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Stacked Auto-Encoder for Scalable Indoor Localization in Wireless Sensor Networks

Abstract : In this paper, we propose a Deep Neural Network model based on WiFi-fingerprinting to improve the accuracy of zone location in a multi-building, multi-floor indoor environment. The proposed model is presented as a Stacked AutoEncoder (SAE) to allow efficient reduction of the feature space in order to achieve robust and precise classification. The multi-label classification is used to simplify and reduce the complexity of the learning classification task during the training phase. To achieve a hierarchical classification, we applied an argmax function on the multi-label output to convert the multi-label classification into multi-class classification ones to estimate the building, the floor and the zone identifier. Experimental results show that the proposed model achieves an accuracy of 100% for building, 99.66% for floor and 83.47% for zone location with a test time that does not exceed 10.21s.
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Conference papers
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https://hal-utt.archives-ouvertes.fr/hal-02362335
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Wednesday, November 13, 2019 - 6:34:50 PM
Last modification on : Tuesday, June 16, 2020 - 4:04:02 PM

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Souad Belmannoubi, Haifa Touati, Hichem Snoussi. Stacked Auto-Encoder for Scalable Indoor Localization in Wireless Sensor Networks. 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC), Jun 2019, Tangier, Morocco. pp.1245-1250, ⟨10.1109/IWCMC.2019.8766761⟩. ⟨hal-02362335⟩

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