Discovering user context with mobile devices : location and time
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2008
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| Online Access: | http://hdl.handle.net/1721.1/41615 |
| _version_ | 1826216530861359104 |
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| author | Song, Ning, M. Eng. Massachusetts Institute of Technology |
| author2 | Larry Rudolph. |
| author_facet | Larry Rudolph. Song, Ning, M. Eng. Massachusetts Institute of Technology |
| author_sort | Song, Ning, M. Eng. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. |
| first_indexed | 2024-09-23T16:49:11Z |
| format | Thesis |
| id | mit-1721.1/41615 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:49:11Z |
| publishDate | 2008 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/416152019-04-10T08:21:11Z Discovering user context with mobile devices : location and time Song, Ning, M. Eng. Massachusetts Institute of Technology Larry Rudolph. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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. 2008-05-19T16:01:06Z 2008-05-19T16:01:06Z 2006 2006 Thesis http://hdl.handle.net/1721.1/41615 216881297 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 62 leaves application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Song, Ning, M. Eng. Massachusetts Institute of Technology Discovering user context with mobile devices : location and time |
| title | Discovering user context with mobile devices : location and time |
| title_full | Discovering user context with mobile devices : location and time |
| title_fullStr | Discovering user context with mobile devices : location and time |
| title_full_unstemmed | Discovering user context with mobile devices : location and time |
| title_short | Discovering user context with mobile devices : location and time |
| title_sort | discovering user context with mobile devices location and time |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/41615 |
| work_keys_str_mv | AT songningmengmassachusettsinstituteoftechnology discoveringusercontextwithmobiledeviceslocationandtime |