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Energy modeling of system settings: A crowdsourced approach

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Energy modeling of system settings : A crowdsourced approach. / Peltonen, E.; Lagerspetz, E.; Nurmi, P.; Tarkoma, S.

2015 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 2015. p. 37-45.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Peltonen, E, Lagerspetz, E, Nurmi, P & Tarkoma, S 2015, Energy modeling of system settings: A crowdsourced approach. in 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, pp. 37-45. https://doi.org/10.1109/PERCOM.2015.7146507

APA

Peltonen, E., Lagerspetz, E., Nurmi, P., & Tarkoma, S. (2015). Energy modeling of system settings: A crowdsourced approach. In 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom) (pp. 37-45). IEEE. https://doi.org/10.1109/PERCOM.2015.7146507

Vancouver

Peltonen E, Lagerspetz E, Nurmi P, Tarkoma S. Energy modeling of system settings: A crowdsourced approach. In 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE. 2015. p. 37-45 https://doi.org/10.1109/PERCOM.2015.7146507

Author

Peltonen, E. ; Lagerspetz, E. ; Nurmi, P. ; Tarkoma, S. / Energy modeling of system settings : A crowdsourced approach. 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 2015. pp. 37-45

Bibtex

@inproceedings{b11bf258de864a3b977295adf2a3bcf2,
title = "Energy modeling of system settings: A crowdsourced approach",
abstract = "The question “Where has my battery life gone?” remains a common source of frustration for many smartphone users. With the increased complexity of smartphone applications, and the increasing number of system settings affecting them, understanding and optimizing battery use has become a difficult chore. The present paper develops a novel approach for constructing energy models from crowdsourced measurements. In contrast to previous approaches, which have focused on the effect of a specific sensor, system setting or application, our approach can simultaneously capture relationships between multiple factors, and provide a unified view of the energy state of the mobile device. We demonstrate the validity of using crowdsourced measurements for constructing battery models through a combination of large-scale analysis of a dataset containing battery discharge and system state measurements and hardware power measurements. The results indicate that the models captured by our approach are both in line with previous studies on battery consumption and empirical measurements, providing a cost-effective way to construct energy models during normal operations of the device. The analysis also provides several new insights about battery consumption. For example, our analysis shows the energy use of high CPU activity with automatic screen brightness is actually higher (resulting in around 9 minutes shorter battery lifetime on average) than with a medium CPU load and manual screen brightness; a Wi-Fi signal strength drop of one bar can result in a battery life loss of over 13%; and a smartphone sitting in the sun can experience over 50% worse battery life than one indoors in cool conditions.",
keywords = "mobile computing, power aware computing, smart phones, battery consumption, battery discharge, battery models, crowdsourced approach, crowdsourced measurements, energy modeling, mobile device, multiple factors, optimizing battery, smartphone applications, smartphone users, Batteries, Battery charge measurement, Brightness, Context, Discharges (electric), IEEE 802.11 Standards, Mobile communication, Energy, Mobile, Subsystems",
author = "E. Peltonen and E. Lagerspetz and P. Nurmi and S. Tarkoma",
year = "2015",
month = mar,
day = "23",
doi = "10.1109/PERCOM.2015.7146507",
language = "English",
isbn = "9781479980345",
pages = "37--45",
booktitle = "2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Energy modeling of system settings

T2 - A crowdsourced approach

AU - Peltonen, E.

AU - Lagerspetz, E.

AU - Nurmi, P.

AU - Tarkoma, S.

PY - 2015/3/23

Y1 - 2015/3/23

N2 - The question “Where has my battery life gone?” remains a common source of frustration for many smartphone users. With the increased complexity of smartphone applications, and the increasing number of system settings affecting them, understanding and optimizing battery use has become a difficult chore. The present paper develops a novel approach for constructing energy models from crowdsourced measurements. In contrast to previous approaches, which have focused on the effect of a specific sensor, system setting or application, our approach can simultaneously capture relationships between multiple factors, and provide a unified view of the energy state of the mobile device. We demonstrate the validity of using crowdsourced measurements for constructing battery models through a combination of large-scale analysis of a dataset containing battery discharge and system state measurements and hardware power measurements. The results indicate that the models captured by our approach are both in line with previous studies on battery consumption and empirical measurements, providing a cost-effective way to construct energy models during normal operations of the device. The analysis also provides several new insights about battery consumption. For example, our analysis shows the energy use of high CPU activity with automatic screen brightness is actually higher (resulting in around 9 minutes shorter battery lifetime on average) than with a medium CPU load and manual screen brightness; a Wi-Fi signal strength drop of one bar can result in a battery life loss of over 13%; and a smartphone sitting in the sun can experience over 50% worse battery life than one indoors in cool conditions.

AB - The question “Where has my battery life gone?” remains a common source of frustration for many smartphone users. With the increased complexity of smartphone applications, and the increasing number of system settings affecting them, understanding and optimizing battery use has become a difficult chore. The present paper develops a novel approach for constructing energy models from crowdsourced measurements. In contrast to previous approaches, which have focused on the effect of a specific sensor, system setting or application, our approach can simultaneously capture relationships between multiple factors, and provide a unified view of the energy state of the mobile device. We demonstrate the validity of using crowdsourced measurements for constructing battery models through a combination of large-scale analysis of a dataset containing battery discharge and system state measurements and hardware power measurements. The results indicate that the models captured by our approach are both in line with previous studies on battery consumption and empirical measurements, providing a cost-effective way to construct energy models during normal operations of the device. The analysis also provides several new insights about battery consumption. For example, our analysis shows the energy use of high CPU activity with automatic screen brightness is actually higher (resulting in around 9 minutes shorter battery lifetime on average) than with a medium CPU load and manual screen brightness; a Wi-Fi signal strength drop of one bar can result in a battery life loss of over 13%; and a smartphone sitting in the sun can experience over 50% worse battery life than one indoors in cool conditions.

KW - mobile computing

KW - power aware computing

KW - smart phones

KW - battery consumption

KW - battery discharge

KW - battery models

KW - crowdsourced approach

KW - crowdsourced measurements

KW - energy modeling

KW - mobile device

KW - multiple factors

KW - optimizing battery

KW - smartphone applications

KW - smartphone users

KW - Batteries

KW - Battery charge measurement

KW - Brightness

KW - Context

KW - Discharges (electric)

KW - IEEE 802.11 Standards

KW - Mobile communication

KW - Energy

KW - Mobile

KW - Subsystems

U2 - 10.1109/PERCOM.2015.7146507

DO - 10.1109/PERCOM.2015.7146507

M3 - Conference contribution/Paper

SN - 9781479980345

SP - 37

EP - 45

BT - 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)

PB - IEEE

ER -