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Exhausting battery statistics: understanding the energy demands on mobile handsets

Authors:

Narseo Vallina-Rodriguez,

Andrew RiceAuthors Info & Claims

MobiHeld '10: Proceedings of the second ACM SIGCOMM workshop on Networking, systems, and applications on mobile handhelds

Pages 9 - 14

https://doi.org/10.1145/1851322.1851327

Published: 30 August 2010 Publication History

Abstract

Despite the advances in battery technologies, mobile phones still suffer from severe energy limitations. Modern handsets are rich devices that can support multitasking thanks to their high processing power and provide a wide range of resources such as sensors and network interfaces with different energy demands. There have been multiple attempts to characterise those energy demands; both to save or to allocate energy to the applications on the handset. However, there is still little understanding on how the interdependencies between resources (interdependencies caused by the applications and users' behaviour) affect the battery life. In this paper, we demonstrate the necessity of considering all those dynamics in order to characterise the energy demands of the system accurately. These results indicate that simple algorithmic and rule-based scheduling techniques [7] are not the most appropriate way of managing the resources since their usage can be affected by contextual factors, making necessary to find customised solutions that consider each user's behaviour and handset features.

References

[1]

Android Reference. http://developer.android.com/reference.

[2]

N. Balasubramanian, A. Balasubramanian, and A. Venkataramani. Energy consumption in mobile phones: a measurement study and implications for network applications. In Proc. of the 9th ACM SIGCOMM IMC, 2009.

Digital Library

[3]

N. Banerjee, A. Rahmati, M. Corner, S. Rollins, and L. Zhong. Users and batteries: Interactions and adaptive energy management in mobile systems. In Proc. of UbiComp, 2007.

Digital Library

[4]

G. Banga, P. Druschel, and J. C. Mogul. Resource containers: A new facility for resource management in server systems. In Proc. of the 3rd ACM OSDI, 1999.

Digital Library

[5]

BeOS. en.wikipedia.org/wiki/beos.

[6]

M. Chuah, W. Luo, and X. Zhang. Impacts of inactivity timer values on UMTS system capacity. In Proc. of WCNC, 2002.

[7]

C. Efstratiou, K. Cheverst, N. Davies, and A. Friday. An architecture for the effective support of adaptive context-aware applications. In Mobile Data Management. Springer, January 2001.

Digital Library

[8]

J. Flinn and M. Satyanarayanan. Energy-aware adaptation for mobile applications. Proc. ACM SIGOPS, 1999.

Digital Library

[9]

J. Flinn and M. Satyanarayanan. Powerscope: A tool for profiling the energy usage of mobile applications. In Proc. of the 2nd IEEE WMCSA, 1999.

Digital Library

[10]

R. Fonseca, P. Dutta, P. Levis, and I. Stoica. Quanto: Tracking energy in networked embedded systems. In Proc. OSDI. USENIX, 2008.

Digital Library

[11]

GoogleLatitude. www.google.com/latitude.

[12]

C.-C. Lee, J.-H. Yeh, and J.-C. Chen. Impact of inactivity timer on energy consumption in WCDMA and cdma2000. In Proc. of WTS, May 2004.

[13]

A. Rahmati and L. Zhong. Context-for-wireless: context-sensitive energy-efficient wireless data transfer. In Proc. MobiSys, 2007.

Digital Library

[14]

N. Ravi, J. Scott, L. Han, and L. Iftode. Context-aware battery management for mobile phones. In Proc. IEEE PERCOM, 2008.

Digital Library

[15]

A. Rice and S. Hay. Decomposing power measurements for mobile devices. Proc. IEEE PERCOM, 2010.

[16]

S. M. Rumble, R. Stutsman, P. Levis, D. Mazières, and N. Zeldovich. Apprehending joule thieves with cinder. In Proc. MobiHeld, 2009.

Digital Library

[17]

G. A. F. Seber. Multivariate observations, Wiley. 1984.

[18]

C. Song, Z. Qu, N. Blumm, and A.L. Barabasi. Limits of predictability in human mobility. Science, 327(5968):1018--1021, 2010.

[19]

L. Song, U. Deshpande, U. C. Kozat, D. Kotz, and R. Jain. Predictability of WLAN mobility and its effects on bandwidth provisioning. In Proc. of IEEE INFOCOM, 2006.

[20]

H. Zeng, C. Ellis, A. R. Lebeck, and A. Vahdat. ECOSystem: Managing energy as a first class operating system resource.Proc. 10th ACM ASPLOS, 2002.

Digital Library

[21]

Z. Zhuang, K.-H. Kim, and J. Singh. Improving energy efficiency of location-sensing on smartphones. To appear in the Proc. of ACM MobiSys, 2010.

