Graduate Exam Abstract
June 30, 2014, 11:00 AM
Dean's Conference Room (B214 Engineering)
Smart Indoor Localization Using Machine Learning Techniques
Abstract: There has been growing interest in location-based services and indoor localization is a challenging new area that is receiving a lot of attention. While numerous smartphone based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. More importantly, these techniques entail high-energy consumption that can quickly drain a smartphone battery. In this work, we propose novel techniques based on machine learning algorithms and smart sensor management for effective indoor localization using smartphones. We evaluate our techniques on several indoor environments with diverse characteristics and show improvements over several state-of-the-art techniques from prior work. We also perform energy and accuracy tradeoff analysis to provide a broader understanding of how to smartly use these techniques. Our best technique achieves an average accuracy between 1-3 meters across most of our evaluated indoor paths.
Adviser: Sudeep Pasricha
Non-ECE Member: Charles Anderson, Computer Science
Member 3: Sourajeet Roy, Electrical and Computer Engineering
Addional Members: N/A
D Jaramillo, V Ugave, R Smart, S Pasricha, "A Secure Cross-Platform Hybrid Mobile Enterprise Voice Agent", IEEE SouthEastCon, March 2014.
D Jaramillo, V Ugave, C Lu, R Alther, "Android OS in the Enterprise", Software Developers Journal, July 2013.
Program of Study: