The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Accurate online power estimation and automatic battery behavior based power model generation for smartphones
Abstract
This paper describes PowerBooter, an automated power model construction technique that uses built-in battery voltage sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components. It requires no external measurement equipment. We also describe PowerTutor, a component power management and activity state introspection based tool that uses the model generated by PowerBooter for online power estimation. PowerBooter is intended to make it quick and easy for application developers and end users to generate power models for new smartphone variants, which each have different power consumption properties and therefore require different power models. PowerTutor is intended to ease the design and selection of power efficient software for embedded systems. Combined, PowerBooter and PowerTutor have the goal of opening power modeling and analysis for more smartphone variants and their users.
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Cites
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Runtime power monitoring in high-end processors: methodology and empirical data
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PowerScope: a tool for profiling the energy usage of mobile applications
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Run-time power estimation in high performance microprocessors
Joseph, R., & Martonosi, M. (2001) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1145/383082.383119 · Cited by 248
Cited by
A Survey of Mobile Cloud Computing Application Models
Khan, A. U. R., Othman, M., Madani, S. A., & Khan, S. U. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.062613.00160 · Cited by 722
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
Yao, S., Hu, S., Zhao, Y., Zhang, A., & Abdelzaher, T. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052577 · Cited by 667
To offload or not to offload? The bandwidth and energy costs of mobile cloud computing
Barbera, M. V., Kosta, S., Mei, A., & Stefa, J. (2013) · 2013 Proceedings IEEE INFOCOM · doi:10.1109/infcom.2013.6566921 · Cited by 398
Accurate WiFi based localization for smartphones using peer assistance
Liu, H., Yang, J., Sidhom, S., Wang, Y., Chen, Y., & Ye, F. (2014) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2013.140 · Cited by 151
DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning
Xu, M., Qian, F., Zhu, M., Huang, F., Pushp, S., & Liu, X. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2893250 · Cited by 138
Metadata
- Authors
- Zhang, L., Tiwana, B., Qian, Z., Wang, Z., Dick, R. P., Mao, Z. M., & Yang, L.
- Venue
- Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
- Publisher
- ACM
- Published
- 2010-10-24
- Pages
- 105-114
- OpenAlex
- W2554125638
- Cited by
- 1,122
- References
- 7
- FWCI
- 122.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhang, L., Tiwana, B., Qian, Z., Wang, Z., Dick, R. P., Mao, Z. M., & Yang, L. (2010). Accurate online power estimation and automatic battery behavior based power model generation for smartphones. Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis. https://doi.org/10.1145/1878961.1878982
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