Skip to main content
SpringerLink
Log in

On the Application of Graph Neural Networks for Indoor Positioning Systems

  • Chapter
  • First Online:
Machine Learning for Indoor Localization and Navigation

Abstract

Due to the inability of GPS (or other GNSS methods) to provide satisfactory precision for the indoor location scenario, indoor positioning systems resort to other signals already available on site, typically Wi-Fi given its ubiquity. However, instead of relying on an error-prone propagation model as in ranging methods, the popular fingerprinting positioning technique considers a more direct data-driven approach to the problem. First of all, the area of interest is divided into zones, and then a machine learning algorithm is trained to map, for instance, power measurements (RSSI) from APs to the localization zone, thus effectively turning the problem into a classification one. However, although the positioning problem is a geometrical one, virtually all methods proposed in the literature disregard the underlying structure of the data, using generic machine learning algorithms. In this chapter we consider instead a graph-based learning method, Graph Neural Networks, a paradigm that has emerged in the last few years and that constitutes the state of the art for several problems. After presenting the pertinent theoretical background, we discuss two possibilities to construct the underlying graph for the positioning problem. We then perform a thorough evaluation of both possibilities and compare it with some of the most popular machine learning alternatives. The main conclusion is that these graph-based methods obtain systematically better results, particularly with regard to practical aspects (e.g., gracefully tolerating faulty APs), which makes them a serious candidate to consider when deploying positioning systems.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/schollz/find3.

  2. 2.

    https://www.kaggle.com/giantuji/UjiIndoorLoc.

  3. 3.

    https://github.com/ffedee7/posifi_mnav/tree/master/data_analysis.

  4. 4.

    https://github.com/schollz/find3.

References

  1. Bahl P, Padmanabhan VN (2000) Radar: An in-building rf-based user location and tracking system. In: Proceedings IEEE INFOCOM 2000. Conference on computer communications. Nineteenth annual joint conference of the IEEE computer and communications societies (Cat. No. 00CH37064), vol 2. IEEE, pp 775–784

    Google Scholar 

  2. Basri C, El Khadimi A (2016) Survey on indoor localization system and recent advances of wifi fingerprinting technique. In: 2016 5th international conference on multimedia computing and systems (ICMCS). IEEE, pp 253–259

    Google Scholar 

  3. Bracco A, Grunwald F, Navcevich A, Capdehourat G, Larroca F (2020) Museum accessibility through wi-fi indoor positioning. Preprint. arXiv:200811340

    Google Scholar 

  4. Chen B, Chang RY (2022) Few-shot transfer learning for device-free fingerprinting indoor localization. CoRR abs/2201.12656. https://arxiv.org/abs/2201.12656, https://doi.org/2201.126562201.12656

  5. Chiou M, Liu Z, Yin Y, Liu A, Zimmermann R (2020) Zero-shot multi-view indoor localization via graph location networks. In: MM ’20: The 28th ACM international conference on multimedia, virtual event/Seattle, WA, USA, October 12–16, 2020. ACM, pp 3431–3440. https://doi.org/10.1145/3394171.3413856

  6. Coley CW, Jin W, Rogers L, Jamison TF, Jaakkola TS, Green WH, Barzilay R, Jensen KF (2019) A graph-convolutional neural network model for the prediction of chemical reactivity. Chemical Science 10(2):370–377

    Article  Google Scholar 

  7. Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 29:3844–3852

    Google Scholar 

  8. Ding Y, Jiang D, Liu Y, Zhang D, He T (2022) Smartloc: Indoor localization with smartphone anchors for on-demand delivery. Proc ACM Interact Mob Wearable Ubiquitous Technol 5(4). https://doi.org/10.1145/3494972

  9. Dong Y, Chawla NV, Swami A (2017) Metapath2vec: Scalable representation learning for heterogeneous networks. Association for Computing Machinery, New York, NY, USA, KDD ’17. https://doi.org/10.1145/3097983.3098036

