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Organic Indoor Location Discovery

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dc.contributor.advisor Seth Teller
dc.contributor.author Hicks, Jamey en_US
dc.contributor.author Curtis, Dorothy en_US
dc.contributor.author Teller, Seth en_US
dc.contributor.author Charrow, Ben en_US
dc.contributor.author Ryan, Russell en_US
dc.contributor.author Ledlie, Jonathan en_US
dc.contributor.author Battat, Jonathan en_US
dc.contributor.other Robotics, Vision & Sensor Networks en_US
dc.date.accessioned 2008-12-30T21:45:11Z
dc.date.available 2008-12-30T21:45:11Z
dc.date.issued 2008-12-30
dc.identifier.uri http://hdl.handle.net/1721.1/43951
dc.description.abstract We describe an indoor, room-level location discovery method based on spatial variations in "wifi signatures," i.e., MAC addresses and signal strengths of existing wireless access points. The principal novelty of our system is its organic nature; it builds signal strength maps from the natural mobility and lightweight contributions of ordinary users, rather than dedicated effort by a team of site surveyors. Whenever a user's personal device observes an unrecognized signature, a GUI solicits the user's location. The resulting location-tagged signature or "bind" is then shared with other clients through a common database, enabling devices subsequently arriving there to discover location with no further user contribution. Realizing a working system deployment required three novel elements: (1) a human-computer interface for indicating location over intervals of varying duration; (2) a client-server protocol for pre-fetching signature data for use in localization; and (3) a location-estimation algorithm incorporating highly variable signature data. We describe an experimental deployment of our method in a nine-story building with more than 1,400 distinct spaces served by more than 200 wireless access points. At the conclusion of the deployment, users could correctly localize to within 10 meters 92 percent of the time. en_US
dc.format.extent 14 p. en_US
dc.relation.ispartofseries MIT-CSAIL-TR-2008-075
dc.subject Shared Sensing en_US
dc.subject Computer Communication Networks en_US
dc.subject Localization en_US
dc.subject Location-Based Services en_US
dc.subject Geo-Tagging en_US
dc.subject Collaborative Computing en_US
dc.subject Distributed Systems en_US
dc.subject Distributed Applications en_US
dc.subject Pervasive Computing en_US
dc.subject Algorithms en_US
dc.subject Experiments en_US
dc.subject Groups and Organization Interfaces en_US
dc.subject Measurement en_US
dc.subject Crowd-Sourcing en_US
dc.subject Information Interfaces and Presentation en_US
dc.subject Human Factors en_US
dc.title Organic Indoor Location Discovery en_US


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