JCSE, vol. 5, no. 4, pp.338-345, 2011

DOI: http://dx.doi.org/10.5626/JCSE.2011.5.4.338

Personalized Battery Lifetime Prediction for Mobile Devices based on Usage Patterns

Joon-Myung Kang, Sin-seok Seo, James Won-Ki Hong
Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada/ Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea/ Division of IT Convergence and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea

Abstract: Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device’s available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate

Keyword: Battery lifetime prediction; Resource management; Performance management; Usage pattern; Mobile device

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