Digital Library

Cited By

Wang XWang X(2023)GraphPowerNet: Graph-based power consumption profiling for mobile phone applicationsComputer Networks10.1016/j.comnet.2023.110056237(110056)Online publication date: Dec-2023
https://doi.org/10.1016/j.comnet.2023.110056
Mukherjee ADe DBuyya R(2022)Green Mobile Cloud Computing forIndustry 5.0Green Mobile Cloud Computing10.1007/978-3-031-08038-8_1(3-20)Online publication date: 7-Oct-2022
https://doi.org/10.1007/978-3-031-08038-8_1
Dias JSilva JPaulino H(2022)Adaptive Replica Selection in Mobile Edge EnvironmentsMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-030-94822-1_14(243-263)Online publication date: 8-Feb-2022
https://doi.org/10.1007/978-3-030-94822-1_14
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Index Terms

Exhausting battery statistics: understanding the energy demands on mobile handsets
1. General and reference
  1. Cross-computing tools and techniques
    1. Measurement
    2. Metrics

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Published In

cover image ACM Conferences

MobiHeld '10: Proceedings of the second ACM SIGCOMM workshop on Networking, systems, and applications on mobile handhelds

August 2010

66 pages

ISBN:9781450301978

DOI:10.1145/1851322

Program Chairs:
Landon Cox
Duke University, USA
,
Alec Wolman
Microsoft Research Redmond, USA

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 August 2010

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SIGCOMM '10: ACM SIGCOMM 2010 Conference

August 30, 2010

New Delhi, India

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Cited By

Wang XWang X(2023)GraphPowerNet: Graph-based power consumption profiling for mobile phone applicationsComputer Networks10.1016/j.comnet.2023.110056237(110056)Online publication date: Dec-2023
https://doi.org/10.1016/j.comnet.2023.110056
Mukherjee ADe DBuyya R(2022)Green Mobile Cloud Computing forIndustry 5.0Green Mobile Cloud Computing10.1007/978-3-031-08038-8_1(3-20)Online publication date: 7-Oct-2022
https://doi.org/10.1007/978-3-031-08038-8_1
Dias JSilva JPaulino H(2022)Adaptive Replica Selection in Mobile Edge EnvironmentsMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-030-94822-1_14(243-263)Online publication date: 8-Feb-2022
https://doi.org/10.1007/978-3-030-94822-1_14
Guntha RRao SRamesh MBenndorf MHaensel-mann T(2020)AROM: An Adaptive Resource Optimization Model for Mobile IoT Edge2020 International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS48256.2020.9027321(396-403)Online publication date: Jan-2020
https://doi.org/10.1109/COMSNETS48256.2020.9027321
Dinesh Subramaniam EKrishnasamy V(2020)RETRACTED ARTICLE: Energy aware smartphone tasks offloading to the cloud using gray wolf optimizationJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-01756-y12:3(3979-3987)Online publication date: 12-Mar-2020
https://doi.org/10.1007/s12652-020-01756-y
Shin EGhosh DMehrotra SVenkatasubramanian NPoor VHan Z(2019)SCARFProceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3360774.3360826(358-367)Online publication date: 12-Nov-2019
https://dl.acm.org/doi/10.1145/3360774.3360826
Flores HHamberg JLi XMalmivirta TZuniga ALagerspetz ENurmi PWolman AZhong L(2019)Evaluating Energy-Efficiency using Thermal ImagingProceedings of the 20th International Workshop on Mobile Computing Systems and Applications10.1145/3301293.3302364(147-152)Online publication date: 22-Feb-2019
https://dl.acm.org/doi/10.1145/3301293.3302364
Atzori LGirau RPilloni VUras M(2019)Assignment of Sensing Tasks to IoT Devices: Exploitation of a Social Network of ObjectsIEEE Internet of Things Journal10.1109/JIOT.2018.28735016:2(2679-2692)Online publication date: Apr-2019
https://doi.org/10.1109/JIOT.2018.2873501
Ortiz GGarcia-De-Prado ABerrocal JHernandez J(2019)Improving Resource Consumption in Context- Aware Mobile Applications Through Alternative Architectural StylesIEEE Access10.1109/ACCESS.2019.29182397(65228-65250)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2918239
Vera-Burgos EZamora WMendoza-Rodriguez HSantamaría-Philco AVera-Navarrete DQuiroz-Palma P(2019)Energy Consumption for Anti-virus Applications in Android OSExplorations in Technology Education Research10.1007/978-3-030-11890-7_33(335-345)Online publication date: 29-Jan-2019
https://doi.org/10.1007/978-3-030-11890-7_33
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