  Indoor localization is a critical task in many embedded applications, such as
asset tracking, emergency response, and realtime navigation. In this article,
we propose a novel fingerprintingbased framework for indoor localization called
SANGRIA that uses stacked autoencoder neural networks with gradient boosted
trees. Our approach is designed to overcome the device heterogeneity challenge
that can create uncertainty in wireless signal measurements across embedded
devices used for localization. We compare SANGRIA to several state-of-the-art
frameworks and demonstrate 42.96% lower average localization error across
diverse indoor locales and heterogeneous devices.
