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International Conference on Intelligent Computing

ICIC 2020: Intelligent Computing Methodologies pp 224–234Cite as

Deep Learning Based Fingerprints Reduction Approach for Visible Light-Based Indoor Positioning System

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12465))

Abstract

Received signal strength and fingerprints based indoor positioning algorithm has been commonly used in recent studies. The actual implementation of this method is, however, quite time-consuming and may not be possible in large spaces, mainly because a large number of fingerprints should be collected to maintain high positioning accuracy. In this work, we first propose the deep learning-based fingerprints reduction approach to reduce the data collection workload in the offline mode while ensuring low positioning error. After estimating the extra fingerprints using a deep learning model, these new fingerprints combine with the initially collected fingerprints to create the whole training dataset for the real estimation process. In the online mode, the final estimated location is determined using the combination of trilateration and k-nearest neighbors. The experiment results showed that mean positioning errors of 1.21 cm, 6.86 cm, and 7.51 cm are achieved in the center area, the edge area, and the corner area, respectively.

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References

  1. Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37(6), 1067–1080 (2007)

    Article  Google Scholar 

  2. Zhuang, Y., et al.: A survey of positioning systems using visible LED lights. IEEE Commun. Surv. Tutor. 20(3), 1963–1988 (2018). thirdquarter

    Article  Google Scholar 

  3. Zhao, C., Zhang, H., Song, J.: Fingerprint and visible light communication based indoor positioning method. In: 2017 9th International Conference on Advanced Infocomm Technology (ICAIT), Chengdu, pp. 204–209 (2017)

    Google Scholar 

  4. Aikawa, S., Yamamoto, S., Morimoto, M.: WLAN finger print localization using deep learning. In: 2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP), Auckland, pp. 541–542 (2018)

    Google Scholar 

  5. Shen, L., Zhang, Q., Pang, J., Xu, H., Li, P.: PRDL: relative localization method of RFID tags via phase and RSSI based on deep learning. IEEE Access 7, 20249–20261 (2019)

    Article  Google Scholar 

  6. Ou, C., et al.: A ZigBee position technique for indoor localization based on proximity learning. In: 2017 IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, pp. 875–880 (2017)

    Google Scholar 

  7. Chen, Y., Guan, W., Li, J., Song, H.: Indoor real-time 3-D visible light positioning system using fingerprinting and extreme learning machine. IEEE Access 8, 13875–13886 (2020)

    Article  Google Scholar 

  8. Tran, H.Q., Ha, C.: Fingerprint-based indoor positioning system using visible light communication—a novel method for multipath reflections. Electronics 8, 63 (2019)

    Article  Google Scholar 

  9. Tran, H.Q., Ha, C.: Improved visible light-based indoor positioning system using machine learning classification and regressio. Appl. Sci. 9, 1048 (2019). https://doi.org/10.3390/app9061048

    Article  Google Scholar 

  10. Alam, F., Chew, M.T., Wenge, T., Gupta, G.S.: An accurate visible light positioning system using regenerated fingerprint database based on calibrated propagation model. IEEE Trans. Instrum. Meas. 68(8), 2714–2723 (2019)

    Article  Google Scholar 

  11. Zhang, H., et al.: High-precision indoor visible light positioning using deep neural network based on the Bayesian regularization with sparse training point. IEEE Photonics J. 11(3), 1–10 (2019). Art no. 7903310

    Google Scholar 

  12. Ghassemlooy, Z., Popoola, W., Rajbhandari, S.: Optical wireless communications, system and channel modeling with MATLAB. CRC Press, Boca Raton (2012). ISBN 9781439851883

    Google Scholar 

  13. Yasir, M., Ho, S., Vellambi, B.N.: Indoor positioning system using visible light and accelerometer. J. Lightwave Technol. 32(19), 3306–3316 (2014). https://doi.org/10.1109/jlt.2014.2344772

    Article  Google Scholar 

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Acknowledgments

This work was supported by the KHNP (Korea Hydro & Nuclear Power Co., Ltd.) Research Fund Haeorum Alliance Nuclear Innovation Center of Ulsan University.

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Authors and Affiliations

  1. Robotics and Mechatronics Lab, Ulsan University, Ulsan, 44610, Republic of Korea

    Huy Q. Tran & Cheolkeun Ha

Authors
  1. Huy Q. Tran
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  2. Cheolkeun Ha
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Corresponding author

Correspondence to Cheolkeun Ha .

Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Tran, H.Q., Ha, C. (2020). Deep Learning Based Fingerprints Reduction Approach for Visible Light-Based Indoor Positioning System. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_19

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  • DOI: https://doi.org/10.1007/978-3-030-60796-8_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60795-1

  • Online ISBN: 978-3-030-60796-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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