Online Cloud-Based Battery Lifetime Estimation Framework for Smartphone Devices

Raja Wasim Ahmad; Raja Sehrab Bashir; Sharjil Saeed; Yangsun Lee; Kwangman Ko; Yunsik Son

doi:10.1016/j.procs.2017.06.118

Online Cloud-Based Battery Lifetime Estimation Framework for Smartphone Devices

Raja Wasim Ahmad, Raja Sehrab Bashir, Sharjil Saeed, Yangsun Lee, Kwangman Ko, Yunsik Son

Division of AI Software Convergence

Research output: Contribution to journal › Conference article › peer-review

8 Scopus citations

Abstract

Smartphones are resource constrained external battery operated devices. The resources of smartphone devices such as a battery, CPU, and RAM are very low compared to the desktop server. However, the requirements of smartphone users are growing tremendously. As a result, smartphone applications perform rich functionality to enrich user experience. However, due to increase in the execution capacity of smartphone applications, smartphone battery lifetime minimizes. This study proposes a cloud-based framework that estimates battery lifetime of a smartphone device. Moreover, it overviews the theoretical design of computational offloading framework to present an application area for the proposed work. Finally, it presents preliminary results to evaluate the proposed framework.

Original language	English
Pages (from-to)	70-77
Number of pages	8
Journal	Procedia Computer Science
Volume	110
DOIs	https://doi.org/10.1016/j.procs.2017.06.118
State	Published - 2017
Event	14th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2017 - Leuven, Belgium Duration: 24 Jul 2017 → 26 Jul 2017

Keywords

Battery Lifetime Estimation
Cloud Computing
Computation Offloading
Energy Optimization
Mobile Computing

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10.1016/j.procs.2017.06.118

Cite this

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title = "Online Cloud-Based Battery Lifetime Estimation Framework for Smartphone Devices",

abstract = "Smartphones are resource constrained external battery operated devices. The resources of smartphone devices such as a battery, CPU, and RAM are very low compared to the desktop server. However, the requirements of smartphone users are growing tremendously. As a result, smartphone applications perform rich functionality to enrich user experience. However, due to increase in the execution capacity of smartphone applications, smartphone battery lifetime minimizes. This study proposes a cloud-based framework that estimates battery lifetime of a smartphone device. Moreover, it overviews the theoretical design of computational offloading framework to present an application area for the proposed work. Finally, it presents preliminary results to evaluate the proposed framework.",

keywords = "Battery Lifetime Estimation, Cloud Computing, Computation Offloading, Energy Optimization, Mobile Computing",

author = "Ahmad, {Raja Wasim} and Bashir, {Raja Sehrab} and Sharjil Saeed and Yangsun Lee and Kwangman Ko and Yunsik Son",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors. Published by Elsevier B.V.; 14th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2017 ; Conference date: 24-07-2017 Through 26-07-2017",

year = "2017",

doi = "10.1016/j.procs.2017.06.118",

language = "English",

volume = "110",

pages = "70--77",

journal = "Procedia Computer Science",

issn = "1877-0509",

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T1 - Online Cloud-Based Battery Lifetime Estimation Framework for Smartphone Devices

AU - Ahmad, Raja Wasim

AU - Bashir, Raja Sehrab

AU - Saeed, Sharjil

AU - Lee, Yangsun

AU - Ko, Kwangman

AU - Son, Yunsik

PY - 2017

Y1 - 2017

N2 - Smartphones are resource constrained external battery operated devices. The resources of smartphone devices such as a battery, CPU, and RAM are very low compared to the desktop server. However, the requirements of smartphone users are growing tremendously. As a result, smartphone applications perform rich functionality to enrich user experience. However, due to increase in the execution capacity of smartphone applications, smartphone battery lifetime minimizes. This study proposes a cloud-based framework that estimates battery lifetime of a smartphone device. Moreover, it overviews the theoretical design of computational offloading framework to present an application area for the proposed work. Finally, it presents preliminary results to evaluate the proposed framework.

AB - Smartphones are resource constrained external battery operated devices. The resources of smartphone devices such as a battery, CPU, and RAM are very low compared to the desktop server. However, the requirements of smartphone users are growing tremendously. As a result, smartphone applications perform rich functionality to enrich user experience. However, due to increase in the execution capacity of smartphone applications, smartphone battery lifetime minimizes. This study proposes a cloud-based framework that estimates battery lifetime of a smartphone device. Moreover, it overviews the theoretical design of computational offloading framework to present an application area for the proposed work. Finally, it presents preliminary results to evaluate the proposed framework.

KW - Battery Lifetime Estimation

KW - Cloud Computing

KW - Computation Offloading

KW - Energy Optimization

KW - Mobile Computing

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DO - 10.1016/j.procs.2017.06.118

M3 - Conference article

AN - SCOPUS:85028661409

SN - 1877-0509

VL - 110

SP - 70

EP - 77

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 14th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2017

Y2 - 24 July 2017 through 26 July 2017

ER -

Online Cloud-Based Battery Lifetime Estimation Framework for Smartphone Devices

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Keywords

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