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Diversity in smartphone usage

Published:15 June 2010Publication History

ABSTRACT

Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.

References

  1. Balasubramanian, N., Balasubramanian, A., and Venkataramani, A. Energy consumption in mobile phones: A measurement study and implications for network applications. In IMC (2009). Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Banerjee, N., Rahmati, A., Corner, M. D., Rollins, S., and Zhong, L. Users and batteries: Interactions and adaptive energy management in mobile systems. In Ubicomp (2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Becker, R., Chambers, J., and Wilks, A. The new S language. Chapman & Hall/CRC, 1988.Google ScholarGoogle Scholar
  4. Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S., and Zhao, L. Statistical analysis of a telephone call center. Journal of the American Statistical Association 100, 469 (2005).Google ScholarGoogle ScholarCross RefCross Ref
  5. Church, K., and Smyth, B. Understanding mobile information needs. In MobileHCI (2008). Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Dempster, A., Laird, N., and Rubin, D. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39, 1 (1977).Google ScholarGoogle ScholarCross RefCross Ref
  7. Esfahbod, B. Preload: An adaptive prefetching daemon. Master's thesis, University of Toronto, 2006.Google ScholarGoogle Scholar
  8. Falaki, H., Govindan, R., and Estrin, D. Smart screen management on mobile phones. Tech. Rep. 74, Center for Embedded Networked Sesning, 2009.Google ScholarGoogle Scholar
  9. Flinn, J., and Satyanarayanan, M. Managing battery lifetime with energy-aware adaptation. ACM TOCS 22, 2 (2004). Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Froehlich, J., Chen, M., Consolvo, S., Harrison, B., and Landay, J. MyExperience: A system for in situ tracing and capturing of user feedback on mobile phones. In MobiSys (2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Fukuda, K., Cho, K., and Esaki, H. The impact of residential broadband traffic on Japanese ISP backbones. SIGCOMM CCR 35, 1 (2005). Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Kotz, D., and Essien, K. Analysis of a campus-wide wireless network. Wireless Networks 11, 1--2 (2005). Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Kwiatkowski, D., Phillips, P., Schmidt, P., and Shin, Y. Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics 54, 1--3 (1992).Google ScholarGoogle ScholarCross RefCross Ref
  14. Lahiri, K., Dey, S., Panigrahi, D., and Raghunathan, A. Battery-driven system design: A new frontier in low power design. In Asia South Pacific design automation/VLSI Design (2002). Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Ljung, G., and Box, G. On a measure of lack of fit in time series models. Biometrika 65, 2 (1978).Google ScholarGoogle ScholarCross RefCross Ref
  16. Smartphone futures 2009-2014. http://www.portioresearch.com/Smartphone09--14.html.Google ScholarGoogle Scholar
  17. Rahmati, A., Qian, A., and Zhong, L. Understanding human-battery interaction on mobile phones. In MobileHCI (2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Rahmati, A., and Zhong, L. Human-battery interaction on mobile phones. Pervasive and Mobile Computing 5, 5 (2009). Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Rahmati, A., and Zhong, L. A longitudinal study of non-voice mobile phone usage by teens from an underserved urban community. Tech. Rep. 0515-09, Rice University, 2009.Google ScholarGoogle Scholar
  20. Rao, R., Vrudhula, S., and Rakhmatov, D. Battery modeling for energy aware system design. IEEE Computer 36, 12 (2003). Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Ravi, N., Scott, J., Han, L., and Iftode, L. Context-aware battery management for mobile phones. In PerCom (2008). Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Sharma, A., Navda, V., Ramjee, R., Padmanabhan, V., and Belding, E. Cool-Tether: Energy efficient on-the-fly WiFi hot-spots using mobile phones. In CoNEXT (2009). Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Sharma, C. US wireless data market update -- Q3 2009. http://www.chetansharma.com/usmarketupdateq309.htm.Google ScholarGoogle Scholar
  24. Shye, A., Sholbrock, B., and G, M. Into the wild: Studying real user activity patterns to guide power optimization for mobile architectures. In Micro (2009). Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Snol, L. More smartphones than desktop PCs by 2011. http://www.pcworld.com/article/171380/more\_smartphones\_than\_desktop%_pcs\_by\_2011.html.Google ScholarGoogle Scholar
  26. Sohn, T., Li, K., Griswold, W., and Hollan, J. A diary study of mobile information needs. In SIGCHI (2008). Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Trestian, I., Ranjan, S., Kuzmanovic, A., and Nucci, A. Measuring serendipity: Connecting people, locations and interests in a mobile 3G network. In IMC (2009). Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Williamson, C., Halepovic, E., Sun, H., and Wu, Y. Characterization of CDMA2000 Cellular Data Network Traffic. In Local Computer Networks (2005). Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Willkomm, D., Machiraju, S., Bolot, J., and Wolisz, A. Primary users in cellular networks: A large-scale measurement study. In DySPAN (2008).Google ScholarGoogle Scholar

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      • Published in

        cover image ACM Conferences
        MobiSys '10: Proceedings of the 8th international conference on Mobile systems, applications, and services
        June 2010
        382 pages
        ISBN:9781605589855
        DOI:10.1145/1814433

        Copyright © 2010 ACM

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        Publication History

        • Published: 15 June 2010

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