The StoryTeller: Scalable Building- and AP-independent Deep Learning-based Floor Prediction

Due to the recent proliferation of location-based services indoors, the need for an accurate floor estimation technique that is easy to deploy in any typical multi-story building is higher than ever. Current approaches that attempt to solve the floor localization problem include sensor-based systems and 3D fingerprinting. Nevertheless, these systems incur high deployment and maintenance overhead, suffer from sensor drift and calibration issues, and/or are not available to all users. In this paper, we propose StoryTeller, a deep learning-based technique for floor prediction in multi-story buildings. StoryTeller leverages the ubiquitous WiFi signals to generate images that are input to a Convolutional Neural Network (CNN) which is trained to predict loors based on detected patterns in visible WiFi scans. Input images are created such that they capture the current WiFi-scan in an AP-independent manner. In addition, a novel virtual building concept is used to normalize the information in order to make them building-independent. This allows StoryTeller to reuse a trained network for a completely new building, significantly reducing the deployment overhead. We have implemented and evaluated StoryTeller using three different buildings with a side-by-side comparison with the state-of-the-art floor estimation techniques. The results show that StoryTeller can estimate the user's floor at least 98.3% within one floor of the actual ground truth floor. This accuracy is consistent across the different testbeds and for scenarios where the models used were trained in a completely different building than the tested building. This highlights StoryTeller's ability to generalize to new buildings and its promise as a scalable, low-overhead, high-accuracy floor localization system.