Massachusetts Institute of Technology
Learning significant user locations with GPS and GSM
Abstract
dc:description.abstractThis thesis addresses the tasks of place discovery and place recognition - learning and recognizing places significant to a user - by analyzing GPS location and GSM cell tower data collected from the user's mobile phone. Location provides valuable context into the user's environment, and place-discovery and recognition algorithms enable human-centric systems to communicate with the user in human terms. In this thesis, we introduce a novel two-phased approach to place-discovery and recognition that combines the advantages of GPS and GSM cell data. We design and implement a system that produces a compact travel summary from the user's daily GPS logs. We then use computational geometry to investigate the aspect ratios of GSM cell coverage polygons as an optimization to place recognition. Finally, we conclude by presenting a one-month empirical study to demonstrate the effectiveness of our two-phased approach, and identify a set of anomalies in our experiment that can direct further development of place-discovery systems.
Degree
thesis:*- Department dc:contributor.department
- Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
- Grantor dc:publisher
- Massachusetts Institute of Technology
- Year dc:date.issued
- 2006
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Yu, Xiao, M. Eng. Massachusetts Institute of Technology
- Advisor dc:contributor.advisor
-
- Larry Rudolph.
Subjects
dc:subject × 1Rights
dc:rights- Statement dc:rights
-
- 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.
- Licence dc:rights.uri
- Language dc:language.iso
- eng
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- http://hdl.handle.net/1721.1/42121
- OAI identifier oai:identifier
- oai:dspace.mit.edu:1721.1/42121