  10. Du J, Zhang S, Wu G, Moura JMF, Kar S (2017) Topology adaptive graph convolutional networks. CoRR abs/1710.10370. http://arxiv.org/abs/1710.10370, 1710.10370

  11. Estrach JB, Zaremba W, Szlam A, LeCun Y (2014) Spectral networks and deep locally connected networks on graphs. In: 2nd international conference on learning representations, ICLR, vol 2014

    Google Scholar 

  12. Fey M, Lenssen JE (2019) Fast graph representation learning with PyTorch Geometric. In: ICLR workshop on representation learning on graphs and manifolds

    Google Scholar 

  13. Gama F, Marques AG, Leus G, Ribeiro A (2019) Convolutional neural network architectures for signals supported on graphs. IEEE Trans Signal Process 67(4):1034–1049. https://doi.org/10.1109/TSP.2018.2887403

    Article  MathSciNet  MATH  Google Scholar 

  14. Gama F, Bruna J, Ribeiro A (2020) Stability properties of graph neural networks. IEEE Trans Signal Process 68:5680–5695. https://doi.org/10.1109/TSP.2020.3026980

    Article  MathSciNet  MATH  Google Scholar 

  15. Isufi E, Gama F, Ribeiro A (2021) Edgenets:edge varying graph neural networks. IEEE Trans Pattern Anal Mach Intell, 1–1. https://doi.org/10.1109/TPAMI.2021.3111054

  16. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th international conference on learning representations (ICLR-17)

    Google Scholar 

  17. Küpper A (2005) Location-based services: fundamentals and operation. Wiley

    Book  Google Scholar 

  18. Lezama F, González GG, Larroca F, Capdehourat G (2021) Indoor localization using graph neural networks. In: 2021 IEEE URUCON, pp 51–54. https://doi.org/10.1109/URUCON53396.2021.9647082

  19. Lin H, Liu G, Li F, Zuo Y (2021) Where to go? Predicting next location in iot environment. Front Comput Sci 15(1):151306. https://doi.org/10.1007/s11704-019-9118-9

    Article  Google Scholar 

  20. Morris C, Ritzert M, Fey M, Hamilton WL, Lenssen JE, Rattan G, Grohe M (2019) Weisfeiler and leman go neural: Higher-order graph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 33(01), pp 4602–4609. https://doi.org/10.1609/aaai.v33i01.33014602, https://ojs.aaai.org/index.php/AAAI/article/view/4384

  21. Navarin N, Tran DV, Sperduti A (2019) Universal readout for graph convolutional neural networks. In: 2019 international joint conference on neural networks (IJCNN), pp 1–7. https://doi.org/10.1109/IJCNN.2019.8852103

  22. Nowicki M, Wietrzykowski J (2017) Low-effort place recognition with wifi fingerprints using deep learning. In: International conference automation. Springer, pp 575–584

    Google Scholar 

  23. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  24. Ruiz L, Gama F, Ribeiro A (2021) Graph neural networks: Architectures, stability, and transferability. Proc IEEE 109(5):660–682. https://doi.org/10.1109/JPROC.2021.3055400

    Article  Google Scholar 

  25. Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Networks 20(1):61–80. https://doi.org/10.1109/TNN.2008.2005605

    Article  Google Scholar 

  26. Schlichtkrull M, Kipf TN, Bloem P, van den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: Gangemi A, Navigli R, Vidal ME, Hitzler P, Troncy R, Hollink L, Tordai A, Alam M (eds) The semantic web. Springer International Publishing, Cham, pp 593–607

    Chapter  Google Scholar 

  27. Shi C, Hu B, Zhao WX, Yu PS (2019) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng 31(2):357–370. https://doi.org/10.1109/TKDE.2018.2833443

    Article  Google Scholar 

  28. Shuman DI, Narang SK, Frossard P, Ortega A, Vandergheynst P (2013) The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process Mag 30(3):83–98. https://doi.org/10.1109/MSP.2012.2235192

