  One of the key technologies for future large-scale location-aware services
covering a complex of multi-story buildings --- e.g., a big shopping mall and a
university campus --- is a scalable indoor localization technique. In this
paper, we report the current status of our investigation on the use of deep
neural networks (DNNs) for scalable building/floor classification and
floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the
hierarchical nature of the building/floor estimation and floor-level
coordinates estimation of a location, we propose a new DNN architecture
consisting of a stacked autoencoder for the reduction of feature space
dimension and a feed-forward classifier for multi-label classification of
building/floor/location, on which the multi-building and multi-floor indoor
localization system based on Wi-Fi fingerprinting is built. Experimental
results for the performance of building/floor estimation and floor-level
coordinates estimation of a given location demonstrate the feasibility of the
proposed DNN-based indoor localization system, which can provide near
state-of-the-art performance using a single DNN, for the implementation with
lower complexity and energy consumption at mobile devices.
