Discovering user context with mobile devices : location and time
Name
216881297-MIT.pdf
Description
Full printable version
Size
4.28 MB
Format
Adobe PDF
Checksum (MD5)
7287c94e957a0f65431c7453c6771f1f
Author(s)
Song, Ning, M. Eng. Massachusetts Institute of Technology
Advisor(s)
Larry Rudolph.
Date Issued
2006
Publisher
Massachusetts Institute of Technology
Abstract
Life for many people is based on a set of daily routines, such as home, work, and leisure. If the activities in life occur in recurring patterns, then the context in which they occur should also follow a pattern. In this thesis, we explore using cell phones for learning recurring locations using only a timestamped history of the cell tower the device is connected to. We base our approach on an existing graph-based online algorithm, but modify it to compute additional statistics for offline analysis to obtain better results. We then further refine the offline algorithm to include time-partitioned nodes to resolve some observed shortcomings. Finally, we evaluate all three algorithms on a dataset of GSM readings over a one month period, and show how our successive modifications improved the locations found.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (leaves 59-62).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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reproduction or distribution in any format is prohibited without written
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