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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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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