Smart Structures and Systems

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A simple and efficient data loss recovery technique for SHM applications

  • Thadikemalla, Venkata Sainath Gupta (Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology) ;
  • Gandhi, Abhay S. (Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology)
  • Received : 2017.02.13
  • Accepted : 2017.05.27
  • Published : 2017.07.25
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Abstract

Recently, compressive sensing based data loss recovery techniques have become popular for Structural Health Monitoring (SHM) applications. These techniques involve an encoding process which is onerous to sensor node because of random sensing matrices used in compressive sensing. In this paper, we are presenting a model where the sampled raw acceleration data is directly transmitted to base station/receiver without performing any type of encoding at transmitter. The received incomplete acceleration data after data losses can be reconstructed faithfully using compressive sensing based reconstruction techniques. An in-depth simulated analysis is presented on how random losses and continuous losses affects the reconstruction of acceleration signals (obtained from a real bridge). Along with performance analysis for different simulated data losses (from 10 to 50%), advantages of performing interleaving before transmission are also presented.

Keywords

References

  1. Bao, Y., Li, H., Sun, X., Yu, Y. and Ou, J. (2013), "Compressive sampling-based data loss recovery for wireless sensor networks used in civil structural health monitoring", Struct. Health Monit., 12(1), 78-95. https://doi.org/10.1177/1475921712462936
  2. Bao, Y., Yu, Y., Li, H., Mao, X., Jiao, W., Zou, Z. and Ou, J. (2015), "Compressive sensing-based lost data recovery of fastmoving wireless sensing for structural health monitoring", Struct. Control Health Monit., 22(3), 433-448. https://doi.org/10.1002/stc.1681
  3. Baraniuk, R., Davenport, M.A., Duarte, M.F. and Hegde, C. (2011), An Introduction to Compressive Sensing, Connexions etextbook.
  4. Berg, E.V. and Friedlander, M.P. (2007), "SPGL1: A solver for large-scale sparse reconstruction", see http://www.cs.ubc.ca/labs/scl/spgl1
  5. Candes, E.J. (2006), "Compressive sampling", Proceedings of the International Congress of Mathematicians, Madrid, Spain.
  6. Candes, E.J. and Wakin, M.B. (2008), An Introduction to Compressive Sampling, IEEE signal processing magazine, March.
  7. Charbiwala, Z., Chakraborty, S., Zahedi, S., He, T., Bisdikian, C., Kim, Y. and Srivastava, M.B. (2010), "Compressive oversampling for robust data transmission in sensor networks", Proceedings of the 2010 IEEE INFOCOM, San Diego, CA.
  8. Garudadri, H., Chi, Y., Baker, S., Majumdar, S., Baheti, P.K. and Ballard, D. (2011), "Diagnostic grade wireless ECG monitoring", Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA.
  9. Hayinga, P.J.M. (1999), "Energy efficiency of error correction on wireless systems", Proceedings of the WCNC. 1999 IEEE Wireless Communications and Networking Conference, New Orleans, LA.
  10. Li, S., Li, H., Liu, Y., Lan, C., Zhou, W. and Ou, J. (2014), "SMC structural health monitoring benchmark problem using monitored data from an actual cable-stayed bridge", Struct. Control Health Monit., 21(2), 156-172. https://doi.org/10.1002/stc.1559
  11. Ma, H., Xiong, J., Xu, Y. and Liang, D. (2009), "Packet loss concealment for speech transmission based on compressed sensing", Proceedings of the IET International Communication Conference on Wireless Mobile and Computing (CCWMC 2009), Shanghai, China.
  12. Nagayama, T., Sim, S.H., Miyamori, Y. and Spencer, B.F. Jr., (2007), "Issues in structural health monitoring employing smart sensors", Smart Struct. Syst., 3(3), 299-320. https://doi.org/10.12989/sss.2007.3.3.299
  13. Selesnick, I. (2012), Introduction to Sparsity in Signal Processing. Connexions (online).
  14. Srinivasan, K., Dutta, P., Tavakoli, A. and Levis, P. (2006), "Understanding the causes of packet delivery success and failure in dense wireless sensor networks", Proceedings of the 4th International Conference on Embedded Networked Sensor Systems, Boulder, Colorado, USA.
  15. Wu, L., Yu, K., Cao, D., Hu, Y. and Wang, Z. (2015), "Efficient sparse signal transmission over a lossy link using compressive sensing", Sensors, 15(8), 19880-19911. https://doi.org/10.3390/s150819880
  16. Yu, W., Chen, C., He, T., Yang, B. and Guan, X. (2016), "Adaptive compressive engine for real-time electrocardiogram monitoring under unreliable wireless channels", IET Communications, 10(6), 607-615. https://doi.org/10.1049/iet-com.2015.0882
  17. Yu, Y., Han, F., Bao, Y. and Ou, J. (2015), "Packet loss and compensation of Wi-Fi based wireless sensor networks", Proceedings of the SPIE 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015, San Diego, California, United States, March.
  18. Zhang, Y. (2006), "When is missing data recoverable?", CAAM Technical Report TR06-15; Department of Computational and Applied Mathematics, Rice University, Houston, Texas.
  19. Zou, Z., Bao, Y., Li, H., Spencer, B.F. Jr. and Ou, J. (2015), "Embedding compressive sensing-based data loss recovery algorithm into wireless smart sensors for structural health monitoring", IEEE Sens. J., 15(2), 797-808. https://doi.org/10.1109/JSEN.2014.2353032

Cited by

  1. A Data Loss Recovery Technique using Compressive Sensing for Structural Health Monitoring Applications vol.22, pp.12, 2018, https://doi.org/10.1007/s12205-017-2070-z