Energy Modeling of System Settings: A Crowdsourced Approach

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.
Original languageEnglish
Title of host publicationPervasive Computing and Communications (PerCom), 2015 IEEE International Conference
Number of pages9
PublisherIEEE
Publication date23 Mar 2015
Pages37-45
ISBN (Electronic)978-1-4799-8033-8
Publication statusPublished - 23 Mar 2015
MoE publication typeA4 Article in conference proceedings
EventIEEE International Conference on Pervasive Computing and Communications - St. Louis, MO, United States
Duration: 23 Mar 201527 Mar 2015
Conference number: 2015 PerCom

Bibliographical note

Marc Weiser Best Paper Award
Volume:
Proceeding volume:

Fields of Science

  • 113 Computer and information sciences
  • Mobile
  • Subsystems
  • Energy

Cite this

Peltonen, E. E., Lagerspetz, E., Nurmi, P. T., & Tarkoma, S. (2015). Energy Modeling of System Settings: A Crowdsourced Approach. In Pervasive Computing and Communications (PerCom), 2015 IEEE International Conference (pp. 37-45). IEEE.
Peltonen, Ella Emilia ; Lagerspetz, Eemil ; Nurmi, Petteri Tapio ; Tarkoma, Sasu. / Energy Modeling of System Settings : A Crowdsourced Approach. Pervasive Computing and Communications (PerCom), 2015 IEEE International Conference . IEEE, 2015. pp. 37-45
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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.",
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Peltonen, EE, Lagerspetz, E, Nurmi, PT & Tarkoma, S 2015, Energy Modeling of System Settings: A Crowdsourced Approach. in Pervasive Computing and Communications (PerCom), 2015 IEEE International Conference . IEEE, pp. 37-45, IEEE International Conference on Pervasive Computing and Communications, St. Louis, MO, United States, 23/03/2015.

Energy Modeling of System Settings : A Crowdsourced Approach. / Peltonen, Ella Emilia; Lagerspetz, Eemil; Nurmi, Petteri Tapio; Tarkoma, Sasu.

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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N1 - Marc Weiser Best Paper Award Volume: Proceeding volume:

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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.

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Peltonen EE, Lagerspetz E, Nurmi PT, Tarkoma S. Energy Modeling of System Settings: A Crowdsourced Approach. In Pervasive Computing and Communications (PerCom), 2015 IEEE International Conference . IEEE. 2015. p. 37-45