research-article

The latency, accuracy, and battery (LAB) abstraction: programmer productivity and energy efficiency for continuous mobile context sensing

Authors:

A.J. Bernheim Brush,

Kathryn S. McKinley,

Todd Mytkowicz,

Ryder ZiolaAuthors Info & Claims

OOPSLA '13: Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications

Pages 661 - 676

https://doi.org/10.1145/2509136.2509541

Published: 29 October 2013 Publication History

Abstract

Emerging mobile applications that sense context are poised to delight and entertain us with timely news and events, health tracking, and social connections. Unfortunately, sensing algorithms quickly drain the phone's battery. Developers can overcome battery drain by carefully optimizing context sensing but that makes programming with context arduous and ties applications to current sensing hardware. These types of applications embody a twist on the classic tension between programmer productivity and performance due to their combination of requirements.

This paper identifies the latency, accuracy, battery (LAB) abstraction to resolve this tension. We implement and evaluate LAB in a system called Senergy. Developers specify their LAB requirements independent of inference algorithms and sensors. Senergy delivers energy efficient context while meeting the requirements and adapts as hardware changes. We demonstrate LAB's expressiveness by using it to implement 22 context sensing algorithms for four types of context (location, driving, walking, and stationary) and six diverse applications. To demonstrate LAB's energy optimizations, we show often an order of magnitude improvements in energy efficiency on applications compared to prior approaches. This relatively simple, priority based API, may serve as a blueprint for future API design in an increasingly complex design space that must tradeoff latency, accuracy, and efficiency to meet application needs and attain portability across evolving, sensor-rich, heterogeneous, and power constrained hardware.

References

[1]

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

Digital Library

[2]

G. Challen and M. Hempstead. The case for power-agile computing. In HotOS, 2011.

Digital Library

[3]

Y. Chon, E. Talipov, H. Shin, and H. Cha. Mobility prediction- based smartphone energy optimization for everyday location monitoring. In Sensys, 2011. ISBN 978-1-4503-0718-5.

Digital Library

[4]

D. Chu, N. D. Lane, T. T.-T. Lai, C. Pang, X. Meng, Q. Guo, F. Li, and F. Zhao. Balancing energy, latency and accuracy for mobile sensor data classification. In ACM SenSys, 2011.

Digital Library

[5]

M. Cohen, H. S. Zhu, E. E. Senem, and Y. D. Liu. Energy types. SIGPLAN Not., 47(10):831--850, Oct. 2012. ISSN 0362-1340.

Digital Library

[6]

S. Consolvo, D. W. McDonald, T. Toscos, M. Y. Chen, J. Froehlich, B. Harrison, P. Klasnja, A. LaMarca, L. LeGrand, R. Libby, I. Smith, and J. A. Landay. Activity sensing in the wild: A field trial of ubifit garden. In CHI, 2008.

Digital Library

[7]

I. Constandache, S. Gaonkar, M. Sayler, R. Choudhury, and L. Cox. Enloc: Energy-efficient localization for mobile phones. In IEEE Infocom, pages 2716--2720, April 2009.

[8]

J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. In OSDI, 2004.

Digital Library

[9]

E. Ertin, N. Stohs, S. Kumar, A. Raij, M. al'Absi, and S. Shah. AutoSense: Unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. In SenSys, 2011.

Digital Library

[10]

M. Haridasan, I. Mohomed, D. Terry, C. A. Thekkath, and L. Zhang. Startrack next generation: A scalable infrastructure for track-based applications. In OSDI, 2010.

Digital Library

[11]

Y. Ju, Y. Lee, J. Yu, C. Min, I. Shin, and J. Song. Symphoney: A coordinated sensing flow execution engine for concurrent mobile sensing applications. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, SenSys '12, pages 211--224, New York, NY, USA, 2012. ACM. ISBN 978-1-4503-1169-4.

Digital Library

[12]

S. Kang, J. Lee, H. Jang, H. Lee, Y. Lee, S. Park, T. Park, and J. Song. SeeMon: Scalable and energy-efficient context monitoring framework for sensor-rich mobile environments. In Proceedings of the 6th international conference on Mobile systems, applications, and services, MobiSys '08, pages 267--280, New York, NY, USA, 2008. ACM. ISBN 978-1-60558-139-2.

