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Deep Neural Networks for Indoor Localization Using WiFi Fingerprints

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Book cover Mobile, Secure, and Programmable Networking (MSPN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 11557))

Abstract

In this paper, we propose a novel Wi-Fi positioning method based on Deep Learning. More specifically, we investigate a Stacked AutoEncoder-based model for global location recognition from WiFi fingerprinting data. Stacked AutoEncoder works very well in learning useful high-level features for better representation of input raw data. For our proposed model, two trained unsupervised autoencoders were stacked, then the whole network was trained globally by adding a Softmax output layer for classification. The experimental results show that our Deep Learning based model performs better than SVM and KNN machine learning approaches in a large multi-floor building composed of 162 rooms. Our model achieves an accuracy of \(85.58\%\) and a test time that does not exceed 0.26 s.

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Author information

Authors and Affiliations

  1. Hatem Bettaher IResCoMath Research Unit, Gabès, Tunisia

    Souad BelMannoubi & Haifa Touati

  2. Faculty of Science of Gabes, University of Gabes, Gabès, Tunisia

    Souad BelMannoubi & Haifa Touati

Authors
  1. Souad BelMannoubi
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  2. Haifa Touati
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Corresponding author

Correspondence to Souad BelMannoubi .

Editor information

Editors and Affiliations

  1. Télécom SudParis, Évry, France

    Éric Renault

  2. Conservatoire National des Arts et Métiers, Paris, France

    Selma Boumerdassi

  3. Hassan II Mohammedia University, Mohammedia, Morocco

    Cherkaoui Leghris

  4. Conservatoire National des Arts et Métiers, Paris, France

    Samia Bouzefrane

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BelMannoubi, S., Touati, H. (2019). Deep Neural Networks for Indoor Localization Using WiFi Fingerprints. In: Renault, É., Boumerdassi, S., Leghris, C., Bouzefrane, S. (eds) Mobile, Secure, and Programmable Networking. MSPN 2019. Lecture Notes in Computer Science(), vol 11557. Springer, Cham. https://doi.org/10.1007/978-3-030-22885-9_21

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  • DOI: https://doi.org/10.1007/978-3-030-22885-9_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22884-2

  • Online ISBN: 978-3-030-22885-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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