SANGRIA: Stacked Autoencoder Neural Networks with Gradient Boosting for Indoor Localization
This addresses the problem of accurate indoor localization for applications like asset tracking and emergency response, though it appears incremental as it builds on existing fingerprinting and neural network techniques.
The paper tackled indoor localization under device heterogeneity by proposing SANGRIA, a fingerprinting-based framework using stacked autoencoder neural networks with gradient boosted trees, achieving a 42.96% lower average localization error compared to state-of-the-art methods.
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.