2026-09-19T10:45:56Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/416152022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Larry Rudolph.
Song, Ning, M. Eng. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2008-05-19T16:01:06Z
2008-05-19T16:01:06Z
2006
2006
http://hdl.handle.net/1721.1/41615
216881297
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (leaves 59-62).
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.
by Ning Song.
M.Eng.
62 leaves
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Discovering user context with mobile devices : location and time
Thesis
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