Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
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 -