无线信号强度对智能手机电池损耗影响的表征和建模

Ning Ding, Daniel T. Wagner, Xiaomeng Chen, Y. C. Hu, A. Rice
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引用次数: 192

摘要

尽管智能手机有着巨大的市场渗透率,但它们的效用一直并将继续受到电池寿命的严重限制。智能手机电池消耗的一个主要来源是在运行各种应用程序和服务时通过蜂窝或WiFi连接访问互联网。尽管有很多轶事证据表明,智能手机用户在低信号强度下会更快地耗尽电池,但人们对智能手机用户经历低信号强度的频率以及低信号强度对手机电池消耗的定量影响的了解有限。这些问题的答案对于诊断和改善蜂窝网络服务和智能手机电池寿命至关重要,并有助于为智能手机建立更准确的在线功率模型,这是构建能量分析和优化智能手机应用程序的基础。在本文中,我们首次对无线信号强度对智能手机能耗的影响进行了测量和建模研究。我们的研究有四个贡献。首先,通过分析在3785部智能手机上收集的至少一个月的信号,我们发现,无论是在空间上还是在时间上,3G和WiFi的信号强度都很差,这是智能手机用户的常规体验。其次,我们量化了由于无线信号强度差而导致的数据传输的额外能量消耗。第三,我们为WiFi和3G开发了一种新的功率模型,该模型结合了信号强度因素,并且比以前的技术水平显著提高了建模精度。最后,我们进行了假设分析,通过探索用户体验的信号强度动态,量化机会主义延迟网络流量的潜在节能。
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Characterizing and modeling the impact of wireless signal strength on smartphone battery drain
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.
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