Clearing a Crowd: Context-Supported Neighbor Positioning for People-Centric Navigation

  • Takamasa Higuchi
  • Hirozumi Yamaguchi
  • Teruo Higashino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7319)


This paper presents a positioning system for “people-centric” navigation, which estimates relative positions of surrounding people to help users to find a target person in a crowd of neighbors. Our system, called PCN, employs pedestrian dead reckoning (PDR) and proximity sensing with Bluetooth only using off-the-shelf mobile phones. Utilizing the feature of “group activity” where people naturally form groups moving similarly and together in exhibitions, parties and so on, PCN corrects deviation of distance and direction in PDR. The group information is also helpful to identify the surrounding people in the navigation. A field experiment in a real exhibition with 20 examinees carrying Google Android phones was conducted to show its effectiveness.


Mobile Phone Mobile Node Position Error Trade Fair Movement Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Banerjee, N., Agarwal, S., Bahl, P., Chandra, R., Wolman, A., Corner, M.: Virtual Compass: Relative Positioning to Sense Mobile Social Interactions. In: Floréen, P., Krüger, A., Spasojevic, M. (eds.) Pervasive 2010. LNCS, vol. 6030, pp. 1–21. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: Proc. of SIGMOD 2005, pp. 491–502 (2005)Google Scholar
  3. 3.
    Chintalapudi, K., Iyer, A.P., Padmanabhan, V.N.: Indoor localization without the pain. In: Proc. of MobiCom 2010, pp. 173–184 (2010)Google Scholar
  4. 4.
    Chitte, S., Dasgupta, S., Ding, Z.: Distance estimation from received signal strength under log-normal shadowing: Bias and variance. IEEE Signal Processing Letters 16(3), 216–218 (2009)CrossRefGoogle Scholar
  5. 5.
    Constandache, I., Bao, X., Azizyan, M., Choudhury, R.R.: Did you see bob?: human localization using mobile phones. In: Proc. of MobiCom 2010, pp. 149–160 (2010)Google Scholar
  6. 6.
    Constandache, I., Choudhury, R.R., Rhee, I.: Towards mobile phone localization without war-driving. In: Proc. of INFOCOM 2010, pp. 1–9 (2010)Google Scholar
  7. 7.
    Fujii, S., Nomura, T., Umedu, T., Yamaguchi, H., Higashino, T.: Real-time trajectory estimation in mobile ad hoc networks. In: Proc. of MSWiM 2009, pp. 163–172 (2009)Google Scholar
  8. 8.
    Harter, A., Hopper, A., Steggles, P., Ward, A., Webster, P.: The anatomy of a context-aware application. In: Proc. of MobiCom 1999, pp. 59–68 (1999)Google Scholar
  9. 9.
    Higuchi, T., Fujii, S., Yamaguchi, H., Higashino, T.: An efficient localization algorithm focusing on stop-and-go behavior of mobile nodes. In: Proc. of PerCom 2011, pp. 205–212 (2011)Google Scholar
  10. 10.
    Kloch, K., Lukowicz, P., Fischer, C.: Collaborative PDR localisation with mobile phones. In: Proc. of ISWC 2011, pp. 37–40 (2011)Google Scholar
  11. 11.
    Krumm, J., Hinckley, K.: The NearMe Wireless Proximity Server. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 283–300. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Peng, C., Shen, G., Zhang, Y., Li, Y., Tan, K.: BeepBeep: A high accuracy acoustic ranging system using COTS mobile devices. In: Proc. of SenSys 2007, pp. 1–14 (2007)Google Scholar
  13. 13.
    Steinhoff, U., Schiele, B.: Dead reckoning from the pocket — an experimental study. In: Proc. of PerCom 2010, pp. 162–170 (2010)Google Scholar
  14. 14.
    Wertheimer, M.: Laws of organization in perceptual forms (1938)Google Scholar
  15. 15.
    Woodman, O., Harle, R.: Pedestrian localisation for indoor environments. In: Proc. of UbiComp 2008, pp. 114–123 (2008)Google Scholar
  16. 16.
    Yin, J., Yang, Q., Ni, L.M.: Learning adaptive temporal radio maps for signal-strength-based location estimation. IEEE Transactions on Mobile Computing 7(7), 869–883 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Takamasa Higuchi
    • 1
  • Hirozumi Yamaguchi
    • 1
    • 2
  • Teruo Higashino
    • 1
    • 2
  1. 1.Graduate School of Information Science and TechnologyOsaka UniversitySuitaJapan
  2. 2.Japan Science and Technology Agency, CRESTJapan

Personalised recommendations