Skip to main content
SpringerLink
Log in

A scalable indoor localization algorithm based on distance fitting and fingerprint mapping in Wi-Fi environments

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With ever-increasing demands on location-based services in indoor environments, indoor localization technologies have attracted considerable attention in both industrial and academic communities. In this work, we propose a scalable indoor localization algorithm (SILA) consisting of two components, namely an annulus-based localization (ABL) component and a local search-based localization (LSL) component, with the objectives of enhancing localization accuracy and reducing online computational overhead. First, the ABL component is developed based on distance fitting using received signal strength indicator (RSSI) of Wi-Fi-based devices. In particular, a distance-RSSI fitting model is proposed based on multinomial function fitting, which is adopted to estimate the distance between the Wi-Fi access point (AP) and the mobile device. On this basis, an annulus construction scheme is proposed to confine the online searching space for possible locations of the mobile device. In addition, based on the observation of signal attenuation characteristics in different physical environments, we design a subarea division scheme, which not only enables the system to choose proper distance-RSSI fitting functions in different areas, but also reduces the overhead of distance fitting. Second, the LSL component is developed based on fingerprint mapping using RSSIs collected at APs. In particular, an RSSI distribution probability model is derived to better map the signal features of an online point (OP) with that of reference points (RPs). Then, an online localization algorithm is proposed, which selects a set of candidate RPs based on Bayes theorem and estimates the final location of an OP using K-nearest-neighbor (KNN) method. Finally, we implement the system prototype and compare the performance of SILA with two representative solutions in the literature. An extensive performance evaluation is conducted in real-world environments, and the results conclusively demonstrate the superiority of SILA in terms of both localization accuracy and system scalability.

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. Cui W, Zhang L, Li B, Guo J, Meng W, Wang H, Xie L (2017) Received-signal-strength based indoor positioning using random vector functional link network. IEEE Trans Ind Inf PP(99):1–1

  2. Lee K, Nam Y, Min SD (2018) An indoor localization solution using bluetooth rssi and multiple sensors on a smartphone. Multimed Tools Appl 77(10):12635–12654

    Article  Google Scholar 

  3. Seco F, Jimenez A (2018) Smartphone-based cooperative indoor localization with rfid technology. Sensors 18(1):266

    Article  Google Scholar 

  4. Liu Z, Zhang L, Liu Q, Yin Y, Cheng L, Zimmermann R (2017) Fusion of magnetic and visual sensors for indoor localization: Infrastructure-free and more effective. IEEE Trans Multimed 19(4):874–888

    Article  Google Scholar 

  5. He S, Chan GSH (2016) Wi-fi fingerprint-based indoor positioning: recent advances and comparisons. IEEE Commun Surv Tutor 18(1):466–490

    Article  Google Scholar 

  6. Liu K, Zhang H, Ng JK, Xia Y, Feng L, Lee VCS, Son SH (2018) Toward low-overhead fingerprint-based indoor localization via transfer learning: design, implementation, and evaluation. IEEE Trans Ind Inf 14(3):898–908

    Article  Google Scholar 

  7. Zhang H, Llorca J, Davis CC, Milner SD (2012) Nature-inspired self-organization, control, and optimization in heterogeneous wireless networks. IEEE Trans Mobile Comput 11(7):1207–1222

    Article  Google Scholar 

  8. Heidari M, Alsindi N, Pahlavan K (2009) Udp identification and error mitigation in toa-based indoor localization systems using neural network architecture. IEEE Trans Wireless Commun 8(7):3597–3607

    Article  Google Scholar 

  9. Jung S, Hann S, Park C (2011) Tdoa-based optical wireless indoor localization using led ceiling lamps. IEEE Trans Consumer Electron 57(4):1592–1597

    Article  Google Scholar 

  10. Yang S, Kim H, Son Y, Han S (2014) Three-dimensional visible light indoor localization using AOA and RSS with multiple optical receivers. J Lightw Technol 32(14):2480–2485

