Discovering user context with mobile devices : location and time
Author(s)
Song, Ning, M. Eng. Massachusetts Institute of Technology
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Other Contributors
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Advisor
Larry Rudolph.
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Show full item recordAbstract
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).
Date issued
2006Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.