薛普俊,朱正伟,诸燕平,朱晨阳.智能手机能耗多目标优化机制研究[J].电子测量与仪器学报,2022,36(3):241-250
智能手机能耗多目标优化机制研究
Research on multi-objective optimization mechanism ofsmartphone energy consumption
  
DOI:
中文关键词:  合并转发  WiFi 模块  统计模型检验  多目标优化  延迟时间
英文关键词:the combined forwarding  WiFi module  statistical model checking  multi-objective optimization  delay time
基金项目:常州市重点研发计划(应用基础研究)(CJ20210123)项目资助
作者单位
薛普俊 1.常州大学计算机与人工智能学院 
朱正伟 1.常州大学计算机与人工智能学院 
诸燕平 1.常州大学计算机与人工智能学院 
朱晨阳 1.常州大学计算机与人工智能学院 
AuthorInstitution
Xue Pujun 1.School of Computer Science and Artificial Intelligence, Changzhou University 
Zhu Zhengwei 1.School of Computer Science and Artificial Intelligence, Changzhou University 
Zhu Yanping 1.School of Computer Science and Artificial Intelligence, Changzhou University 
Zhu Chenyang 1.School of Computer Science and Artificial Intelligence, Changzhou University 
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中文摘要:
      智能手机的网络请求导致其续航能力下降,合并转发技术可有效降低能耗,但设置最优的合并转发时间仍是技术发展 的关键,通过大量人工实验解决该问题耗时耗力。 因此,基于统计模型检验,使用工具 UPPAAL-SMC,以概率时间自动机对安卓 设备中用户请求以及 WiFi 模块进行仿真建模,量化能耗、延迟、用户满意度等属性,进而利用统计模型检验,对不同请求频率的 场景进行蒙特卡洛模拟,获得延迟对能耗以及用户满意度的影响。 最终进行多目标优化,求得通用最优合并转发延迟时间为 22 s,在满足用户体验的前提下平均降低了 20%的能耗。 该方法可在不同的使用场景下模拟得到通用最优合并转发延迟时间, 为开发者提供参考。
英文摘要:
      The network request of smart phone leads to the decline of its endurance. The combined forwarding technology can effectively reduce energy consumption. But setting the optimal combined forwarding time is still the key to technological development. And it is time-consuming and labor-intensive to solve this problem through a large number of manual experiments. Therefore, this paper based on statistical model checking and used the tool UPPAAL-SMC to conduct simulation modeling of user requests and WiFi modules in Android devices with probabilistic time automata. Quantifying energy consumption, delay, user satisfaction and other attributes. And then used statistical model checking to conduct Monte Carlo simulation of scenarios with different request frequencies to obtain the effect of acquisition delay on energy consumption and user satisfaction. Finally, multi-objective optimization was carried out to obtain the general optimal combined forwarding delay time of 22 s, which reduced energy consumption by 20% on average on the premise of satisfying user experience. This method can simulate the general optimal merge forwarding delay time in different application scenarios, which can provide reference for developers.
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