    Article  Google Scholar 

  29. Sun Y, Xie Q, Pan G, Zhang S, Xu S (2021) A novel GCN based indoor localization system with multiple access points. In: 17th international wireless communications and mobile computing, IWCMC 2021, Harbin City, China, June 28–July 2, 2021. IEEE, pp 9–14. https://doi.org/10.1109/IWCMC51323.2021.9498616

  30. Tang J, Zhang J, Jin R, Yang Z, Cai K, Zhang L, Su Z (2011) Topic level expertise search over heterogeneous networks. Machine Learning 82(2):211–237

    Article  MathSciNet  Google Scholar 

  31. Torres-Sospedra J, Montoliu R, Martínez-Usó A, Avariento JP, Arnau TJ, Benedito-Bordonau M, Huerta J (2014) Ujiindoorloc: A new multi-building and multi-floor database for wlan fingerprint-based indoor localization problems. In: 2014 international conference on indoor positioning and indoor navigation (IPIN). IEEE, pp 261–270

    Google Scholar 

  32. Varshavsky A, LaMarca A, Hightower J, De Lara E (2007) The skyloc floor localization system. In: Fifth annual IEEE international conference on pervasive computing and communications (PerCom’07). IEEE, pp 125–134

    Google Scholar 

  33. Vignac C, Loukas A, Frossard P (2020) Building powerful and equivariant graph neural networks with structural message-passing. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H (eds) Advances in neural information processing systems, vol 33. Curran Associates, Inc., pp 14143–14155. https://proceedings.neurips.cc/paper/2020/file/a32d7eeaae19821fd9ce317f3ce952a7-Paper.pdf

  34. Wang P, Xu B, Wu Y, Zhou X (2015) Link prediction in social networks: the state-of-the-art. Sci China Inf Sci 58(1):1–38

    Article  Google Scholar 

  35. Whitehouse K, Karlof C, Culler D (2007) A practical evaluation of radio signal strength for ranging-based localization. SIGMOBILE Mob Comput Commun Rev 11(1):41–52. https://doi.org/10.1145/1234822.1234829

    Article  Google Scholar 

  36. Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2021) A comprehensive survey on graph neural networks. IEEE Trans Neural Networks Learn Syst 32(1):4–24. https://doi.org/10.1109/TNNLS.2020.2978386

    Article  MathSciNet  Google Scholar 

  37. Yan W, Jin D, Lin Z, Yin F (2021) Graph neural network for large-scale network localization. In: IEEE international conference on acoustics, speech and signal processing, ICASSP 2021, Toronto, ON, Canada, June 6–11, 2021. IEEE, pp 5250–5254. https://doi.org/10.1109/ICASSP39728.2021.9414520

  38. Yiu S, Dashti M, Claussen H, Perez-Cruz F (2017) Wireless rssi fingerprinting localization. Signal Processing 131:235–244. https://doi.org/10.1016/j.sigpro.2016.07.005, https://www.sciencedirect.com/science/article/pii/S0165168416301566

  39. Zafari F, Gkelias A, Leung KK (2019) A survey of indoor localization systems and technologies. IEEE Commun Surv Tutor 21(3):2568–2599

    Article  Google Scholar 

  40. Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: A review of methods and applications. AI Open 1:57–81. https://doi.org/10.1016/j.aiopen.2021.01.001, https://www.sciencedirect.com/science/article/pii/S2666651021000012

Download references

Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay

    Facundo Lezama, Federico Larroca & Germán Capdehourat

Authors
  1. Facundo Lezama
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Federico Larroca
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Germán Capdehourat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Federico Larroca .

Editor information

Editors and Affiliations

  1. Colorado State University, Fort Collins, CO, USA

    Saideep Tiku

  2. Colorado State University, Fort Collins, CO, USA

    Sudeep Pasricha

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Lezama, F., Larroca, F., Capdehourat, G. (2023). On the Application of Graph Neural Networks for Indoor Positioning Systems. In: Tiku, S., Pasricha, S. (eds) Machine Learning for Indoor Localization and Navigation. Springer, Cham. https://doi.org/10.1007/978-3-031-26712-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26712-3_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26711-6

  • Online ISBN: 978-3-031-26712-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation