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Sensor networks for geospatial event detection - theory and applications

Citation

Liu, Annie Hsin-Wen (2013) Sensor networks for geospatial event detection - theory and applications. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:06062013-224746692

Abstract

This thesis presents theories, analyses, and algorithms for detecting and estimating parameters of geospatial events with today's large, noisy sensor networks. A geospatial event is initiated by a significant change in the state of points in a region in a 3-D space over an interval of time. After the event is initiated it may change the state of points over larger regions and longer periods of time. Networked sensing is a typical approach for geospatial event detection. In contrast to traditional sensor networks comprised of a small number of high quality (and expensive) sensors, trends in personal computing devices and consumer electronics have made it possible to build large, dense networks at a low cost. The changes in sensor capability, network composition, and system constraints call for new models and algorithms suited to the opportunities and challenges of the new generation of sensor networks. This thesis offers a single unifying model and a Bayesian framework for analyzing different types of geospatial events in such noisy sensor networks. It presents algorithms and theories for estimating the speed and accuracy of detecting geospatial events as a function of parameters from both the underlying geospatial system and the sensor network. Furthermore, the thesis addresses network scalability issues by presenting rigorous scalable algorithms for data aggregation for detection. These studies provide insights to the design of networked sensing systems for detecting geospatial events. In addition to providing an overarching framework, this thesis presents theories and experimental results for two very different geospatial problems: detecting earthquakes and hazardous radiation. The general framework is applied to these specific problems, and predictions based on the theories are validated against measurements of systems in the laboratory and in the field.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:sensor network; geospatial event; event detection; Bayesian statistics; sparse optimization
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Computer Science
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Chandy, K. Mani
Thesis Committee:
  • Chandy, K. Mani (chair)
  • Clayton, Robert W.
  • Wierman, Adam C.
  • Beck, James L.
  • Murray, Richard M.
Defense Date:21 May 2013
Funders:
Funding AgencyGrant Number
NSF Graduate Research FellowshipUNSPECIFIED
NSF East Asia and Pacific Summer Institute FellowshipUNSPECIFIED
Record Number:CaltechTHESIS:06062013-224746692
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:06062013-224746692
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:7856
Collection:CaltechTHESIS
Deposited By: Annie Liu
Deposited On:12 Jun 2013 18:16
Last Modified:12 Jun 2013 18:16

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