Advertisement

Deep Recurrent Neural Networks for Wi-Fi Based Indoor Trajectory Sensing

  • Hao LiEmail author
  • Joseph K. Ng
  • Junxing Ke
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1036)

Abstract

Wi-Fi based sensing becomes more and more popular in ubiquitous computing with prevalence of Wi-Fi devices. In this paper, we propose a new task, indoor trajectory sensing, based on Received Signal Strength Indicator (RSSI) of Wi-Fi. Traditional distance measure based methods, like Dynamic Time Warping (DTW) based Nearest-Neighbor (1NN) method, have poor performance in indoor trajectory sensing due to that RSSI of Wi-Fi in indoor environment is fluctuating, partially missing, time-varying and device-dependent. Recently, Recurrent Neural Networks (RNN) and its variants have strong abilities in learning the temporal dependency of sequence data since it can extract more meaningful features. Consequently, it is necessary to design an RNN model for the indoor trajectory sensing problem with relatively small size data. We adopt a passive way to collect Wi-Fi signals from the smart phone to ensure more data collected and generate multiple time series for each trajectory. For the recurrent neural network training, RNN and its variants are applied into our sequence data to find more meaningful patterns especially for different environment and devices. Series of real-world experiments have been conducted in our test bed and the results show that the deep based approach can achieve better performance than traditional methods with challenging environment and device factors.

Keywords

Ubiquitous computing Wi-Fi sensing Indoor trajectory Recurrent neural networks 

References

  1. 1.
    Jeong, Y.-S., Jeong, M.K., Omitaomu, O.A.: Weighted dynamic time warping for time series classification. Pattern Recognit. 44(9), 2231–2240 (2011)CrossRefGoogle Scholar
  2. 2.
    Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., Keogh, E.: Experimental comparison of representation methods and distance measures for time series data. Data Min. Knowl. Disc. 26(2), 275–309 (2013)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chouakria-Douzal, A., Nagabhushan, P.N.: Improved fréchet distance for time series. In: Data Science and Classification, pp. 13–20. Springer, Berlin (2006)Google Scholar
  4. 4.
    Chuan-Chin, P., Chung, W.-Y.: Mitigation of multipath fading effects to improve indoor RSSI performance. IEEE Sens. J. 8(11), 1884–1886 (2008)CrossRefGoogle Scholar
  5. 5.
    Zheng, V.W., Xiang, E.W., Yang, Q., Shen, D.: Transferring localization models over time. In: AAAI, pp. 1421–1426 (2008)Google Scholar
  6. 6.
    Li, H., Ng, J.K., Cheng, V.C.W., Cheung, W.K.: Fast indoor localization for exhibition venues with calibrating heterogeneous mobile devices. Internet of Things 3, 175–186 (2018)CrossRefGoogle Scholar
  7. 7.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  8. 8.
    Wang, X., Gao, L., Mao, S., Pandey, S.: Deepfi: deep learning for indoor fingerprinting using channel state information. In: 2015 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1666–1671. IEEE (2015)Google Scholar
  9. 9.
    Haseeb, M.A.A., Parasuraman, R.: Wisture: RNN-based learning of wireless signals for gesture recognition in unmodified smartphones. arXiv preprint arXiv:1707.08569 (2017)
  10. 10.
    Hu, W., Wang, Y., Song, L.: Sequence-type fingerprinting for indoor localization. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), Banff, Alberta, Canada (2015)Google Scholar
  11. 11.
    Ye, X., Wang, Y., Hu, W., Song, L., Gu, Z., Li, D.: Warpmap: accurate and efficient indoor location by dynamic warping in sequence-type radio-map. In: 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 1–9. IEEE (2016)Google Scholar
  12. 12.
    Hochreiter, S., Schmidhuber, J.: LSTM can solve hard long time lag problems. In: Advances in Neural Information Processing Systems, pp. 473–479 (1997)Google Scholar
  13. 13.
    Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
  14. 14.
    Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
  15. 15.
    Weiss, G., Goldberg, Y., Yahav, E.: On the practical computational power of finite precision rnns for language recognition. arXiv preprint arXiv:1805.04908 (2018)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceHong Kong Baptist UniversityHong KongChina

Personalised recommendations