Digital Library

[13]

S. Kang, Y. Lee, C. Min, Y. Ju, T. Park, J. Lee, Y. Rhee, and J. Song. Orchestrator: An active resource orchestration framework for mobile context monitoring in sensor-rich mobile environments. In Pervasive Computing and Communications (PerCom), 2010 IEEE International Conference on, pages 135--144, 2010.

[14]

D. H. Kim, Y. Kim, D. Estrin, and M. B. Srivastava. Sensloc: Sensing everyday places and paths using less energy. In ACM SenSys, 2010.

Digital Library

[15]

M. B. Kjaergaard, J. Langdal, T. Godsk, and T. Toftkjaer. En- tracked: Energy-efficient robust position tracking for mobile devices. In MobiSys, 2009.

Digital Library

[16]

N. D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. T. Campbell. A survey of mobile phone sensing. Comm. Mag., 48:140--150, September 2010.

Digital Library

[17]

F. X. Lin, Z. Wang, R. LiKamWa, and L. Zhong. Reflex: Using low-power processors in smartphones without knowing them. In ASPLOS, 2012.

Digital Library

[18]

K. Lin, A. Kansal, D. Lymberopoulos, and F. Zhao. Energy- accuracy trade-off for continuous mobile device location. In MobiSys, pages 285--298, 2010.

Digital Library

[19]

H. Lu, J. Yang, Z. Liu, N. D. Lane, T. Choudhury, and A. T. Campbell. The Jigsaw continuous sensing engine for mobile phone applications. In SenSys, 2010.

Digital Library

[20]

H. Lu, A. J. B. Brush, B. Priyantha, A. K. Karlson, and J. Liu. Speakersense : Energy efficient unobtrusive speaker identification on mobile phones. Pervasive Computing, 6696: 188--205, 2011.

Digital Library

[21]

M. Mun, D. Estrin, J. Burke, and M. Hansen. Parsimonious mobility classification using gsm and wifi traces. In HotEm-Nets, 2008.

[22]

S. Nath. Ace: exploiting correlation for energy-efficient and continuous context sensing. In Mobisys, 2012.

Digital Library

[23]

T. Nick, E. Coersmeier, J. Geldmacher, and J. Goetze. Clas- sifying means of transportation using mobile sensor data. In The 2010 International Joint Conference on Neural Networks (IJCNN), 2010.

[24]

J. Paek, J. Kim, and R. Govindan. Energy-efficient rate-adaptive gps-based positioning for smartphones. In MobiSys, New York, NY, USA, 2010.

Digital Library

[25]

A. Popescu. Geolocation api specification. http://www.w3.org/TR/geolocation-API/.

[26]

B. Priyantha, D. Lymberopoulos, and J. Liu. LittleRock: Enabling energy-efficient continuous sensing on mobile phones. IEEE Pervasive Computing, 10(2), 2011.

Digital Library

[27]

M. Rabbi, S. Ali, T. Choudhury, and E. Berke. Passive and in-situ assessment of mental and physical well-being using mobile sensors. In Ubicomp, 2011.

Digital Library

[28]

K. K. Rachuri, M. Musolesi, C. Mascolo, P. J. Rentfrow, C. Longworth, and A. Aucinas. Emotionsense: a mobile phones based adaptive platform for experimental social psychology research. In Ubicomp, 2010.

Digital Library

[29]

L. Ravindranath, A. Thiagarajan, H. Balakrishnan, and S. Madden. Code in the air: simplifying sensing and coor- dination tasks on smartphones. In Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications, HotMobile '12, pages 4:1--4:6, New York, NY, USA, 2012. ACM. ISBN 978-1-4503-1207-3.

Digital Library

[30]

S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava. Using mobile phones to determine transportation modes. ACM Trans. Sen. Netw., 6(2):13:1--13:27, Mar. 2010. ISSN 1550-4859.

