The Resource Statistical inference in wireless sensor and mobile networks, by Peng Zhuang, (electronic resource)

Statistical inference in wireless sensor and mobile networks, by Peng Zhuang, (electronic resource)

Label
Statistical inference in wireless sensor and mobile networks
Title
Statistical inference in wireless sensor and mobile networks
Statement of responsibility
by Peng Zhuang
Creator
Contributor
Thesis advisor
Subject
Genre
Language
eng
Summary
In recent years, wireless sensor networks have emerged as a cost effective alternative to traditional wired sensor systems. In the meantime, mobile networks have also gained many momentums. The two emerging networks share many common features. Firstly, both networks consist of network nodes equipped with sensors that monitor the physical environment. Secondly, they both have short-range wireless communication. Finally, both network nodes operate on batteries, which requires power efficient programs in order to extend the length of operating time. In this dissertation, we focus on four important problems in wireless sensor and mobile networks: a) data authentication, b) faulty sensor detection, c) indoor localization and tracking, and d) prediction. We formulate them as spatial/temporal statistical inference problems and develop efficient centralized and decentralized solution methods. In the problem of data authentication, we aim at providing an energy efficient means for data authentication using spatial correlations. A centralized method is proposed and is suitable for a wide range of sensor network applications that emphasize data integrities, such as traffic monitoring and control. Compared to three competing methods, it reduces the average data error by up to 60% and reduces the security overhead by an order of one magnitude. In the problem of faulty sensor detection, we introduce a new method for detecting faulty sensor nodes without human or centralized interventions. The proposed method is based on the principles of probabilistic collective theory. The method consistently outperforms two competing methods with up to 50% higher detection accuracy. It is suitable for decentralized sensor networks operated in remote or harsh environments. In the problem of indoor localization and tracking, we propose a new method for simultaneously tracking a target and constructing an indoor logic map using smart phones. The method is designed based on temporal inference and particle filtering. Simulation results show the proposed method outperforms an existing method by approximately 9 times in tracking accuracy and constructs maps of 89% accuracy on average. It can be used for location based services like a restaurant finder and for internet map services. In the problem of prediction, we focus on the area of traffic sensor data prediction and present an analytical method to derive the spatial correlation model. We show that the analytical method acquires close estimation to the learned correlation model without the need for extensive training sensor deployment
Cataloging source
MUU
http://library.link/vocab/creatorDate
1979-
http://library.link/vocab/creatorName
Zhuang, Peng
Degree
Ph. D.
Dissertation year
2010.
Granting institution
University of Missouri--Columbia
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorDate
1967-
http://library.link/vocab/relatedWorkOrContributorName
Shang, Yi
http://library.link/vocab/subjectName
  • Wireless sensor networks
  • Wireless sensor nodes
  • Mobile communication systems
  • Mathematical statistics
  • Probabilities
Target audience
specialized
Label
Statistical inference in wireless sensor and mobile networks, by Peng Zhuang, (electronic resource)
Instantiates
Publication
Note
  • Title from PDF of title page (University of Missouri--Columbia, viewed on June 7, 2010)
  • The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file
  • Dissertation advisor: Dr. Yi Shang
  • Vita
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
656904302
Extent
1 online resource (xiv, 113 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (some color).
Specific material designation
remote
System control number
(OCoLC)656904302
Label
Statistical inference in wireless sensor and mobile networks, by Peng Zhuang, (electronic resource)
Publication
Note
  • Title from PDF of title page (University of Missouri--Columbia, viewed on June 7, 2010)
  • The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file
  • Dissertation advisor: Dr. Yi Shang
  • Vita
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
656904302
Extent
1 online resource (xiv, 113 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (some color).
Specific material designation
remote
System control number
(OCoLC)656904302

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