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Personalized Battery Lifetime Prediction for Mobile Devices based on Usage Patterns

  • Kang, Joon-Myung (Department of Electrical and Computer Engineering, University of Toronto) ;
  • Seo, Sin-Seok (Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH)) ;
  • Hong, James Won-Ki (Division of IT Convergence and Engineering, Pohang University of Science and Technology (POSTECH))
  • Received : 20110700
  • Accepted : 20110800
  • Published : 2011.12.30

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 our approach in the real world.

Keywords

References

  1. S. Rollins and C. Chang-Yit, "A battery-aware algorithm for supporting collaborative applications," Collaborative Computing: Networking, Applications and Worksharing. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, E. Bertino and J. B. D. Joshi, Eds., Heidelberg, Germany: Springer Berlin, 2009, pp. 594-608.
  2. L. Benini, A. Bogliolo, and G. De Micheli, "Dynamic power management of electronic systems," Proceedings of the IEEE/ ACM International Conference on Computer-Aided Design, San Jose, CA, 1998, pp. 696-702.
  3. C. Krintz, Y. Wen, and R. Wolski, "Application-level prediction of battery dissipation," Proceedings of the International Symposium on Lower Power Electronics and Design, Newport Beach, CA, 2004, pp. 224-229.
  4. Intel Corporation and Microsoft Corporation, Advanced Power Management (APM) BIOS Interface Specification Revision 1.2, 1996.
  5. Compaq Computer Corporation, Intel Corporation, Microsoft Corporation, Phoenix Technologies Ltd., and Toshiba Corporation, Advanced Configuration and Power Interface Specification, Revision 2.0b, 2002.
  6. M. Doyle, T. Fuller, and J. Newman, "Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell," Journal of the Electrochemical Society, vol. 140, no. 6, pp. 1526- 1533, 1993. https://doi.org/10.1149/1.2221597
  7. V. Tiwari, S. Malik, A. Wolfe, and M. T. C. Lee, "Instruction level power analysis and optimization of software," Journal of VLSI Signal Processing Systems, vol. 13, no. 2-3, pp. 223-238, 1996. https://doi.org/10.1007/BF01130407
  8. H. Saputra, M. Kandemir, N. Vijaykrishnan, M. J. Irwin, J. S. Hu, C. H. Hsu, and U. Kremer, "Energy-conscious compilation based on voltage scaling," Proceedings of the Joint Conference on Languages, Compilers and Tools for Embedded Systems and Software and Compilers for Embedded Systems, Berlin, Germany, 2002, pp. 2-11.
  9. V. Tiwari, S. Malik, and A. Wolfe, "Power analysis of embedded software: a first step towards software power minimization," IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 2, no. 4, pp. 437-445, 1994. https://doi.org/10.1109/92.335012
  10. G. K. Zipf, Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology, New York, NY: Hafner Publishing Co., 1965.
  11. J. M. Kang, C. K. Park, S. S. Seo, M. J. Choi, and J. Hong, "User-centric prediction for battery lifetime of mobile devices," Challenges for Next Generation Network Operations and Service Management. Lecture Notes in Computer Science Vol. 5297, Y. Ma, D. Choi, and S. Ata, Eds., Heidelberg, Germany: Springer Berlin, 2008, pp. 531-534.
  12. Benchmarq Microelectronics Inc., Duracell Inc., Energizer Power Systems, Intel Corporation, Linear Technology, Maxim Integrated Products, Mitsubishi Electric Corporation, National Semiconductor Corporation, and Toshiba Battery Co., Smart Battery Data Specification, Revision 1.1, 1998.
  13. L. Zhang, B. Tiwana, R. P. Dick, Z. Qian, Z. M. Mao, Z. Wang, and L. Yang, "Accurate online power estimation and automatic battery behavior based power model generation for smartphones," Proceedings of the 8th IEEE/ACM International Conference on Hardware/Software-Co-Design and System Synthesis, Scottsdale, AZ, 2010, pp. 105-114.
  14. R. G. Brown, Statistical Forecasting for Inventory Control, New York, NY: McGraw-Hill, 1959.
  15. P. R. Winters, "Forecasting sales by exponentially weighted moving averages," Management Science, vol. 6, no. 3, pp. 324-342, 1960. https://doi.org/10.1287/mnsc.6.3.324

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