Computer Science Thesis Oral

Thursday, May 5, 2016 -
10:00am to 11:30am


McWilliams Classroom 4303 Gates & Hillman Centers



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We investigate the challenges of collecting sensor measurements with mobile platforms to reveal spatio-temporal insights across our surrounding environments and enable novel data-driven algorithms.  Our efforts aim to take advantage of the increasing ubiquity of mobile platforms are already equipped with sensors. Today, sensor measurements are primarily used in limited settings with coarse-grain accuracy. As device localization continues to improve, the goal of this thesis is to investigate developments towards fine-grain sensor data with centimeter-level accuracy. In particular, we show that fine-grain sensor maps enable novel data-driven algorithms that leverage awareness of surrounding conditions.  This thesis seeks to addresses the key aspects of collecting, analyzing, and using fine-grain sensor measurements. Mobile platforms have a distinct advantage over existing data collection efforts due  to  recent  developments  in  localization  and  navigation  that  allow  a  device  to capture labeled sensor data.  Benefits include lower costs due to reduced dedicated sensor hardware requirements, reduced human effort due to automatic localization, and increased measurement diversity from moving around. In this thesis, our contributions focus specifically on wireless and indoor climate sensors, where we contend with different measurements characteristics. We demonstrate the value of these efforts with several concrete contributions. First,  we  use  mobile  robots  to  gather  sensor measurements  over  several  months across two enterprise environments.  Through our analysis efforts, we reveal fine-grain spatio-temporal insights that show wireless conditions and indoor climate variations. We also show that data-driven wireless handoffs are able to significantly improve wireless performance for our robot that originally faced intermittent wireless connectivity issues while moving. With the increasing pervasiveness and technological developments of mobile devices, this thesis investigates the opportunity to leverage their sensor hardware to better integrate computing technology with our surrounding environment. Thesis Committee: Srinivasan Seshan (Co-Chair) Manuela Veloso (Co-Chair) Peter Steenkiste Daniel Lee (University of Pennsylvania)


Thesis Oral