RUcore: Rutgers University Community Repository
Improving the speed and accuracy of indoor localization
Descriptive
TypeOfResource
Text
TitleInfo
(ID = T-1)
Title
Improving the speed and accuracy of indoor localization
SubTitle
PartName
PartNumber
NonSort
Identifier
ETD_1359
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051023
Language
(objectPart = )
LanguageTerm
(authority = ISO639-2);
(type = code)
eng
Genre
(authority = marcgt)
theses
Subject
(ID = SBJ-1);
(authority = RUETD)
Topic
Computer Science
Subject
(ID = SBJ-2);
(authority = ETD-LCSH)
Topic
Wireless communication systems
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
Wireless sensor networks
Abstract
Advances in technology have enabled a large number of computing devices to communicate wirelessly. In addition, radio waves, which are the primary means of transmitting data in wireless communication, can be used to localize devices in the 2D and 3D space. As a result there has been an increasing number of applications that rely on the availability of device location. Many systems have been developed to provide location estimates indoors, where Global Positioning System (GPS) devices do not work. However, localization indoors faces many challenges. First, a localization system should use as little extra hardware as possible, should work on any wireless device with very little or no modification, and localization latency should be small. Also, wireless signals indoors suffer from environmental effects like reflection, diffraction and scattering, making signal characterization with respect to location difficult.
Moreover, many algorithms require detailed profiling of the environment, making the systems hard to deploy.
This thesis addresses some of the aforementioned issues for localization systems that rely on radio properties like Received Signal Strength (RSS).
The advantage of these systems is that they reuse the existing communication infrastructure, rather than necessitating the deployment of specialized hardware. Specifically, we improved the latency of a particular localization method that relies on Bayesian Networks (BNs). This method has the advantage of requiring a small size of training data, can localize many devices simultaneously, and some versions of BNs can localize without requiring the knowledge of the locations where signal strength properties are collected.
We proposed Markov Chain Monte Carlo (MCMC) algorithms and evaluated their performance by introducing a metric which we call relative accuracy.
We reduced latency by identifying MCMC methods that improve the relative accuracy to solutions returned by existing statistical packages in as little time as possible. In addition, we parallelized the MCMC process to improve latency when localizing devices whose number is on the order of hundreds. Finally, since wireless transmission is heavily affected by the physical environment indoors, we investigated the impact of using multiple antennas on the performance of various localization algorithms. We showed that deploying low-cost antennas at fixed locations can improve the accuracy and stability of localization algorithms indoors.
Moreover, many algorithms require detailed profiling of the environment, making the systems hard to deploy.
This thesis addresses some of the aforementioned issues for localization systems that rely on radio properties like Received Signal Strength (RSS).
The advantage of these systems is that they reuse the existing communication infrastructure, rather than necessitating the deployment of specialized hardware. Specifically, we improved the latency of a particular localization method that relies on Bayesian Networks (BNs). This method has the advantage of requiring a small size of training data, can localize many devices simultaneously, and some versions of BNs can localize without requiring the knowledge of the locations where signal strength properties are collected.
We proposed Markov Chain Monte Carlo (MCMC) algorithms and evaluated their performance by introducing a metric which we call relative accuracy.
We reduced latency by identifying MCMC methods that improve the relative accuracy to solutions returned by existing statistical packages in as little time as possible. In addition, we parallelized the MCMC process to improve latency when localizing devices whose number is on the order of hundreds. Finally, since wireless transmission is heavily affected by the physical environment indoors, we investigated the impact of using multiple antennas on the performance of various localization algorithms. We showed that deploying low-cost antennas at fixed locations can improve the accuracy and stability of localization algorithms indoors.
PhysicalDescription
Form
(authority = gmd)
electronic resource
Extent
xiii, 107 p. : ill.
InternetMediaType
application/pdf
InternetMediaType
text/xml
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references (p. 103-106)
Note
(type = statement of responsibility)
by Konstantinos Kleisouris
Name
(ID = NAME-1);
(type = personal)
NamePart
(type = family)
Kleisouris
NamePart
(type = given)
Konstantinos
Role
RoleTerm
(authority = RULIB);
(type = )
author
DisplayForm
Konstantinos Kleisouris
Name
(ID = NAME-2);
(type = personal)
NamePart
(type = family)
Martin
NamePart
(type = given)
Richard
Role
RoleTerm
(authority = RULIB);
(type = )
chair
Affiliation
Advisory Committee
DisplayForm
Richard Martin
Name
(ID = NAME-3);
(type = personal)
NamePart
(type = family)
Littman
NamePart
(type = given)
Michael
Role
RoleTerm
(authority = RULIB);
(type = )
internal member
Affiliation
Advisory Committee
DisplayForm
Michael Littman
Name
(ID = NAME-4);
(type = personal)
NamePart
(type = family)
Elgammal
NamePart
(type = given)
Ahmed
Role
RoleTerm
(authority = RULIB);
(type = )
internal member
Affiliation
Advisory Committee
DisplayForm
Ahmed Elgammal
Name
(ID = NAME-5);
(type = personal)
NamePart
(type = family)
Vannucci
NamePart
(type = given)
Giovanni
Role
RoleTerm
(authority = RULIB);
(type = )
outside member
Affiliation
Advisory Committee
DisplayForm
Giovanni Vannucci
Name
(ID = NAME-1);
(type = corporate)
NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB);
(type = )
degree grantor
Name
(ID = NAME-2);
(type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm
(authority = RULIB);
(type = )
school
OriginInfo
DateCreated
(point = );
(qualifier = exact)
2009
DateOther
(qualifier = exact);
(type = degree)
2009-01
Location
PhysicalLocation
(authority = marcorg)
NjNbRU
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
(type = local)
rucore19991600001
Identifier
(type = doi)
doi:10.7282/T3TD9XMB
Genre
(authority = ExL-Esploro)
ETD doctoral
Back to the top
Rights
RightsDeclaration
(AUTHORITY = GS);
(ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
RightsEvent
(AUTHORITY = rulib);
(ID = 1)
Type
Permission or license
Detail
Non-exclusive ETD license
AssociatedObject
(AUTHORITY = rulib);
(ID = 1)
Type
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.
Back to the top
Technical
ContentModel
ETD
MimeType
(TYPE = file)
application/pdf
MimeType
(TYPE = container)
application/x-tar
FileSize
(UNIT = bytes)
1986560
Checksum
(METHOD = SHA1)
39333f2f9cbf7bd40b3d3a3e29485260c92c5f61
Back to the top
Statistical Profile