Supervised and Semi-supervised Deep Learning-based Models for Indoor Location Prediction and Recognition

Bourgogne Franche-Comt e UTBM, F-90010, Belfort, France ABSTRACT Predicting smartphone users location with WiFi fingerprints has been a popular research topic recently. In this work, we propose two novel deep learning-based models, the con-volutional mixture density recurrent neural network and the V AE-based semi-supervised learning model. The convolu-tional mixture density recurrent neural network is designed for path prediction, in which the advantages of convolutional neural networks, recurrent neural networks and mixture density networks are combined. Further, since most of real-world datasets are not labeled, we devise the V AE-based model for the semi-supervised learning tasks. In order to test the proposed models, we conduct the validation experiments on the real-world datasets. The final results verify the effectiveness of our approaches and show the superiority over other existing methods. Index T erms-- Mixture density network, variational au-toencoder, semi-supervised learning, WiFi fingerprint, indoor positioning 1. INTRODUCTION Location based services (LBS) are essential for applications like location-based advertising, outdoor/indoor navigation and social networking, etc. With the help of significant advancement of the smartphone technology in recent decades, smartphone devices are integrated with various built-in sensors, such as GPS modules, WiFi modules, cellular modules, etc. Acquiring the data from such kinds of sensors enables researchers to study human activities. There are several types of data can be utilised for such research purpose.