GPS technology has revolutionized the way we localize and navigate outdoors. However, the poor reception of GPS signals in buildings makes it unsuitable for indoor localization. WiFi fingerprinting-based indoor localization is one of the most promising ways to meet this demand. Unfortunately, most work in the domain fails to resolve challenges associated with deployability on resource-limited embedded devices. In this work, we propose a compression-aware and high-accuracy deep learning framework called CHISEL that outperforms the best-known works in the area while maintaining localization robustness on embedded devices.
@article{arxiv.2107.01192,
title = {CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning},
author = {Liping Wang and Saideep Tiku and Sudeep Pasricha},
journal= {arXiv preprint arXiv:2107.01192},
year = {2021}
}