Skip to main content
SpringerLink
Log in

Indoor localization with a crowdsourcing based fingerprints collecting

  • Published:
Journal of Shanghai Jiaotong University (Science) Aims and scope Submit manuscript

Abstract

Fingerprint matching is adopted by a large family of indoor localization schemes, where collecting fingerprints is inevitable but all consuming. While the increasingly popular crowdsourcing based approach provides an opportunity to relieve the burden of fingerprints collecting, a number of formidable challenges for such an approach have yet been studied. For instance, querying in a large fingerprints database for matching process takes a lot of time and calculation; fingerprints collected by crowdsourcing lacks of robustness because of heterogeneous devices problem. Those are important challenges which impede practical deployment of the fingerprint matching indoor localization system. In this study, targeting on effectively utilizing and mining large amount fingerprint data, enhancing the robustness of fingerprints under heterogeneous devices’ collection and realizing the real time localization response, we propose a crowdsourcing based fingerprints collecting mechanism for indoor localization systems. With the proposed approach, massive raw fingerprints will be divided into small clusters while diverse devices’ uploaded fingerprints will be merged for overcoming device heterogeneity, both of which will contribute to reduce response time. We also build a mobile cloud testbed to verify the proposed scheme. Comprehensive real world experiment results indicate that the scheme can provide comparable localization accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kuo S P, Tseng Y C. A scrambling method for fingerprint positioning based on temporal diversity and spatial de endency [J]. Knowledgeand Data Engineering, 2008, 20(5): 678–684.

    Article  Google Scholar 

  2. Jin Y, Soh W S, Wong W C. Indoor localization with channel impulse response based fingerprint and nonparametric regression [J]. Wireless Communications, 2010, 9(3): 1120–1127.

    Article  Google Scholar 

  3. Xiang Z, Song S, Chen J, et al. A wireless lan-based indoor positioning technology [J]. IBM Journal of Research and Development, 2004, 48(5/6): 617–626.

    Article  Google Scholar 

  4. Kao K F, Liao I E, Lyu J S. An indoor locationbased service using access points as signal strength data collectors [C]//2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Banff, Canada: IEEE, 2010: 1–6.

    Chapter  Google Scholar 

  5. Haeberlen A, Flannery E, Ladd A M, et al. Practical robust localization over large-scale 802. 11 wireless networks [C]//Proceedings of the 10th Annual International Conference on Mobile Computing and Networking. PA, USA: ACM, 2004: 70–84.

    Google Scholar 

  6. Krumm J, Horvitz E. Locadio: Inferring motion and location from wifi signal strengths [C]//2th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. Boston, USA: IEEE, 2004: 4–13.

    Google Scholar 

  7. Bhasker E S, Brown S W, Griswold W G. Employing user feedback for fast, accurate, lowmaintenance geolocationing [C]//Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications. Orlando, USA: IEEE, 2004: 111–120.

    Google Scholar 

  8. Bolliger P. Redpin-adaptive, zero-configuration indoor localization through user collaboration [C]//Proceedings of the First ACM International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments. San Francisco, USA: ACM, 2008: 55–60.

    Chapter  Google Scholar 

  9. Park J G, Charrow B, Curtis D, et al. Growing an organic indoor location system [C]//Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services. San Francisco, USA: ACM, 2010: 271–284.

    Google Scholar 

  10. Dudin E, Smetanin Y G. A review of cloud computing [J]. Scientific and Technical Information Processing, 2011, 38(4): 280–284.

    Article  Google Scholar 

  11. Zaruba G V, Huber M, Kamangar F, et al. Indoor location tracking using RSSI readings from a single wifi access point [J]. Wireless Networks, 2007, 13(2): 221–235.

    Article  Google Scholar 

  12. Seco F, Plagemann C, Jimenez A R, et al. Improving RFID-based indoor positioning accuracy using Gaussian processes [C]//2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Banff, Canada: IEEE, 2010: 7–15.

    Google Scholar 

  13. Park J G, CurtisD, Teller S, et al. Implications of device diversity for organic localization [C]//The 29th Conference on Computer Communications. Shanghai, China: IEEE, 2011: 3182–3190.

    Google Scholar 

  14. Botev Z, Grotowski J, Kroese D. Kernel density estimation via diffusion [J]. The Annals of Statistics, 2010, 38(5): 2916–2957.

    Article  MathSciNet  MATH  Google Scholar 

  15. Feng C, Au W S A, Valaee S, et al. Compressive sensing based positioning using RSS of WLAN access points [C]//The 29th Conference on Computer Communications. San Diego, USA: IEEE, 2010: 1–9.

    Google Scholar 

  16. Frey B J, Dueck D. Clustering by passing messages between data points [J]. Science, 2007, 315(5814): 972–976.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Wireless Communication Technology, Shanghai Jiaotong University, Shanghai, 200240, China

    Zheng-yong Huang  (黄正勇), Hui Yu  (俞晖), Yun-feng Guan  (管云峰) & Kun Chen  (陈坤)

Authors
  1. Zheng-yong Huang  (黄正勇)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Yu  (俞晖)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun-feng Guan  (管云峰)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kun Chen  (陈坤)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zheng-yong Huang  (黄正勇).

Additional information

Foundation item: the National Science and Technology Major Project of China (No. 2013ZX03001007-004) and the Shanghai Basic Research Key Project (No. 11DZ1500206)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Zy., Yu, H., Guan, Yf. et al. Indoor localization with a crowdsourcing based fingerprints collecting. J. Shanghai Jiaotong Univ. (Sci.) 20, 548–557 (2015). https://doi.org/10.1007/s12204-015-1662-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12204-015-1662-3

Keywords

CLC number

Document code

Search

Navigation