A&S Graduate Studies
[PAST EVENT] Ge Peng, Computer Science - Dissertation Defense
Abstract:
Smart devices are experiencing rapid development and great popularity. Various smart products available nowadays have largely enriched people?s lives. While users are enjoying their smart devices, there are two major user concerns: energy efficiency and privacy protection. In this dissertation, we propose solutions to enhance energy efficiency and privacy protection on smart devices.
First, we study different ways to handle WiFi broadcast frames during smartphone suspend mode. We reveal the dilemma of existing methods: either receive all of them suffering high power consumption, or receive none of them sacrificing functionalities. To address the dilemma, we propose Software Broadcast Filter (SBF). SBF is smarter than the ?receive-none? method as it only blocks useless broadcast frames and does not impair application functionalities. SBF is also more energy efficient than the ?receive-all? method. Our trace driven evaluation shows that SBF saves up to 49.9% energy consumption compared to the ?receive-all? method.
Second, we design a system, namely HIDE, to further reduce smartphone energy wasted on useless WiFi broadcast frames. With the HIDE system, smartphones in suspend mode do not receive useless broadcast frames or wake up to process useless broadcast frames. Our trace-driven simulation shows that the HIDE system saves 34%-75% energy for the Nexus One phone when 10% of the broadcast frames are useful to the smartphone. Our overhead analysis demonstrates that the HIDE system has negligible impact on network capacity and packet delay.
Third, to better protect user privacy, we propose a continuous and non-invasive authentication system for wearable glasses, namely GlassGuard. GlassGuard discriminates the owner and an imposter with biometric features from touch gestures and voice commands, which are all available during normal user interactions. With data collected from 32 users on Google Glass, we show that GlassGuard achieves a 99% detection rate and a 0.5% false alarm rate after 3.5 user events on average when all types of user events are available with equal probability. Under five typical usage scenarios, the system has a detection rate above 93% and a false alarm rate below 3% after less than 5 user events.
Bio:
Ge Peng has been working on her Ph.D. degree in the Department of Computer Science, William & Mary since Fall 2011. She is working with Dr. Gang Zhou in the fields of wireless networking, smartphone energy efficiency, and ubiquitous computing. Before joining William & Mary, she was a graduate student in Institute of Computing Technology, Chinese Academy of Science (CAS). Ge Peng got her B.S. in 2008 from National University of Defense Technology, China.