  There has been an increasing tendency to move from outdoor to indoor
lifestyle in modern cities. The emergence of big shopping malls, indoor sports
complexes, factories, and warehouses is accelerating this tendency. In such an
environment, indoor localization becomes one of the essential services, and the
indoor localization systems to be deployed should be scalable enough to cover
the expected expansion of those indoor facilities. One of the most economical
and practical approaches to indoor localization is Wi-Fi fingerprinting, which
exploits the widely-deployed Wi-Fi networks using mobile devices (e.g.,
smartphones) without any modification of the existing infrastructure.
Traditional Wi-Fi fingerprinting schemes rely on complicated data
pre/post-processing and time-consuming manual parameter tuning. In this paper,
we propose hierarchical multi-building and multi-floor indoor localization
based on a recurrent neural network (RNN) using Wi-Fi fingerprinting,
eliminating the need of complicated data pre/post-processing and with less
parameter tuning. The RNN in the proposed scheme estimates locations in a
sequential manner from a general to a specific one (e.g.,
building->floor->location) in order to exploit the hierarchical nature of the
localization in multi-building and multi-floor environments. The experimental
results with the UJIIndoorLoc dataset demonstrate that the proposed scheme
estimates building and floor with 100% and 95.24% accuracy, respectively, and
provides three-dimensional positioning error of 8.62 m, which outperforms
existing deep neural network-based schemes.