Digital Library

[31]

D. Salber, A. K. Dey, and G. D. Abowd. The context toolkit: aiding the development of context-enabled applications. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems, CHI '99, pages 434--441, New York, NY, USA, 1999. ACM. ISBN 0-201-48559-1.

Digital Library

[32]

T. Sohn, A. Varshavsky, A. Lamarca, M. Y. Chen, T. Choudhury, I. Smith, S. Consolvo, J. Hightower, W. G. Griswold, and E. D. Lara. Mobility detection using everyday gsm traces. In Ubicomp, 2006.

Digital Library

[33]

J. Sorber, A. Kostandinov, M. Garber, M. Brennan, M.D. Corner, and E. D. Berger. Eon: A language and runtime system for perpetual systems, In SenSys, 2007.

Digital Library

[34]

S. P. Tarzia, R. P. Dick, P. A. Dinda, and G. Memik. Sonar-based measurement of user presence and attention. In Ubicomp, 2009.

Digital Library

[35]

A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan, S. Toledo, and J. Eriksson. Vtrack: Accu- rate, energy-aware road traffic delay estimation using mobile phones. In SenSys, 2009.

Digital Library

[36]

TI. OMAP 5 mobile application platform, 2011.

[37]

A. B. Waluyo, W.-S. Yeoh, I. Pek, Y. Yong, and X. Chen. Mobisense: Mobile body sensor network for ambulatory monitoring. ACM Trans. Embed. Comput. Syst., 10(1):13:1--13:30, Aug. 2010. ISSN 1539-9087.

Digital Library

[38]

Y. Wang, J. Lin, M. Annavaram, Q. A. Jacobson, J. Hong, B. Krishnamachari, and N. Sadeh. A framework of energy efficient mobile sensing for automatic user state recognition. In MobiSys, 2009.

Digital Library

[39]

T. Yan, D. Chu, D. Ganesan, A. Kansal, and J. Liu. Fast app launching for mobile devices using predictive user context. In Mobisys, 2012.

Digital Library

[40]

Z. Zhuang, K.-H. Kim, and J. P. Singh. Improving energy efficiency of location sensing on smartphones. In MobiSys, 2010.

Digital Library

Cited By

Pervaiz AYang YDuracz ABartha FSai RImes CCartwright RPalem KLu SHoffmann HScholliers CSinger J(2022)GOAL: Supporting General and Dynamic Adaptation in Computing SystemsProceedings of the 2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software10.1145/3563835.3567655(16-32)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1145/3563835.3567655
Kannan THoffmann HFalsafi BFerdman MLu SWenisch T(2022)Protecting adaptive sampling from information leakage on low-power sensorsProceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3503222.3507775(240-254)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3503222.3507775
Wan CSantriaji MRogers EHoffmann HMaire MLu SGavrilovska AZadok E(2020)ALERTProceedings of the 2020 USENIX Conference on Usenix Annual Technical Conference10.5555/3489146.3489170(353-369)Online publication date: 15-Jul-2020
https://dl.acm.org/doi/10.5555/3489146.3489170
Show More Cited By

Index Terms

The latency, accuracy, and battery (LAB) abstraction: programmer productivity and energy efficiency for continuous mobile context sensing
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results

Recommendations

The latency, accuracy, and battery (LAB) abstraction: programmer productivity and energy efficiency for continuous mobile context sensing
OOPSLA '13

Emerging mobile applications that sense context are poised to delight and entertain us with timely news and events, health tracking, and social connections. Unfortunately, sensing algorithms quickly drain the phone's battery. Developers can overcome ...
Battery-aware scheduling inwireless mesh networks

Wireless mesh networks recently emerge as a flexible, low-cost and multipurpose networking platform with wired infrastructure connected to the Internet. A critical issue in mesh networks is to maintain network activities for a long lifetime with high ...
A Tour into Ambient Energy Resources and Battery Optimization
ICSIP '14: Proceedings of the 2014 Fifth International Conference on Signal and Image Processing

Modern mobile devices incorporate rich collection of sensing and communication capabilities allowing the design of diverse range of interactive context aware applications. Intensive use of these resources comes at a cost, typically in the form of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

