Constella: Crowdsourced system setting recommendations for mobile devices

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The question “Where has my battery 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 reveals the combined effect of high CPU activity and automatic screen brightness to be higher (resulting in 9 min shorter battery lifetime on average) than the effect of medium CPU load and manual screen brightness; a Wi-Fi signal strength drop of one bar can shorten battery life by over 13%; and a smartphone sitting in direct sunlight can witness over 50% shorter battery life than one indoors in cool conditions. Based on the crowdsourced energy models, we develop Constella, a novel recommender system for system settings. Constella provides actionable and human-readable recommendations on how to adjust system settings in order to reduce overall battery drain. We validate the effectiveness of Constella through a hardware power measurement experiment carried out using three application case studies. The results of the evaluation demonstrate that Constella is capable of generating recommendations that can provide up to 61% improvements in battery life.
Original languageEnglish
JournalPervasive and Mobile Computing
Volume26
Pages (from-to)71-90
Number of pages20
ISSN1574-1192
DOIs
Publication statusPublished - Feb 2016
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 113 Computer and information sciences
  • Mobile sensing
  • Energy-awareness
  • Energy modeling
  • System settings

Cite this

@article{abe0d11a58fe47d4910bff4aa9298be3,
title = "Constella: Crowdsourced system setting recommendations for mobile devices",
abstract = "The question “Where has my battery 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 reveals the combined effect of high CPU activity and automatic screen brightness to be higher (resulting in 9 min shorter battery lifetime on average) than the effect of medium CPU load and manual screen brightness; a Wi-Fi signal strength drop of one bar can shorten battery life by over 13{\%}; and a smartphone sitting in direct sunlight can witness over 50{\%} shorter battery life than one indoors in cool conditions. Based on the crowdsourced energy models, we develop Constella, a novel recommender system for system settings. Constella provides actionable and human-readable recommendations on how to adjust system settings in order to reduce overall battery drain. We validate the effectiveness of Constella through a hardware power measurement experiment carried out using three application case studies. The results of the evaluation demonstrate that Constella is capable of generating recommendations that can provide up to 61{\%} improvements in battery life.",
keywords = "113 Computer and information sciences, Mobile sensing, Energy-awareness, Energy modeling, System settings",
author = "Ella Peltonen and Eemil Lagerspetz and Petteri Nurmi and Sasu Tarkoma",
year = "2016",
month = "2",
doi = "10.1016/j.pmcj.2015.10.011",
language = "English",
volume = "26",
pages = "71--90",
journal = "Pervasive and Mobile Computing",
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Constella : Crowdsourced system setting recommendations for mobile devices. / Peltonen, Ella; Lagerspetz, Eemil; Nurmi, Petteri; Tarkoma, Sasu.

In: Pervasive and Mobile Computing, Vol. 26, 02.2016, p. 71-90.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Constella

T2 - Crowdsourced system setting recommendations for mobile devices

AU - Peltonen, Ella

AU - Lagerspetz, Eemil

AU - Nurmi, Petteri

AU - Tarkoma, Sasu

PY - 2016/2

Y1 - 2016/2

N2 - The question “Where has my battery 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 reveals the combined effect of high CPU activity and automatic screen brightness to be higher (resulting in 9 min shorter battery lifetime on average) than the effect of medium CPU load and manual screen brightness; a Wi-Fi signal strength drop of one bar can shorten battery life by over 13%; and a smartphone sitting in direct sunlight can witness over 50% shorter battery life than one indoors in cool conditions. Based on the crowdsourced energy models, we develop Constella, a novel recommender system for system settings. Constella provides actionable and human-readable recommendations on how to adjust system settings in order to reduce overall battery drain. We validate the effectiveness of Constella through a hardware power measurement experiment carried out using three application case studies. The results of the evaluation demonstrate that Constella is capable of generating recommendations that can provide up to 61% improvements in battery life.

AB - The question “Where has my battery 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 reveals the combined effect of high CPU activity and automatic screen brightness to be higher (resulting in 9 min shorter battery lifetime on average) than the effect of medium CPU load and manual screen brightness; a Wi-Fi signal strength drop of one bar can shorten battery life by over 13%; and a smartphone sitting in direct sunlight can witness over 50% shorter battery life than one indoors in cool conditions. Based on the crowdsourced energy models, we develop Constella, a novel recommender system for system settings. Constella provides actionable and human-readable recommendations on how to adjust system settings in order to reduce overall battery drain. We validate the effectiveness of Constella through a hardware power measurement experiment carried out using three application case studies. The results of the evaluation demonstrate that Constella is capable of generating recommendations that can provide up to 61% improvements in battery life.

KW - 113 Computer and information sciences

KW - Mobile sensing

KW - Energy-awareness

KW - Energy modeling

KW - System settings

U2 - 10.1016/j.pmcj.2015.10.011

DO - 10.1016/j.pmcj.2015.10.011

M3 - Article

VL - 26

SP - 71

EP - 90

JO - Pervasive and Mobile Computing

JF - Pervasive and Mobile Computing

SN - 1574-1192

ER -