  One of key technologies for future large-scale location-aware services in
access is a scalable indoor localization technique. In this paper, we report
preliminary results from our investigation on the use of deep neural networks
(DNNs) for hierarchical building/floor classification and floor-level location
estimation based on Wi-Fi fingerprinting, which we carried out as part of a
feasibility study project on Xi'an Jiaotong-Liverpool University (XJTLU) Campus
Information and Visitor Service System. To take into account the hierarchical
nature of the building/floor classification problem, we propose a new DNN
architecture based on a stacked autoencoder for the reduction of feature space
dimension and a feed-forward classifier for multi-label classification with
argmax functions to convert multi-label classification results into multi-class
classification ones. We also describe the demonstration of a prototype
DNN-based indoor localization system for floor-level location estimation using
real received signal strength (RSS) data collected at one of the buildings on
the XJTLU campus. The preliminary results for both building/floor
classification and floor-level location estimation clearly show the strengths
of DNN-based approaches, which can provide near state-of-the-art performance
with less parameter tuning and higher scalability.
