research-article

Accurate online power estimation and automatic battery behavior based power model generation for smartphones

Authors:

Birjodh Tiwana,

Zhaoguang Wang,

Robert P. Dick,

Zhuoqing Morley Mao,

Lei YangAuthors Info & Claims

CODES/ISSS '10: Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis

Pages 105 - 114

https://doi.org/10.1145/1878961.1878982

Published: 24 October 2010 Publication History

Abstract

This paper describes PowerBooter, an automated power model construction technique that uses built-in battery voltage sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components. It requires no external measurement equipment. We also describe PowerTutor, a component power management and activity state introspection based tool that uses the model generated by PowerBooter for online power estimation. PowerBooter is intended to make it quick and easy for application developers and end users to generate power models for new smartphone variants, which each have different power consumption properties and therefore require different power models. PowerTutor is intended to ease the design and selection of power efficient software for embedded systems. Combined, PowerBooter and PowerTutor have the goal of opening power modeling and analysis for more smartphone variants and their users.

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Cited By

Özkaya ÖÖrs B(2024)Model-based, fully simulated, system-level power consumption estimation of IoT devicesMicroprocessors and Microsystems10.1016/j.micpro.2024.105009105(105009)Online publication date: Mar-2024
https://doi.org/10.1016/j.micpro.2024.105009
Dorronsoro BAragón-Jurado JJareño Jde la Torre JRuiz P(2024)A Survey on Automatic Source Code Transformation for Green Software GenerationEncyclopedia of Sustainable Technologies10.1016/B978-0-323-90386-8.00122-4(765-779)Online publication date: 2024
https://doi.org/10.1016/B978-0-323-90386-8.00122-4
Hugenroth DBeresford ACalandrino JTroncoso C(2023)Powering privacyProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620541(5431-5448)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620541
Show More Cited By

Index Terms

Accurate online power estimation and automatic battery behavior based power model generation for smartphones
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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cover image ACM Conferences

CODES/ISSS '10: Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis

October 2010

348 pages

ISBN:9781605589053

DOI:10.1145/1878961

Program Chairs:
Tony Givargis
University of California, Irvine, CA
,
Adam Donlin
Xilinx, USA

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 October 2010

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Cited By

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https://doi.org/10.1016/j.micpro.2024.105009
Dorronsoro BAragón-Jurado JJareño Jde la Torre JRuiz P(2024)A Survey on Automatic Source Code Transformation for Green Software GenerationEncyclopedia of Sustainable Technologies10.1016/B978-0-323-90386-8.00122-4(765-779)Online publication date: 2024
https://doi.org/10.1016/B978-0-323-90386-8.00122-4
Hugenroth DBeresford ACalandrino JTroncoso C(2023)Powering privacyProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620541(5431-5448)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620541
Yang XArdakanian O(2023)Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningACM Transactions on Sensor Networks10.1145/362339720:1(1-32)Online publication date: 20-Oct-2023
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Chen RWan QZhang XQin XHou YWang DFu XPan MHui PAmiri Sani ANurmi PLiu Y(2023)EEFL: High-Speed Wireless Communications Inspired Energy Efficient Federated Learning over Mobile DevicesProceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services10.1145/3581791.3596865(544-556)Online publication date: 18-Jun-2023
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Smith-Creasey MSmith-Creasey M(2023)Considerations and ChallengesContinuous Biometric Authentication Systems10.1007/978-3-031-49071-2_5(105-116)Online publication date: 29-Oct-2023
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