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dc.contributor.advisorPasricha, Sudeep
dc.contributor.authorDonohoo, Brad Kyoshi
dc.contributor.committeememberAnderson, Charles
dc.contributor.committeememberJayasumana, Anura P.
dc.date.accessioned2007-01-03T08:10:38Z
dc.date.available2007-01-03T08:10:38Z
dc.date.issued2012
dc.description2012 Summer.
dc.descriptionIncludes bibliographical references.
dc.description.abstractMobile smartphones and other portable battery operated embedded systems (PDAs, tablets) are pervasive computing devices that have emerged in recent years as essential instruments for communication, business, and social interactions. While performance, capabilities, and design are all important considerations when purchasing a mobile device, a long battery lifetime is one of the most desirable attributes. Battery technology and capacity has improved over the years, but it still cannot keep pace with the power consumption demands of today's mobile devices. This key limiter has led to a strong research emphasis on extending battery lifetime by minimizing energy consumption, primarily using software optimizations. This thesis presents two strategies that attempt to optimize mobile device energy consumption with negligible impact on user perception and quality of service (QoS). The first strategy proposes an application and user interaction aware middleware framework that takes advantage of user idle time between interaction events of the foreground application to optimize CPU and screen backlight energy consumption. The framework dynamically classifies mobile device applications based on their received interaction patterns, then invokes a number of different power management algorithms to adjust processor frequency and screen backlight levels accordingly. The second strategy proposes the usage of machine learning techniques to learn a user's mobile device usage pattern pertaining to spatiotemporal and device contexts, and then predict energy-optimal data and location interface configurations. By learning where and when a mobile device user uses certain power-hungry interfaces (3G, WiFi, and GPS), the techniques, which include variants of linear discriminant analysis, linear logistic regression, non-linear logistic regression, and k-nearest neighbor, are able to dynamically turn off unnecessary interfaces at runtime in order to save energy.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierDonohoo_colostate_0053N_11114.pdf
dc.identifierETDF2012500154ECEN
dc.identifier.urihttp://hdl.handle.net/10217/68004
dc.languageEnglish
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019 - CSU Theses and Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectenergy optimization
dc.subjectsmartphones
dc.subjectmachine learning
dc.subjecthuman factors
dc.titleMachine learning techniques for energy optimization in mobile embedded systems
dc.typeText
dcterms.rights.dplaThe copyright and related rights status of this Item has not been evaluated (https://rightsstatements.org/vocab/CNE/1.0/). Please refer to the organization that has made the Item available for more information.
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)


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