Wonjung Kim
Youngjae Chang
Junehwa Song
ABSTRACT
Energy-efficiency is a key performance metric of mobile sensing applications. However, assessment of energy-efficiency is greatly limited in practice. The main difficulty is that it requires assessment of power consumption in various user's real-life situation in the long term. This poster presents DeepPower, a system for assessing energy-efficiency of mobile sensing applications in fast and scalable manner. DeepPower introduces a sensor trace-based power use prediction technique, which significantly reduces the cost of assessing power consumption compared to existing power emulation techniques. Our experiments with three mobile sensing applications and five 1-hour-long sensor traces show that DeepPower can predict hardware usage of 1-hour-long sensor traces in less than a second, achieving average error rate of 4.6%.
- Corusen. 2020. Accupedo Pedometer. http://www.accupedo.com/Google Scholar
- Chulhong Min, Seungchul Lee, Changhun Lee, Youngki Lee, Seungwoo Kang, Seungpyo Choi, Wonjung Kim, and Junehwa Song. 2016. PADA: power-aware development assistant for mobile sensing applications. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 946--957.Google ScholarDigital Library
- Chulhong Min, Youngki Lee, Chungkuk Yoo, Seungwoo Kang, Sangwon Choi, Pillsoon Park, Inseok Hwang, Younghyun Ju, Seungpyo Choi, and Junehwa Song. 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. 31--44.Google ScholarDigital Library
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DeepPower: fast and scalable energy assessment of mobile sensing applications: poster abstract
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