Towards the Implementation of Recurrent Neural Network Schemes for WiFi Fingerprint-Based Indoor Positioning

The rapid development of Indoor Positioning System has attracted researcher to develop a robust scheme to predict the location based on Received Signal Strength Indicator (RSSI) signal. A lot of research topics presented in many journals and conferences by many researchers concern indoor positioning system as a main topic [1], [2]. Currently, the study related to find the robust algorithm for indoor positioning system becomes a high demand topic in several conferences. Our work intents to evaluate the effectiveness of Recurrent Neural Network (RNN) as a deep learning technique to be implemented in this field. In addition, LSTM as a variant of RNN scheme is also implemented. The purpose of this implementation is to explore both LSTM and original RNN to be utilized for localization in indoor positioning scheme, especially for Wifi Fingerprinting Dataset. From all evaluations, our proposed approach could get 99.7% accuracy for predicting which floor the sensor belongs to. In addition, the distance errors of our scheme are around 2.5-2.7 meters.