CellStory: Extendable Cellular Signals-Based Floor Estimator Using Deep Learning

Abstract

As the demand for location-based services increases, several research efforts have aimed for robust and accurate indoor localization, especially 3D localization. Due to the widespread availability of cellular networks and their support by commodity cellphones, cellular-based systems have recently been proposed as a means of achieving this. However, because of the inherent noise and instability of wireless signals, localization accuracy typically degrades and is not robust to the dynamic heterogeneity of mobile devices.In this paper, we present a CellStory, a deep learning-based floor estimation system that achieves a fine-grained and robust accuracy in the presence of noise. CellStory combines stacked denoising autoencoder learning models, and a probabilistic framework to handle noise in the received signal and capture the complex relationship between the signals detected by the mobile phone and its location. Evaluation using different Android phones in a real testbed shows that CellStory can accurately estimate the user’s floor 98.7% of the time and within one floor error 100% of the time. This accuracy demonstrates CellStory’s superiority over state-of-the-art systems as well as its robustness to heterogeneous devices.