GammaSense: Infrastructureless Positioning Using Background Radioactivity

  • Doina Bucur
  • Mikkel Baun Kjærgaard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5279)

Abstract

We introduce the harvesting of natural background radioactivity for positioning. Using a standard Geiger-Müller counter as sensor, we fingerprint the natural levels of gamma radiation with the aim of then roughly pinpointing the position of a client in terms of interfloor, intrafloor, and indoor-versus-outdoor locations. We find that the performance of a machine-learning algorithm in detecting position varies with the building, and is highest for interfloor detection in the case of an old domestic house, while it is highest for intrafloor detection if the floor spans building segments made from different construction materials. Altogether, the technique has lower performance than infrastructure-based localization techniques.

Keywords

Permeability Argon Helium Uranium Radar 

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References

  1. 1.
    Kjærgaard, M.B.: A Taxonomy for Radio Location Fingerprinting. In: Proceedings of the Third International Symposium on Location- and Context-Awareness (2007)Google Scholar
  2. 2.
    Bahl, P., Padmanabhan, V.N.: RADAR: An In-Building RF-based User Location and Tracking System. In: Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM (2000)Google Scholar
  3. 3.
    Otsason, V., Varshavsky, A., Marca, A.L., de Lara, E.: Accurate GSM Indoor Localization. In: Beigl, M., Intille, S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 141–158. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Youssef, M., Agrawala, A.: The Horus WLAN Location Determination System. In: Proceedings of the Third International Conference on Mobile Systems, Applications, and Services (2005)Google Scholar
  5. 5.
    Haeberlen, A., Flannery, E., Ladd, A.M., Rudys, A., Wallach, D.S., Kavraki, L.E.: Practical Robust Localization over Large-Scale 802.11 Wireless Networks. In: Proceedings of the Tenth ACM International Conference on Mobile Computing and Networking (2004)Google Scholar
  6. 6.
    Varshavsky, A., Lamarca, A., Hightower, J., de Lara, E.: The SkyLoc Floor Localization System. In: Proceedings of the Fifth Annual IEEE International Conference on Pervasive Computing and Communications (2007)Google Scholar
  7. 7.
    Patel, S., Truong, K., Abowd, G.: PowerLine Positioning: A Practical Sub-Room-Level Indoor Location System for Domestic Use. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 441–458. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Ravi, N., Iftode, L.: FiatLux: Fingerprinting Rooms Using Light Intensity. In: Adjunct Proceedings of the Fifth International Conference on Pervasive Computing (2007)Google Scholar
  9. 9.
    Draganić, I.G., Draganić, Z.D., Adloff, J.-P.: Radiation and Radioactivity on Earth and Beyond, 2nd edn. CRC Press, Boca Raton (1993)Google Scholar
  10. 10.
    Royal Society of Chemistry: Essays on Radiochemistry: Alpha, Beta and Gamma Radioactivity (unknown year), http://www.rsc.org/pdf/radioactivity/number3.pdf
  11. 11.
    United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR): ANNEX B: Exposures from natural radiation sources, subsubsection IIC2 (2000), http://www.unscear.org/docs/reports/annexb.pdf
  12. 12.
    European Commission: Commission recommendation of 21 february 1990 on the protection of the public against indoor exposure to radon (1990), http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:31990H0143:EN:NOT
  13. 13.
    Ghany, H.A.A.: Variability of radon levels in different rooms of egyptian dwellings. Indoor and Built Environment 15(2), 193–196 (2006)CrossRefGoogle Scholar
  14. 14.
    Sonkawade, R.G., Ram, R., Kanjilal, D.K., Ramola, R.C.: Radon in tube-well drinking water and indoor air. Indoor and Built Environment 13(5), 383–385 (2004)CrossRefGoogle Scholar
  15. 15.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar
  16. 16.
    Venere, E., Gardner, E.K.: Cell phone sensors detect radiation to thwart nuclear terrorism (2008), http://www.purdue.edu/UNS/x/2008a/080122FischbachNuclear.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Doina Bucur
    • 1
  • Mikkel Baun Kjærgaard
    • 1
  1. 1.Department of Computer ScienceUniversity of AarhusDenmark

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