CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization With Deep Learning

GPS technology has revolutionized the way we localize and navigate outdoors. However, poor reception of signals in buildings makes it unsuitable for indoor localization. WiFi fingerprinting-based localization is one most promising ways to meet this demand. Unfortunately, work domain fails resolve challenges associated with deployability on resource-limited embedded devices. In work, propose a compression-aware high-accuracy deep learning framework called CHISEL that outperforms best-known works area while maintaining robustness