computers, November 1993, 1, p. 40–44.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
      • Boyle, F.A., Haupt, J., Fudge, G.L., Yeh, C.-C.A.: `Detecting signal structure from randomly-sampled data', IEEE/SP 14th Workshop on Statistical Signal Processing, SSP ’07, 2007, p. 326–330.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • Charbiwala, Z., Kim, Y., Zahedi, S., Friedman, J., Srivastava, M.B.: `Energy efficient sampling for event detection in wireless sensor networks', Proc. 14th ACM/IEEE Int. Symp. on Low Power Electronics and Design, ISLPED’09, 2009, New York, NY, USA, p. 419–424.
    22. 22)
      • Available at: http://users.ece.gatech.edu/~justin/l1magic/, 2009.
    23. 23)
      • Malioutov, D., Cetin, M., Willsky, A.: `Homotopy continuation for sparse signal representation', Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2005), 2005, 5, p. v/733–v/736.
    24. 24)
    25. 25)
    26. 26)
      • Hamamoto, T., Nagao, S., Aizawa, K.: `Real-time objects tracking by using smart image sensor and FPGA', Proc. 2002 Int. Conf. on Image Processing, 2002, 3, p. III-441–III-444.
    27. 27)
      • Available at: http://www.bridgeforum.com/humber/hessle-anchorage-cal.php, 2009.
    28. 28)
    29. 29)
    30. 30)
    31. 31)
      • Dang, T., Bulusu, N., Hu, W.: `Lightweight acoustic classification for cane-toad monitoring', 2008 42nd Asilomar Conf. on Signals, Systems and Computers, 2008, p. 1601–1605.
    32. 32)
      • Chen, W., Wassell, I.J.: `Energy efficient signal acquisition via compressive sensing in wireless sensor networks', Sixth Int. Symp. on Wireless Pervasive Computing, ISWPC, 2011.
    33. 33)
    34. 34)
    35. 35)
    36. 36)
    37. 37)
    38. 38)
    39. 39)
      • P. Garrigues , L.E. Ghaoui . A homotopy algorithm for the LASSO with online observations. Adv. Neural Inf. Process. Syst. , 489 - 496
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