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

• URL: http://arxiv.org/abs/2107.01192v1
• Date: Fri, 2 Jul 2021 17:00:01 GMT
• Title: CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning
• Authors: Liping Wang, Saideep Tiku, Sudeep Pasricha
• Abstract summary: WiFi fingerprinting-based indoor localization is one of the most promising ways to meet this demand. We propose a compression-aware and high-accuracy deep learning framework called CHISEL that outperforms the best-known works in the area.
• Score: 4.657486836910778
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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