Show simple item record

dc.contributor.author
Faulkner, Matthew
dc.date.accessioned
2017-06-11T15:13:52Z
dc.date.available
2017-06-11T15:13:52Z
dc.date.issued
2014
dc.identifier.uri
http://hdl.handle.net/20.500.11850/95760
dc.description.abstract
The proliferation of smartphones and other internet-enabled, sensor-equipped consumer devices enables us to sense and act upon the physical environment in unprecedented ways. This thesis considers Community Sense-and-Response (CSR) systems, a new class of web application for acting on sensory data gathered from participants' personal smart devices. The thesis describes how rare events can be reliably detected using a decentralized anomaly detection architecture that performs client-side anomaly detection and server-side event detection. After analyzing this decentralized anomaly detection approach, the thesis describes how weak but spatially structured events can be detected, despite significant noise, when the events have a sparse representation in an alternative basis. Finally, the thesis describes how the statistical models needed for client-side anomaly detection may be learned efficiently, using limited space, via coresets. The Caltech Community Seismic Network (CSN) is a prototypical example of a CSR system that harnesses accelerometers in volunteers' smartphones and consumer electronics. Using CSN, this thesis presents the systems and algorithmic techniques to design, build and evaluate a scalable network for real-time awareness of spatial phenomena such as dangerous earthquakes.
dc.language.iso
en
dc.publisher
California Institute of Technology
dc.title
Community sense and response systems
dc.type
Doctoral Thesis
ethz.size
130 p.
ethz.publication.place
Pasadena, CA
ethz.publication.status
published
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03908 - Krause, Andreas / Krause, Andreas
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03908 - Krause, Andreas / Krause, Andreas
ethz.identifier.url
http://resolver.caltech.edu/CaltechTHESIS:04152014-111007328
ethz.date.deposited
2017-06-11T15:14:18Z
ethz.source
ECIT
ethz.identifier.importid
imp593652c4e125092350
ethz.ecitpid
pub:150137
ethz.eth
no
ethz.availability
Metadata only
ethz.rosetta.installDate
2017-07-15T04:39:55Z
ethz.rosetta.lastUpdated
2021-02-14T12:30:40Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Community%20sense%20and%20response%20systems&rft.date=2014&rft.au=Faulkner,%20Matthew&rft.genre=unknown&rft.btitle=Community%20sense%20and%20response%20systems
 Search print copy at ETH Library

Files in this item

FilesSizeFormatOpen in viewer

There are no files associated with this item.

Publication type

Show simple item record