OOPSLA '13: Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications

October 2013

904 pages

ISBN:9781450323741

DOI:10.1145/2509136

Co-chair:
Antony Hosking
Purdue University, USA
,
General Chair:
Patrick Eugster
Purdue University, USA
,
Program Chair:
Cristina V. Lopes
University of California, Irvine, USA

ACM SIGPLAN Notices Volume 48, Issue 10
OOPSLA '13
October 2013
867 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/2544173
Editor:
Mark W. Bailey
Hamilton College, Clinton, NY
Issue’s Table of Contents

Copyright © 2013 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 the author(s) 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].

Sponsors

SIGPLAN: ACM Special Interest Group on Programming Languages

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SPLASH '13

Sponsor:

SIGPLAN

SPLASH '13: Conference on Systems, Programming, and Applications: Software for Humanity

October 29 - 31, 2013

Indiana, Indianapolis, USA

Acceptance Rates

OOPSLA '13 Paper Acceptance Rate 50 of 189 submissions, 26%;

Overall Acceptance Rate 268 of 1,244 submissions, 22%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

37
Total Citations
View Citations
463
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)2

Reflects downloads up to 20 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Pervaiz AYang YDuracz ABartha FSai RImes CCartwright RPalem KLu SHoffmann HScholliers CSinger J(2022)GOAL: Supporting General and Dynamic Adaptation in Computing SystemsProceedings of the 2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software10.1145/3563835.3567655(16-32)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1145/3563835.3567655
Kannan THoffmann HFalsafi BFerdman MLu SWenisch T(2022)Protecting adaptive sampling from information leakage on low-power sensorsProceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3503222.3507775(240-254)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3503222.3507775
Wan CSantriaji MRogers EHoffmann HMaire MLu SGavrilovska AZadok E(2020)ALERTProceedings of the 2020 USENIX Conference on Usenix Annual Technical Conference10.5555/3489146.3489170(353-369)Online publication date: 15-Jul-2020
https://dl.acm.org/doi/10.5555/3489146.3489170
Moamen AJamali N(2020)Opportunistic Sharing of Continuous Mobile Sensing Data for Energy and Power ConservationIEEE Transactions on Services Computing10.1109/TSC.2017.270568513:3(503-514)Online publication date: 1-May-2020
https://doi.org/10.1109/TSC.2017.2705685
Colin ARuppel ELucia B(2018)A Reconfigurable Energy Storage Architecture for Energy-harvesting DevicesACM SIGPLAN Notices10.1145/3296957.317321053:2(767-781)Online publication date: 19-Mar-2018
https://dl.acm.org/doi/10.1145/3296957.3173210
Singh RCassell BKeshav SBrecht T(2018)TussleOSACM SIGCOMM Computer Communication Review10.1145/3243157.324316046:3(1-8)Online publication date: 27-Jul-2018
https://dl.acm.org/doi/10.1145/3243157.3243160
Colin ARuppel ELucia BShen XTuck JBianchini RSarkar V(2018)A Reconfigurable Energy Storage Architecture for Energy-harvesting DevicesProceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3173162.3173210(767-781)Online publication date: 19-Mar-2018
https://dl.acm.org/doi/10.1145/3173162.3173210
Wendt NJulien C(2018)Paco: A System-Level Abstraction for On-Loading Contextual Data to Mobile DevicesIEEE Transactions on Mobile Computing10.1109/TMC.2018.279560417:9(2127-2140)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1109/TMC.2018.2795604
Li YChen FLi TGuo YHuang GFredrikson MAgarwal YHong J(2017)PrivacyStreamsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31309411:3(1-26)Online publication date: 11-Sep-2017
https://dl.acm.org/doi/10.1145/3130941
Zhu YReddi V(2016)GreenWeb: language extensions for energy-efficient mobile web computingACM SIGPLAN Notices10.1145/2980983.290808251:6(145-160)Online publication date: 2-Jun-2016
https://dl.acm.org/doi/10.1145/2980983.2908082
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents