Skip to main content
SpringerLink
Log in

Reducing the site survey using fingerprint refinement for cost-efficient indoor location

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Recently, RSS fingerprint-based location has been considered as a low-complexity solution for indoor localization. However, constructing a fingerprint map requires a great amount of manual effort to achieve a high location accuracy. In this paper, we present a refinement method to reduce the necessary manual effort without degrading the location accuracy. This method transforms a coarse-gained fingerprint map containing only a small number of offline samples into a high-density fingerprint map by augmenting the map with artificial samples. In particular, a local-to-local strategy is proposed to improve the accuracy of artificial samples. Furthermore, we propose a judgment criterion to determine whether a fingerprint map should continue to be refined when it has reached a certain density and which refined fingerprint map can achieve the best location accuracy. Extensive experimental results show that our proposed method can significantly improve the location accuracy without additional manual effort compared with the original fingerprint map.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Alarifi, A., & Al-Salman, A. (2016). Ultra wideband indoor positioning technologies: Analysis and recent advances. Sensors (Basel), 16(5), 1–36.

    Article  Google Scholar 

  2. Li, N., & Becerik-Gerber, B. (2011). Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment. Advanced Engineering Informatics, 25(3), 535–546.

    Article  Google Scholar 

  3. Yang, S. H., Jung, E. M., & Han, S. K. (2013). Indoor location estimation based on LED visible light communication using multiple optical receivers. IEEE Communications Letters, 17(9), 1834–1837.

    Article  Google Scholar 

  4. Yang, Z., Zhou, Z., & Liu, Y. (2013). From RSSI to CSI: Indoor localization via channel response. Acm Computing Surveys, 46(2), 1–32.

    Article  MATH  Google Scholar 

  5. Kjrgaard, M. B. (2007). A taxonomy for radio location fingerprinting. In Location-and context-awareness. DBLP, 4718, (pp. 139–156).

  6. Liu, C., & Fang, D. (2016). RSS distribution-based passive localization and its application in sensor networks. IEEE Transactions on Wireless Communications, 15(4), 2883–2895.

    Article  MathSciNet  Google Scholar 

  7. Jung, S. H., Moon, B. C., & Han, D. (2016). Unsupervised learning for crowdsourced indoor localization in wireless networks. IEEE Transactions on Mobile Computing, 15(11), 2892–2906.

    Article  Google Scholar 

  8. de Moraes, L. F. M., & Nunes, B. A. A. (2006). Calibration-free wlan location system based on dynamic mapping of signal strength. In Proceedings of the 4th ACM international workshop on Mobility management and wireless access, (pp. 92–99).

  9. Gwon, Y., & Jain, R. (2004). Error characteristics. Calibration-free Techniques for Wireless LAN-based Location Estimation, MobiWac (p. 29).

  10. Gu, Y., Lo, A., & Niemegeers, I. (2009). A survey of indoor positioning systems for wireless personal networks. IEEE Communications Surveys and Tutorials, 11(1).

  11. Jung, S. H., Moon, B.-C., & Han, D. (2017). Performance evaluation of radio map construction methods for Wi-Fi positioning systems. IEEE Transactions on Intelligent Transportation System, 18(4), 880–889.

    Article  Google Scholar 

  12. Chai, X., & Yang, Q. (2007). Reducing the calibration effort for probabilistic indoor location estimation. IEEE Transactions on Mobile Computing, 6(6), 649–662.

    Article  Google Scholar 

  13. Pan, J. J., Pan, S. J., Yin, J., Ni, L. M., & Yang, Q. (2012). Tracking mobile users in wireless networks via semi-supervised colocalization. IEEE Transactions on Pattern Analysis & Machine Intelligence, 34(3), 587.

    Article  Google Scholar 

  14. Tran, D. A., & Truong, P. (2013). Total variation regularization for training of indoor location fingerprints. ACM International Workshop on Mission-oriented Wireless Sensor Networking, 2013, 27–32.

    Google Scholar 

  15. Gao, C., & Harle, R. (2016). Easing the survey burden: Quantitative assessment of low-cost signal surveys for indoor positioning. In International conference on indoor positioning and indoor navigation (IPIN), (pp. 1–8).

  16. Chintalapudi, K., Padmanabha Iyer, A., & Padmanabhan, V. N. (2010) Indoor localization without the pain. Mobicom, (pp. 173–184).

  17. He, S., & Chan, S. H. G. (2017). Towards crowdsourced signal map construction via implicit interaction of IoT devices. In Annual IEEE international conference on sensing, communication. and networking (SECON), (pp. 1–9).

