Accurate prediction of available battery time for mobile applications

Dongwon Kim; Yohan Chon; Wonwoo Jung; Yungeun Kim; Hojung Cha

doi:10.1145/2875423

Accurate prediction of available battery time for mobile applications

Dongwon Kim, Yohan Chon, Wonwoo Jung, Yungeun Kim, Hojung Cha

College of Computing

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

Abstract

Energy consumption in mobile devices is an important issue for both system developers and users. Users are aware of the battery-related information of their mobile devices and tend to take appropriate actions to increase the battery life. In this article, we propose a framework that accurately estimates the remaining battery time of applications at runtime. The framework profiles the power behavior of applications tied with activated hardware components and estimates the remaining battery budget utilizing the battery-related data provided by the device. The experiments validate that our method predicts the remaining battery time for applications with approximately 93% of accuracy.

Original language	English
Article number	48
Journal	ACM Transactions on Embedded Computing Systems
Volume	15
Issue number	3
DOIs	https://doi.org/10.1145/2875423
Publication status	Published - 2016 May

Bibliographical note

Publisher Copyright:
© 2016 ACM.

All Science Journal Classification (ASJC) codes

Software
Hardware and Architecture

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1145/2875423

Cite this

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AB - Energy consumption in mobile devices is an important issue for both system developers and users. Users are aware of the battery-related information of their mobile devices and tend to take appropriate actions to increase the battery life. In this article, we propose a framework that accurately estimates the remaining battery time of applications at runtime. The framework profiles the power behavior of applications tied with activated hardware components and estimates the remaining battery budget utilizing the battery-related data provided by the device. The experiments validate that our method predicts the remaining battery time for applications with approximately 93% of accuracy.

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KW - Predictability

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Accurate prediction of available battery time for mobile applications

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

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