Modeling Smartphone Power

A Master of Science thesis in Computer Engineering by Sameer Allan Alawnah entitled, "Modeling Smartphone Power," submitted in June 2013. Thesis advisor is Dr. Assim Sagahyroon. Available are both soft and hard copies of the thesis.

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Main Author: Alawnah, Sameer Allan (author)
Format: doctoralThesis
Published: 2013
Subjects:
Online Access:http://hdl.handle.net/11073/5893
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author Alawnah, Sameer Allan
author_facet Alawnah, Sameer Allan
author_role author
dc.contributor.none.fl_str_mv Sagahyroon, Assim
dc.creator.none.fl_str_mv Alawnah, Sameer Allan
dc.date.none.fl_str_mv 2013-09-11T05:52:23Z
2013-09-11T05:52:23Z
2013-06
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.identifier.none.fl_str_mv 35.232-2013.20
http://hdl.handle.net/11073/5893
dc.language.none.fl_str_mv en_US
dc.subject.none.fl_str_mv power modeling
regression
neural networks
android
Smartphones
Cell phones
Batteries
Cell phones
Design and construction
dc.title.none.fl_str_mv Modeling Smartphone Power
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/doctoralThesis
description A Master of Science thesis in Computer Engineering by Sameer Allan Alawnah entitled, "Modeling Smartphone Power," submitted in June 2013. Thesis advisor is Dr. Assim Sagahyroon. Available are both soft and hard copies of the thesis.
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id aus_e6e60b92e940cb9ca9df0e94e3ca9573
identifier_str_mv 35.232-2013.20
language_invalid_str_mv en_US
network_acronym_str aus
network_name_str aus
oai_identifier_str oai:repository.aus.edu:11073/5893
publishDate 2013
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
spelling Modeling Smartphone PowerAlawnah, Sameer Allanpower modelingregressionneural networksandroidSmartphonesCell phonesBatteriesCell phonesDesign and constructionA Master of Science thesis in Computer Engineering by Sameer Allan Alawnah entitled, "Modeling Smartphone Power," submitted in June 2013. Thesis advisor is Dr. Assim Sagahyroon. Available are both soft and hard copies of the thesis.Battery technology has not advanced rapidly enough to keep pace with the growing energy demands of today's portable electronics. Leading this critical need for energy are smartphone devices which are being deployed and adopted at an increasing rate. Developing sound energy management techniques for these devices requires a good understanding of where and how battery energy is being utilized. Power consumption modeling is therefore crucial for understanding the inner working of these devices and for developing energy-efficient software to run on them. In this work, we attempt to develop power models for Android-based smartphones. A logger application is developed to monitor users' activity and collect data related to this activity. We then utilize regression techniques and neural networks to develop power models that relate power consumption to usage behavior. We demonstrate the feasibility of applying the two approaches and provide a detailed comparison between them.College of EngineeringDepartment of Computer Science and EngineeringMaster of Science in Computer Engineering (MSCoE)Sagahyroon, Assim2013-09-11T05:52:23Z2013-09-11T05:52:23Z2013-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfapplication/pdf35.232-2013.20http://hdl.handle.net/11073/5893en_USoai:repository.aus.edu:11073/58932025-06-26T12:33:10Z
spellingShingle Modeling Smartphone Power
Alawnah, Sameer Allan
power modeling
regression
neural networks
android
Smartphones
Cell phones
Batteries
Cell phones
Design and construction
status_str publishedVersion
title Modeling Smartphone Power
title_full Modeling Smartphone Power
title_fullStr Modeling Smartphone Power
title_full_unstemmed Modeling Smartphone Power
title_short Modeling Smartphone Power
title_sort Modeling Smartphone Power
topic power modeling
regression
neural networks
android
Smartphones
Cell phones
Batteries
Cell phones
Design and construction
url http://hdl.handle.net/11073/5893