计算机与现代化

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智能移动终端能耗敏感行为分析

  

  1. 1.东南大学信息科学与工程学院,江苏南京210096;2.国网电力科学研究院,江苏南京211000;
    3.国网冀北电力有限公司,北京100053
  • 收稿日期:2016-12-01 出版日期:2017-08-31 发布日期:2017-09-01
  • 作者简介:程茹洁(1992-),女,安徽太湖人,东南大学信息科学与工程学院硕士研究生,研究方向:信号处理,数据分析; 通信作者:陆建(1980-),男,讲师,博士,研究方向:信号处理,数据分析; 蒋 厚明(1980-),男,国网电力科学研究院工程师,硕士,研究方向:电力信息自动化; 胡牧(1979-),男,高级工程师,本科,研究方向:电力信息自动化; 吴佳,女,国网冀北电力有限公司高级 工程师,硕士,研究方向:分布式系统,电力信息自动化。
  • 基金资助:
    国家电网公司科技项目(SGTYHT\\14-JS-188)

Energyconscious Behavior Analysis of Mobile Phones

  1. 1. School of Information Science and Engineering, Southeast University, Nanjing 210096, China;

    2. State Grid Electric Power Research Institute, Nanjing 211000, China;
    3. State Grid Jibei Electric Power Company Limited, Beijing 100053, China
  • Received:2016-12-01 Online:2017-08-31 Published:2017-09-01

摘要:

 移动终端能耗敏感行为分析是手机能耗分析的重要组成部分,确定高能耗敏感行为及其影响因素能够为能耗优化提供帮助。本文通过软件和硬件检测相结合的方法,对iOS和Android平台的手机进
行测试。软件层面上借助系统API获取手机电量,设计开发应用程序获取手机在不同运行场景下的能耗,通过对该能耗的分析获得关键硬件组件如屏幕、音频、网络接口的相关行为能耗,并确定关键性变
量。硬件层面上通过NI myDAQ与计算机通信,实时获得手机电流并进一步计算出手机能耗,该测试结果进一步验证了软件层面能耗检测的有效性。最后,针对手机能耗敏感行为进行纵向比较排名,从而
确定手机的高能耗敏感行为。

关键词: 移动终端, 能耗敏感行为, 能耗分析, 软件检测, 硬件检测

Abstract:

Energyconscious behavior analysis plays a vital part in energy consumption analysis of mobile phones, it is of great help to energy optimization by understanding how
and why the energy is used. This paper presents a method which combined software detection and hardware detection on both iOS and Android platforms. In terms of software
detection,  the battery level was obtained based on system APIs and relevant applications which were developed to measure the energy consumption of key energyconsuming
entities, such as screen, audio and network interface under different using scenarios, so that the key factors could be determined. In terms of hardware detection, NI myDAQ was
used to communicate with computer. By recording the current in real time, the energy consumption was calculated. The experiment result of hardware detection validates the
effectiveness of software detection. In the end, by comparing all the energyconscious behaviors, the highly conscious behaviors are identified.

Key words:  , mobile phones; energyconscious behavior; energy consumption analysis; software detection; hardware detection

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