Blind Calibration of Mobile Sensors Using Informed Nonnegative Matrix Factorization

  • Clément Dorffer
  • Matthieu Puigt
  • Gilles Delmaire
  • Gilles Roussel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9237)

Abstract

In this paper, we assume several heterogeneous, geolocalized, and time-stamped sensors to observe an area over time. We also assume that most of them are uncalibrated and we propose a novel formulation of the blind calibration problem as a Nonnegative Matrix Factorization (NMF) with missing entries. Our proposed approach is generalizing our previous informed and weighted NMF method, which is shown to be accurate for the considered application and to outperform blind calibration based on matrix completion and nonnegative least squares.

Keywords

Blind calibration Mobile sensor network Informed nonnegative matrix factorization Missing values 

Notes

Acknowledgments

This work was funded by the “OSCAR” project within the Région Nord – Pas de Calais “Chercheurs Citoyens” Program.

References

  1. 1.
    Balzano, L., Nowak, R.: Blind calibration of sensor networks. In: Proceedings of IPSN, pp. 79–88 (2007)Google Scholar
  2. 2.
    Becker, S., Candès, E., Grant, M.: Templates for convex cone problems with applications to sparse signal recovery. Math. Program. Comput. 3(3), 165–218 (2011)MATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    Benachir, D., Deville, Y., Hosseini, S., Karoui, M.S., Hameurlain, A.: Hyperspectral image unmixing by non-negative matrix factorization initialized with modified independent component analysis. In: Proceedings of WHISPERS (2013)Google Scholar
  4. 4.
    Bilen, C., Puy, G., Gribonval, R., Daudet, L.: Convex optimization approaches for blind sensor calibration using sparsity. IEEE Trans. Signal Process. 62(18), 4847–4856 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Choo, J., Lee, C., Reddy, C., Park, H.: Weakly supervised nonnegative matrix factorization for user-driven clustering. Data Min. Knowl. Disc. 1–24 (2014)Google Scholar
  6. 6.
    Cochran, E., Lawrence, J., Kaiser, A., Fry, B., Chung, A., Christensen, C.: Comparison between low-cost and traditional MEMS accelerometers: a case study from the M7.1 Darfield, New Zealand, aftershock deployment. Ann. Geophys. 54(6), 728–737 (2012)Google Scholar
  7. 7.
    D’Hondt, E., Stevens, M., Jacobs, A.: Participatory noise mapping works! An evaluation of participatory sensing as an alternative to standard techniques for environmental monitoring. Pervasive Mobile Comput. 9(5), 681–694 (2013)CrossRefGoogle Scholar
  8. 8.
    Ganti, R., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag. 49(11), 32–39 (2011)CrossRefGoogle Scholar
  9. 9.
    Lee, B.T., Son, S.C., Kang, K.: A blind calibration scheme exploiting mutual calibration relationships for a dense mobile sensor network. IEEE Sens. J. 14(5), 1518–1526 (2014)CrossRefGoogle Scholar
  10. 10.
    Limem, A., Delmaire, G., Puigt, M., Roussel, G., Courcot, D.: Non-negative matrix factorization under equality constraints–a study of industrial source identification. Appl. Numer. Math. 85, 1–15 (2014)MATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    Lipor, J., Balzano, L.: Robust blind calibration via total least squares. In: Proceedings of ICASSP, pp. 4244–4248, May 2014Google Scholar
  12. 12.
    Miluzzo, E., Lane, N.D., Campbell, A.T., Olfati-Saber, R.: CaliBree: a self-calibration system for mobile sensor networks. In: Nikoletseas, S.E., Chlebus, B.S., Johnson, D.B., Krishnamachari, B. (eds.) DCOSS 2008. LNCS, vol. 5067, pp. 314–331. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  13. 13.
    Plouvin, M., Limem, A., Puigt, M., Delmaire, G., Roussel, G., Courcot, D.: Enhanced NMF initialization using a physical model for pollution source apportionment. In: Proceedings of ESANN, pp. 261–266 (2014)Google Scholar
  14. 14.
    Saukh, O., Hasenfratz, D., Thiele, L.: Reducing multi-hop calibration errors in large-scale mobile sensor networks. In: Proceedings of IPSN (2015)Google Scholar
  15. 15.
    Saukh, O., Hasenfratz, D., Walser, C., Thiele, L.: On rendezvous in mobile sensing networks. In: Langendoen, K., Hu, W., Ferrari, F., Zimmerling, M., Mottola, L. (eds.) Real-World Wireless Sensor Networks, Part I. LNEE, vol. 281, pp. 29–42. Springer, Switzerland (2014) CrossRefGoogle Scholar
  16. 16.
    Schulke, C., Caltagirone, F., Krzakala, F., Zdeborova, L.: Blind calibration in compressed sensing using message passing algorithms. In: Proceedings of NIPS, vol. 26, pp. 566–574 (2013)Google Scholar
  17. 17.
    Sharp Corp.: GP2Y1010AU0F compact optical dust sensor (2006), datasheetGoogle Scholar
  18. 18.
    Wang, C., Ramanathan, P., Saluja, K.: Moments based blind calibration in mobile sensor networks. In: Proceedings of ICC 2008, pp. 896–900, May 2008Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Clément Dorffer
    • 1
  • Matthieu Puigt
    • 1
  • Gilles Delmaire
    • 1
  • Gilles Roussel
    • 1
  1. 1.LISICULCO, Université Lille Nord de FranceCalaisFrance

Personalised recommendations