  18. Rai, A., Chintalapudi, K. K., Padmanabhan, V. N., & Sen, R. (2012). Zee: Zero-effort crowdsourcing for indoor localization. Mobicom, (pp. 293–304).

  19. Shen, G., Chen, Z., Zhang, P., Moscibroda, T., & Zhang, Y. (2013). Walkie–Markie: Indoor pathway mapping made easy.NSDI.

  20. Zhou, M., Zhang, Q., Wang, Y., & Tian, Z. (2017). Hotspot ranking based indoor mapping and mobility analysis using crowdsourced Wi-Fi signal, IEEE Access, 5, 3594–3602.

  21. Li, L., Shen, G., Zhao, C. (2014). Experiencing and handling the diversity in data density and environmental locality in an indoor positioning service. International Conference on Mobile Computing and Networking, 21(3).

  22. Bahl, P., & Padmanabhan, V. N. (2000). Radar: An in-building RF-based user location and tracking system. In IEEE conference on computer communications (INFOCOM), (vol. 2, pp. 775–784).

  23. Jiang, P. (2015). Indoor mobile localization based on Wi-Fi fingerprints important access point. International Journal of Distributed Sensor Networks, 45.

  24. Xuanmin, L., & Yang, Q. (2016). An improved dynamic prediction fingerprint localization algorithm based on KNN. In Sixth international conference on instrumentation and measurement, computer, communication and control, CY1425, (pp. 289–292).

  25. Xie, Y., & Wang, Y. (2016). An improved K-nearest-neighbor indoor localization method based on spearman distance. IEEE Signal Processing Letters, 23(3), 351–355.

    Article  Google Scholar 

  26. Wen, Y. (2015). Fundamental limits of RSS fingerprinting based indoor localization. In IEEE Conference on Computer Communications (INFOCOM), (pp. 2479–2487).

  27. Ali, K., Gatsis, N., & Akopian, D. (2016). Structured group sparsity: A novel indoor WLAN localization, outlier detection, and radio map interpolation scheme. IEEE Transactions on Vehicular Technology, pp. (99), 1–1.

  28. Zhao, H., Huang, B., & Jia, B. (2016). Applying kriging interpolation for Wi-Fi fingerprinting based indoor positioning systems. In Wireless communications and networking conference.

  29. Khalajmehrabadi, A., Gatsis, N., & Akopian, D. (2017). Structured group sparsity: A novel indoor WLAN localization, outlier detection, and radio map interpolation scheme. IEEE Transactions on Vehicular Technology, 66(7).

  30. Shau-Shiun, J., Shuo-Ju, Y., & Ya-Wen, L. (2015). Received signal strength database interpolation by Kriging for a Wi-Fi indoor positioning system. Sensors, 15(9).

  31. Shin, H., Chon, Y., Kim, Y., & Cha, H. (2011). MRI: Model-based radio interpolation for indoor war-walking. IEEE Transactions on Mobile Computing, 14(6).

  32. Pahlavan, K., & Levesque, A. (1995). Wireless information networks. New York: Wiley.

    Google Scholar 

  33. Papamanthou, C., Preparata, F. P., & Tamassia, R. (2008). Algorithms for location estimation based on RSSI sampling, 5389, pp. 72–86.

  34. King, T., Haenselmann, T., & Effelsberg, W. (2008). On-demand fingerprint selection for 802.11-based positioning systems. In International symposium on a world of wireless, mobile and multimedia networks, 82(1), 1–8.

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant Nos. 60903193, 61370199, 61672379, 61772251 and 61702365, the Tianjin Research Program of Application Foundation and Advanced Technology under Grant No. 12JCQNJC00200 and the Natural Science Foundation of Tianjin under Grant No. 17JCQNJC00700.

Author information

Authors and Affiliations

  1. School of Computer Software, Tianjin University, Tianjin, 300350, China

    Yanwei Li

  2. School of Computer Science and Technology, Tianjin University, Tianjin, 300350, China

    Gaotao Shi, Xiaobo Zhou, Wenyu Qu & Keqiu Li

  3. Tianjin Key Laboratory of Advanced Networking (TANK), Tianjin, 300350, China

    Yanwei Li, Gaotao Shi, Xiaobo Zhou, Wenyu Qu & Keqiu Li

Authors
  1. Yanwei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gaotao Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaobo Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wenyu Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Keqiu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gaotao Shi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Shi, G., Zhou, X. et al. Reducing the site survey using fingerprint refinement for cost-efficient indoor location. Wireless Netw 25, 1201–1213 (2019). https://doi.org/10.1007/s11276-018-1711-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-018-1711-6

Keywords

Search

Navigation