Characterizing and modeling the impact of wireless signal strength on smartphone battery drain

  • Authors:

  • Ning Ding;Daniel Wagner;Xiaomeng Chen;Abhinav Pathak;Y. Charlie Hu;Andrew Rice

  • Affiliations:

  • Purdue University, West Lafayette, IN, USA;Cambridge University, Cambridge, United Kingdom;Purdue University, West Lafayette, IN, USA;Purdue University, West Lafayette, IN, USA;Purdue University, West Lafayette, IN, USA;Cambridge University, Cambridge, United Kingdom

  • Venue:
  • Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
  • Year:
  • 2013

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Abstract

Despite the tremendous market penetration of smartphones, their utility has been and will remain severely limited by their battery life. A major source of smartphone battery drain is accessing the Internet over cellular or WiFi connection when running various apps and services. Despite much anecdotal evidence of smartphone users experiencing quicker battery drain in poor signal strength, there has been limited understanding of how often smartphone users experience poor signal strength and the quantitative impact of poor signal strength on the phone battery drain. The answers to such questions are essential for diagnosing and improving cellular network services and smartphone battery life and help to build more accurate online power models for smartphones, which are building blocks for energy profiling and optimization of smartphone apps. In this paper, we conduct the first measurement and modeling study of the impact of wireless signal strength on smartphone energy consumption. Our study makes four contributions. First, through analyzing traces collected on 3785 smartphones for at least one month, we show that poor signal strength of both 3G and WiFi is routinely experienced by smartphone users, both spatially and temporally. Second, we quantify the extra energy consumption on data transfer induced by poor wireless signal strength. Third, we develop a new power model for WiFi and 3G that incorporates the signal strength factor and significantly improves the modeling accuracy over the previous state of the art. Finally, we perform what-if analysis to quantify the potential energy savings from opportunistically delaying network traffic by exploring the dynamics of signal strength experienced by users.