    Article  Google Scholar 

  11. Wang B, Zhou S, Liu W, Mo Y (2015) Indoor localization based on curve fitting and location search using received signal strength. IEEE Trans Ind Electron 62(1):572–582

    Article  Google Scholar 

  12. Zhang H, Cao X, Ho JKL, Chow TWS (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inf 13(2):520–531

    Article  Google Scholar 

  13. Youssef M (2005) The horus wlan location determination system. In: Proceedings of International Conference on Mobile Systems, Applications, and Services, vol. 14, pp 357–374

  14. Premebida C, Faria DR, Nunes U (2017) Dynamic bayesian network for semantic place classification in mobile robotics. Auton Robots 41(5):1161–1172

    Article  Google Scholar 

  15. Ferris B, Fox D, Lawrence N (2007) Wifi-slam using gaussian process latent variable models. International joint conference on artifical intelligence, pp 2480–2485

  16. Bahl P, Padmanabhan VN (2000) Radar: an in-building rf-based user location and tracking system. In: Nineteenth joint conference of the IEEE computer and communications societies, vol. 2, pp 775–784

  17. Xie Y, Wang Y, Nallanathan A, Wang L (2016) An improved k-nearest-neighbor indoor localization method based on spearman distance. IEEE Signal Process Lett 23(3):351–355

    Article  Google Scholar 

  18. Cho H, Kim SW (2010) Mobile robot localization using biased chirp-spread-spectrum ranging. IEEE Trans Ind Electron 57(8):2826–2835

    Article  Google Scholar 

  19. Chintalapudi K, Iyer AP, Padmanabhan VN (2010) Indoor localization without the pain. In: Sixteenth international conference on mobile computing and networking, vol. 49, pp. 173–184

  20. Farid Z, Nordin R, Ismail M (2013) Recent advances in wireless indoor localization techniques and system. J Comput Netw Commun 2013:185138

    Google Scholar 

  21. Peng Y, Fan W, Dong X, Zhang X (2017) An iterative weighted KNN (IW-KNN) based indoor localization method in bluetooth low energy (BLE) environment. In: Ubiquitous intelligence computing, pp 794–800

  22. Shu Y, Huang Y, Zhang J, Cheng P, Chen J, Kang GS (2016) Gradient-based fingerprinting for indoor localization and tracking. IEEE Trans Ind Electron 63(4):2424–2433

    Article  Google Scholar 

  23. Yin Z, Wu C (2017) Gain without pain: accurate wifi-based localization using fingerprint spatial gradient. Proc ACM Interact Mob Wearable Ubiquitous Technol 1(2):29

    Google Scholar 

  24. Katircioglu O, Isel H, Ceylan O, Taraktas F, Yagci HB (2012) Comparing ray tracing, free space path loss and logarithmic distance path loss models in success of indoor localization with RSSI. Telecommunications Forum, pp. 313–316

  25. Bholowalia P, Kumar A (2014) Ebk-means: a clustering technique based on elbow method and k-means in WSN. Int J Comput Appl 105(9):17–24

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Science Foundation of China under Grant Nos. 61872049, 61572088 and 61876025; the Frontier Interdisciplinary Research Funds for the Central Universities (Project No. 2018CDQYJSJ0034); and the Venture & Innovation Support Program for Chongqing Overseas Returnees (Project No. cx2018016).

Author information

Authors and Affiliations

  1. Department of Computer Science, Chongqing University, Chongqing, 400040, China

    Hao Zhang, Kai Liu, Feiyu Jin & Liang Feng

  2. Computer Science Department, City University of Hong Kong, Kowloon Tong, Hong Kong

    Victor Lee

  3. Computer Science Department, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Joseph Ng

Authors
  1. Hao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Feiyu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liang Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Victor Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Joseph Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kai Liu.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

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

Zhang, H., Liu, K., Jin, F. et al. A scalable indoor localization algorithm based on distance fitting and fingerprint mapping in Wi-Fi environments. Neural Comput & Applic 32, 5131–5145 (2020). https://doi.org/10.1007/s00521-018-3961-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3961-8

Keywords

Search

Navigation