Skip to main content
SpringerLink
Log in

MAPS: Indoor Localization Algorithm Based on Multiple AP Selection

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

In recent years, indoor fingerprint-based localization algorithm has been widely used by applications on smart phone. In these localization algorithms, it’s very popular to use WiFi signal characteristics to represent the location fingerprint. With the fast popularization of WiFi, the WiFi access points (APs) could be seen everywhere. However, as the number of APs increases, the dimension of the fingerprint and the complexity of fingerprint-based localization algorithm subsequently increase. Responding to the above challenges, this paper proposes a novel indoor localization algorithm MAPS (indoor localization algorithm based on multiple access point selection). MAPS could effectively reduce the complexity of localization computation, and improve the performance of localization through AP selection method. With the first round AP selection, MAPS can obtain a stable subset of AP, thus reducing the dimension of fingerprint, and obtaining better discrimination. And with the second round of AP selection, AP subset could be further condensed to construct a decision tree in each location cluster. This step can further improve the localization performance. The experimental results shown, as compared with classical indoor localization algorithm, MAPS has better positioning accuracy, and could achieve the accuracy of over 90% within 2m location error.

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

Similar content being viewed by others

References

  1. Kaemarungsi K, Krishnamurthy P (2004) Modeling of indoor positioning systems based on location fingerprinting, Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies. IEEE, vol 2, pp 1012–1022

  2. Youssef MA, Agrawala A, Shankar AU (2003) WLAN location determination via clustering and probability distributions. Proceedings of the 1st IEEE international conference on pervasive computing and communications, pp 143–150

  3. Du L, Bai Y, Chen L (2007) Access point selection strategy for large-scale wireless local area networks. IEEE Wireless Communications and Networking Conference 2007:2161–2166

    Google Scholar 

  4. Chen Y, Yang Q, Yin J, et al. (2006) Power-efficient access-point selection for indoor location estimation. IEEE Trans Knowl Data Eng 18(7):877–888

    Article  Google Scholar 

  5. Zhao Q et al (2010) An effective preprocessing scheme for WLAN-based fingerprint positioning systems, 2010 12th IEEE international conference on communication technology (ICCT)

  6. Kushki A, Plataniotis KN, Venetsanopoulos AN (2007) Kernel-based positioning in wireless local area networks. IEEE Trans Mobile Comput 6(6):689–705

    Article  Google Scholar 

  7. Zou G et al (2014) An indoor positioning algorithm using joint information entropy based on WLAN fingerprint, 2014 International conference on computing, communication and networking technologies (ICCCNT)

  8. Laoudias C, Panayiotou CG, Kemppi P (2010) On the RBF-based positioning using WLAN signal strength fingerprints, 2010 7th workshop on positioning navigation and communication (WPNC)

  9. Liang D, Zhang Z, Peng M (2015) Access point reselection and adaptive cluster splitting-based indoor localization in wireless local area networks. IEEE Internet of Things Journal 2(6):573–585

    Article  Google Scholar 

  10. Zhou M, Wei Y, Tian Z, et al. (2017) Achieving cost-efficient indoor fingerprint localization on WLAN platform: A hypothetical test approach. IEEE Access 5:15865–15874

    Article  Google Scholar 

  11. Jia B, Huang B, Gao H, et al. (2019) Selecting critical WiFi APs for indoor localization based on a theoretical error analysis. IEEE Access 7:36312–36321

    Article  Google Scholar 

  12. Duda RO, Hart PE, Stork DG (2012) Pattern classification [M]. Wiley, New York

    MATH  Google Scholar 

  13. Quinlan JR (2014) C4. 5: programs for machine learning [M]. Elsevier

Download references

Acknowledgements

The financial support of the program of Key Industry Innovation Chain of Shaanxi Province, China (2017ZDCXL-GY-04-02), of the program of Xi’an Science and Technology Plan (201805029YD7CG13(5) ), Shaanxi, China, of Key R&D Program C The Industry Project of Shaanxi (Grant No. 2018GY-017), of Key R&D Program C The Industry Project of Shaanxi (Grant No. 2017GY-191) and of Education Department of Shaanxi Province Natural Science Foundation, China (15JK1742) are gratefully acknowledged.

Author information

Authors and Affiliations

  1. School of Telecommunications Engineering, State Key Labs of ISN, Xidian University, 710071, Xi’an, Shaanxi, People’s Republic of China

    Pengyu Huang, Haojie Zhao & Wei Liu

  2. School of Astronautics and Aeronautic, University of Electronic Science and Technology of China, 611731, Chengdu, People’s Republic of China

    Dingde Jiang

Authors
  1. Pengyu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haojie Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dingde Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pengyu Huang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, P., Zhao, H., Liu, W. et al. MAPS: Indoor Localization Algorithm Based on Multiple AP Selection. Mobile Netw Appl 26, 649–656 (2021). https://doi.org/10.1007/s11036-019-01411-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-019-01411-7

Keywords

Search

Navigation