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Direct inference of location-related context from wireless signal strength
Descriptive
TitleInfo
Title
Direct inference of location-related context from wireless signal strength
Name
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Chandrasekaran
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Gayathri
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Gayathri Chandrasekaran
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author
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Martin
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Richard P
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Richard P Martin
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Advisory Committee
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chair
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Marco
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Marco Gruteser
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Advisory Committee
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co-chair
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Wright
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Rebecca
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Rebecca Wright
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Advisory Committee
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internal member
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Ganapathy
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Vinod
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Vinod Ganapathy
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Advisory Committee
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internal member
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Caceres
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Ramon
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Ramon Caceres
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Advisory Committee
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outside member
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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school
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Text
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theses
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DateCreated
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2011
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2011-05
Language
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eng
Subject
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Topic
Computer Science
Subject
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Topic
Wireless communication systems
Subject
(authority = ETD-LCSH)
Topic
Location-based services
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
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rucore19991600001
Note
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Ph.D.
Note
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Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Gayathri Chandrasekaran
PhysicalDescription
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electronic resource
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Extent
xii, 94 p. : ill.
Abstract
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In this dissertation we derive location-related context likemobility-states, co-mobility, speed and decelerations directly from the wireless signal strength information. The key insight is that the time-series of signal strength is robust to environmental factors that typically negatively affect the RSS-based localization systems. Therefore, inferring these physical properties directly from the time-series of wireless signal strength is more accurate than deriving them from location estimates. We apply correlation and time warping algorithms to the time series of wireless signals
to infer these properties. Our trace-driven experimental approach shows that our inference techniques can work with minimal infrastructure, are computationally efficient, requires no explicit user participation and can produce higher accuracies than location-based systems. We have also experimentally identified the factors that limit the accuracy of indoor localization and
have proved the existing assumptions behind theoretical lower bounds of indoor localization
incorrect. Our results will enable new context aware applications, because accurate estimates of comobility
and speed offer a richer set of primitives available to applications. Such applications can derive user mobility states like walking, running, driving or social states, such as if a user is in a meeting or alone.
to infer these properties. Our trace-driven experimental approach shows that our inference techniques can work with minimal infrastructure, are computationally efficient, requires no explicit user participation and can produce higher accuracies than location-based systems. We have also experimentally identified the factors that limit the accuracy of indoor localization and
have proved the existing assumptions behind theoretical lower bounds of indoor localization
incorrect. Our results will enable new context aware applications, because accurate estimates of comobility
and speed offer a richer set of primitives available to applications. Such applications can derive user mobility states like walking, running, driving or social states, such as if a user is in a meeting or alone.
Identifier
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http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061167
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Identifier
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doi:10.7282/T38C9VKR
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Chandrasekaran
GivenName
Gayathri
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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(point = start)
2011-04-15 10:23:52
AssociatedEntity
Name
Gayathri Chandrasekaran
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
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License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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