Abstract
This paper aims at predicting accurate outdoor and indoor locations using deep neural networks, for the data collected using the Long-Range Wide-Area Network (LoRaWAN) communication protocol. First, we propose an interpolation aided fingerprinting-based localization system architecture. We propose a deep autoencoder method to effectively deal with the large number of missing samples/outliers caused by the large size and wide coverage of LoRa networks. We also leverage three different deep learning models, i.e., the Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN), for fingerprinting based location regression. The superior localization performance of the proposed system is validated by our experimental study using a publicly available outdoor dataset and an indoor LoRa testbed.