Skip to main content

Modeling the Impacts of WiFi Signals on Energy Consumption of Smartphones

  • Conference paper
  • First Online:
Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2017)

Abstract

In this paper, we explore the impacts of the WiFi signal strengths under normal signal conditions on the energy consumption of smartphones. Controlled experiments are conducted to quantitatively study the phone energy impacts by normal WiFi signals. As the experimental results show, the weaker the signal strength is, the faster the phone energy dissipates. To quantitatively describe such impacts, we construct a time-based signal strength-aware energy model. The energy modeling methods proposed in the paper enable ordinary developers to conveniently compute phone energy draw by utilizing cheap power meters as measurement tools. The modeling methods are general and able to be used for phones of any type and platform.

This work is supported by the National Natural Science Funds of China under Grant #61402197, Guangdong Province Science and Technology Plan Project #2017A040405030, Guangdong Province Natural Science Funds Team Project #S2012030006242, and Tianhe District Science and Technology Plan Project #201702YH108 in Guangzhou City of China.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Li, D., Hao, S., Gui, J., Halfond, W.G.: An empirical study of the energy consumption of android applications. In: Proceedings of 2014 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp. 121–130, September 2014

    Google Scholar 

  2. Pathak, A., Hu, Y.C., Zhang, M.: Where is the energy spent inside my app? Fine grained energy accounting on smartphones with Eprof. In: Proceedings of the 7th ACM European Conference on Computer Systems, pp. 29–42, April 2012

    Google Scholar 

  3. Prasad, S., Balaji, S.: Real-time energy dissipation model for mobile devices. In: Shetty, N.R., Prasad, N.H., Nalini, N. (eds.) Emerging Research in Computing, Information, Communication and Applications, pp. 281–288. Springer, New Delhi (2015). https://doi.org/10.1007/978-81-322-2550-8_27

    Chapter  Google Scholar 

  4. Gupta, A., Mohapatra, P.: Energy consumption and conservation in WiFi based phones: a measurement-based study. In: Proceedings of 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON 2007), pp. 122–131, June 2007

    Google Scholar 

  5. Ding, N., Wagner, D., Chen, X., Pathak, A., Hu, Y.C., Rice, A.: Characterizing and modeling the impact of wireless signal strength on smartphone battery drain. ACM SIGMETRICS Perform. Eval. Rev. 41, 29–40 (2013)

    Article  Google Scholar 

  6. Sun, L., Deng, H., Sheshadri, R.K., Zheng, W., Koutsonikolas, D.: Experimental evaluation of WiFi active power/energy consumption models for smartphones. IEEE Trans. Mob. Comput. 16(1), 115–129 (2017)

    Article  Google Scholar 

  7. Gomez Chavez, K.M.: Energy efficiency in wireless access networks: measurements, models and algorithms. Dissertation, University of Trento (2013)

    Google Scholar 

  8. WirelessMon tool, PassMark Software Inc. http://www.passmark.com/products/wirelessmonitor.htm

  9. Help Documentation on Evaluating Goodness of Fit, the MathWorks, Inc. https://www.mathworks.com/help/curvefit/evaluating-goodness-of-fit.html

  10. Zhang, L., et al.: Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In: Proceedings of 2010 IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), pp. 105–114, October 2010

    Google Scholar 

  11. Khan, M.O., et al.: Model-driven energy-aware rate adaptation. In: Proceedings of the Fourteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 217–226, July 2013

    Google Scholar 

  12. Xu, F., Liu, Y., Li, Q., Zhang, Y.: V-edge: fast self-constructive power modeling of smartphones based on battery voltage dynamics. In: Proceedings of USENIX NSDI, vol. 13, pp. 43–56, April 2013

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong Tang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sun, Y., Chen, J., Tang, Y. (2018). Modeling the Impacts of WiFi Signals on Energy Consumption of Smartphones. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00916-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00915-1

  • Online ISBN: 978-3-030-00916-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics