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Low-Effort Place Recognition with WiFi Fingerprints Using Deep Learning

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Automation 2017 (ICA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 550))

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Abstract

Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions.

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Notes

  1. 1.

    https://keras.io/.

  2. 2.

    https://www.tensorflow.org/.

  3. 3.

    http://scikit-learn.org/.

  4. 4.

    https://archive.ics.uci.edu/ml/datasets/UJIIndoorLoc.

References

  1. Kasiński, A., Skrzypczyński, P.: Perception network for the team of indoor mobile robots, concept, architecture, implementation. Eng. Appl. Artif. Intell. 14(2), 125–137 (2001)

    Article  Google Scholar 

  2. Skrzypczyński, P.: Simultaneous localization and mapping: a feature-based probabilistic approach. Int. J. Appl. Math. Comput. Sci. 19(4), 575–588 (2009)

    MATH  Google Scholar 

  3. Montiel, J.M.M., Mur-Artal, R., Tardós, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. CoRR, abs/1502.00956 (2015)

    Google Scholar 

  4. Nowicki, M., Skrzypczyński, P.: Combining photometric and depth data for lightweight and robust visual odometry. In: European Conference on Mobile Robots (ECMR), pp. 125–130, Barcelona (2013)

    Google Scholar 

  5. Fularz, M., Nowicki, M., Skrzypczyński, P.: Adopting feature-based visual odometry for resource-constrained mobile devices. In: Campilho, A., Kamel, M. (eds.) ICIAR 2014. LNCS, vol. 8815, pp. 431–441. Springer, Cham (2014). doi:10.1007/978-3-319-11755-3_48

    Google Scholar 

  6. Nowicki, M., Skrzypczyński, P.: Indoor navigation with a smartphone fusing inertial and WiFi data via factor graph optimization. In: Sigg, S., Nurmi, P., Salim, F. (eds.) MobiCASE 2015. LNICSSITE, vol. 162, pp. 280–298. Springer, Cham (2015). doi:10.1007/978-3-319-29003-4_16

    Chapter  Google Scholar 

  7. Plaza, J.M.C., Olivera, V.M., Serrano, O.S.: WiFi localization methods for autonomous robots. Robotica 24(4), 455–461 (2006)

    Article  Google Scholar 

  8. Biswas, J., Veloso, M.: WiFi localization and navigation for autonomous indoor mobile robots. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 4379–4384 (2010)

    Google Scholar 

  9. Sweatt, M., Ayoade, A., Han, Q., Steele, J., Al-Wahedi, K., Karki, H.: WiFi based communication and localization of an autonomous mobile robot for refinery inspection. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4490–4495 (2015)

    Google Scholar 

  10. Ciurana, M., Barcelo-Arroyo, F., Izquierdo, F.: A ranging method with IEEE 802.11 data frames for indoor localization. In: Proceedings of the IEEE Wireless Communications and Networking Conference, Washington, DC, USA, pp. 2092–2096. IEEE Computer Society (2007)

    Google Scholar 

  11. Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: INFOCOM, Proceedings of Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 775–784. IEEE (2000)

    Google Scholar 

  12. Moreira, A., Nicolau, M.J., Meneses, F., Costa, A.: Wi-Fi fingerprinting in the real world - RTLS@UM at the EvAAL competition. In: 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–10 (2015)

    Google Scholar 

  13. Khodayari, S., Maleki, M., Hamedi, E.: A RSS-based fingerprinting method for positioning based on historical data. In: 2010 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), pp. 306–310 (2010)

    Google Scholar 

  14. Torres-Sospedra, J., Montoliu, R., Martínez-Usó, A., Avariento, J.P., Arnau, T.J., Benedito-Bordonau, M., Huerta, J.: 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), pp. 261–270 (2014)

    Google Scholar 

  15. Elbasiony, R., Gomaa, W.: Wifi localization for mobile robots based on random forests and GPLYM. In: 2014 13th International Conference on Machine Learning and Applications (ICMLA), pp. 225–230 (2014)

    Google Scholar 

  16. Beer, Y.: WiFi fingerprinting using bayesian and hierarchical supervised machine learning assisted by GPS. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (2016)

    Google Scholar 

  17. Luo, J., Gao, H.: Deep belief networks for fingerprinting indoor localization using ultrawideband technology. Int. J. Distrib. Sen. Netw. 2016, Article no. 18 (2016)

    Google Scholar 

  18. Lajoie, I., Bengio, Y., Vincent, P., Larochelle, H., Manzagol, P.-A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)

    MathSciNet  MATH  Google Scholar 

  19. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

This research was funded by the National Science Centre in Poland in years 2016–2019 under the grant 2015/17/N/ST6/01228.

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Authors and Affiliations

  1. Institute of Control and Information Engineering, Poznań University of Technology, ul. Piotrowo 3A, 60-965, Poznań, Poland

    Michał Nowicki & Jan Wietrzykowski

Authors
  1. Michał Nowicki
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  2. Jan Wietrzykowski
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Correspondence to Michał Nowicki .

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Editors and Affiliations

  1. Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland

    Roman Szewczyk

  2. Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland

    Cezary Zieliński

  3. Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland

    Małgorzata Kaliczyńska

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Nowicki, M., Wietrzykowski, J. (2017). Low-Effort Place Recognition with WiFi Fingerprints Using Deep Learning. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2017. ICA 2017. Advances in Intelligent Systems and Computing, vol 550. Springer, Cham. https://doi.org/10.1007/978-3-319-54042-9_57

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  • DOI: https://doi.org/10.1007/978-3-319-54042-9_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54041-2

  • Online ISBN: 978-3-319-54042-9

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