LearnLoc: A framework for smart indoor localization with embedded mobile devices

Abstract

There has been growing interest in location-based services and indoor localization in recent years. While several smartphone based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. These prior efforts also ignore energy consumption analysis which is a crucial quality metric in resource-constrained smartphones. In this work, we propose novel techniques based on machine learning algorithms and smart sensor management for real-time indoor localization using smartphones. We implement our proposed techniques as well as state-of-the-art techniques on real smartphones and evaluate their tracking effectiveness and energy overheads across several diverse real-world indoor environments. Our best technique improves upon prior work, achieving indoor localization accuracy between 1-3 meters.