In selecting a mobile application, the absence of the power-related information obstructs the user from making the smart decision. To handle this problem, we propose an energy efficiency grading system for mobile applications. The system measures the power consumption of the applications over the test scenarios where the representative usage patterns of users are considered. After that, the system rates the grades of the applications based on the measured power consumption. Therefore, the user can refer to the appropriate energy efficiency grade of the applications by taking into account own usage pattern. Moreover, the proposed system provides a label for the energy efficiency grades, so the user can intuitively understand the power-related information of the applications. The evaluation of the system shows that it can help the user to select a better application among interchangeable applications.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Statista (2016) Number of apps available in leading app stores as of June 2016. http://www.statista.com/statistics/276623/number-of-apps-available-in-leading-app-stores/. Accessed 18 July 2017
Qualcomm (2013) Designing mobile devices for low power and thermal efficiency. Qualcomm Technologies Report 1-13
Fayal-Khelfi M (2016) Using mobile data collectors to enhance energy efficiency and reliability in delay tolerant wireless sensor networks. J Inf Process Syst 12(2):275–294
Pughat A, Sharma V (2015) A review on stochastic approach for dynamic power management in wireless sensor networks. Hum Centric Comput Inf Sci 5(1):1–14
Saravanan V et al (2015) An optimizing pipeline stall reduction algorithm for power and performance on multi-core CPUs. Hum Centric Comput Inf Sci 5(1):1–13
Hao S et al (2013) Estimating mobile application energy consumption using program analysis. In: International Conference on Software Engineering (ICSE), pp 92–101
Pathak A, Hu YC, Zhang M (2012) Where is the energy spent inside my app?: fine grained energy accounting on smartphones with eprof. In: Proceedings of the 7th ACM European Conference on Computer Systems, pp 29–42
Zhang L et al (2010) Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In: Proceedings of 8th IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, pp 105–114
Chanmin, (2012) AppScope: Application energy metering framework for android smartphones using kernel activity monitoring. In: Proceedings of 2012 USENIX Annual Technical Conference, p 36
Gui J et al (2016) Lightweight measurement and estimation of mobile ad energy consumption. In: Proceedings of 5th IEEE/ACM International Workshop on Green and Sustainable Software, pp 1–7
Min C et al (2015) PowerForecaster: predicting smartphone power impact of continuous sensing applications at pre-installation time. In: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, pp 31–44
Min C et al (2016) Pada: power-aware development assistant for mobile sensing applications. In: Proceedings of 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp 946–957
Lee S (2017) A context-driven energy assessment for energy-aware development of mobile sensing applications. In: Proceedings of 2017 Workshop on MobiSys 2017 Ph. D. Forum 7-8
Luo C et al (2017) TestAWARE: a laboratory-oriented testing tool for mobile context-aware applications. In: Proceedings of ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol 80, pp 1–29
Jeon J, Baek D, Lee J-W et al (2016) Study on evaluating power consumption of context-aware application based on BMT. In: The 2016 Spring Conference of the KIPS, pp 533–545
Ferreira D et al (2013) Revisiting human–battery interaction with an interactive battery interface. In: Proceedings of 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp 563–572
Lee J, Kim H (2013) Framework for automated power estimation of android applications. In: Proceedings of 11th Annual International Conference on Mobile Systems, Applications, and Services, pp 541–542
Mobile Enerlytics LCC. eStar: because mobile devices are not mobile if they are plugged in. http://mobileenerlytics.com. Accessed 14 Feb 2018
Wilke C (2014) Energy-aware development and labeling for mobile applications. Diss. Saechsische Landesbibliothek-Staats-und Universitaetsbibliothek Dresden
Jabbarvand R et al (2015) Ecodroid: an approach for energy-based ranking of android apps. In: Proceedings of Fourth International Workshop on Green and Sustainable Software, pp 8–14
Choi Ki-Yong, Lee Jung-Won (2014) Portable power measurement system for mobile devices. J KIISE Comput Pract Lett 20:131–142
Monsoon Solutions, Inc. http://www.msoon.com/LabEquipment/PowerMonitor. Accessed 22 July 2017
Brouwers N et al (2014) NEAT: a novel energy analysis toolkit for free-roaming smartphones. In: Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, Sensys’ 14, pp 16–30
strAI (2016) FRep—Finger Replayer. https://play.google.com/store/apps/. Accessed 24 July 2017
This research was supported by the National Research Foundation of Korea (NRF) Grant funded by the MSIP (NRF-2016R1A2B1014376).
About this article
Cite this article
Baek, D., Park, J. & Lee, J. An energy efficiency grading system for mobile applications based on usage patterns. J Supercomput 74, 6502–6515 (2018). https://doi.org/10.1007/s11227-018-2439-x
- Energy efficiency grade
- Power consumption
- Low power
- Mobile application
- Mobile device