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A Deep Neural Network Based on Stacked Auto-encoder and Dataset Stratification in Indoor Location

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12743))

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Abstract

Indoor location has become the core part in the large-scale location-aware services, especially in the extendable/scalable applications. Fingerprint location by using the signal strength indicator (RSSI) of the received WiFi signal has the advantages of full coverage and strong expansibility. It also has the disadvantages of requiring data calibration and lacking samples under the dynamic environment. This paper describes a deep neural network method used for indoor positioning (DNNIP) based on stacked auto-encoder and data stratification. The experimental results show that this DNNIP has better classification accuracy than the machine learning algorithms that are based on UJIIndoorLoc dataset.

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Authors and Affiliations

  1. Department of Communication, Shanghai Normal University, Shanghai, 200234, People’s Republic of China

    Jing Zhang & Ying Su

Authors
  1. Jing Zhang
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  2. Ying Su
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Corresponding author

Correspondence to Ying Su .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Zhang, J., Su, Y. (2021). A Deep Neural Network Based on Stacked Auto-encoder and Dataset Stratification in Indoor Location. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12743. Springer, Cham. https://doi.org/10.1007/978-3-030-77964-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-77964-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77963-4

  • Online ISBN: 978-3-030-77964-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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