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Performance Evaluation of Statistical Methods Applicable to Estimating Remaining Battery Runtime of Mobile Smart Devices

모바일 스마트 장치 배터리의 남은 시간 예측에 적용 가능한 통계 기법들의 평가

  • Tak, Sungwoo (School of Electrical and Computer Engineering, Pusan National University)
  • Received : 2017.10.18
  • Accepted : 2017.11.30
  • Published : 2018.02.28

Abstract

Statistical methods have been widely used to estimate the remaining battery runtime of mobile smart devices, such as smart phones, smart gears, tablets, and etc. However, existing work available in the literature only considers a particular statistical method. Thus, it is difficult to determine whether statistical methods are applicable to estimating thr remaining battery runtime of mobile devices or not. In this paper, we evaluated the performance of statistical methods applicable to estimating the remaining battery runtime of mobile smart devices. The statistical estimation methods evaluated in this paper are as follows: simple and moving average, linear regression, multivariate adaptive regression splines, auto regressive, polynomial curve fitting, and double and triple exponential smoothing methods. Research results presented in this paper give valuable data of insight to IT engineers who are willing to deploy statistical methods on estimating the remaining battery runtime of mobile smart devices.

모바일 스마트 장치 배터리의 남은 시간 예측에 통계적 기법이 많이 사용되고 있다. 그러나 특정 통계 기법만을 사용한 기존 연구들의 결과만으로는, 통계적 기법이 배터리의 남은 시간 예측에 적합한지가 판단하기 어렵다. 이에 본 논문에서는 스마트 장치 배터리의 남은 시간 예측에 적용 가능한 다양한 통계 기법들의 성능을 평가하였다. 평가에 사용된 통계 예측 기법은 단순 및 이동 평균, 선형 회귀, 다변수 적응 회귀, 자기 회귀, 다항식 회귀, 이중 및 삼중 지수평활 기법이다. 분석 결과는, 향후 통계적 기법을 배터리 남은 사용 시간 예측에 적용하려는 IT 엔지니어에게 중요한 자료로 활용될 수 있다.

Keywords

References

  1. S. Tak, "Evaluating power consumption and real-time performance of android cpu governors," Journal of Korea Institute of Information and Communication Engineering, vol. 20, no. 12, pp.2401-2409, Dec. 2016. https://doi.org/10.6109/jkiice.2016.20.12.2401
  2. C. Krintz, Y. Wen, and R. Wolski, "Application-level prediction of battery dissipation," in Proceedings of Symposium on Low Power Electronics and Design, California:USA, pp. 224-229, 2004.
  3. M. Kim, J. Kong, and S. Chung, "Enhancing online power estimation accuracy for smartphones," IEEE Transactions on Consumer Electronics, vol. 58, no. 2, pp. 333-339, May 2012. https://doi.org/10.1109/TCE.2012.6227431
  4. R. Murmuria, J. Medsger, A. Stavrou, and J.M.Voas, "Mobile Application and Device Power Usage Measurements," in Proceedings of Software Security and Reliability, Gaithersburg: USA, pp. 147-156, 2012.
  5. X. Xia, W. Xu, and X. Bai, "A smart remaining battery life prediction based on MARS," in Proceedings of Innovative Smart Grid Technologies, Washington: USA, pp. 1-5, 2014.
  6. J. Anton, P. Nieto, F. Juez, F. Lasheras, C. Viejo, and N. Gutierrez, "Battery state-of-charge estimator using the MARS technique," IEEE Transactions on Power Electronics, vol. 28, no. 8, pp. 3798-3805, Aug. 2013. https://doi.org/10.1109/TPEL.2012.2230026
  7. C. Thompson, D. Schmidt, H. Turner, and J White, "Analyzing mobile application software power consumption via model-driven engineering," in Proceedings of Pervasive and Embedded Computing and Communication Systems, Vilamoura: Portugal, pp. 101-113, 2011.
  8. M. Kuhn and K. Johnson, Applied predictive modeling, 1st ed. New York, Springer, 2013.
  9. K. Bryson and O. Ngwenyama, Advances in research methods for information systems research, 1st ed. New York, Springer, 2016.
  10. G. Lindgren and H. Rootzen, Stationary stochastic processes for scientists and engineers, 1st ed. London, CRC 2013.
  11. Engineering statistics handbook. Double exponential smoothing [Internet]. Available: http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc433.htm.
  12. D. Montgomery, C. Jennings, and M. Kulahci, Introduction to time series analysis and forecasting, 2nd ed. Wiley, 2015.
  13. Galaxy player (YP-GB1). Samsung [Internet]. Available: http://www.samsung.com/sec/support/model/YP-GB1CW
  14. Cynogenmods. Android custom ROMs [Internet]. Available: http://www.cyanogenmods.org