一种基于用户行为的嵌入式功耗优化方法

系统仿真学报 ›› 2015, Vol. 27 ›› Issue (2): 320-326.

一种基于用户行为的嵌入式功耗优化方法

王海, 高岭, 陈东棋, 任杰

西北大学信息科学与技术学院,西安 710127

收稿日期:2014-07-10 修回日期:2014-10-26 发布日期:2020-09-02
作者简介:王海(1977-),男,博士,讲师,研究方向为低功耗嵌入式系统、云计算;高岭(1964-),男,博士,教授,研究方向为云计算、网络安全、嵌入式系统;陈东棋(1988-),男,博士,研究方向为低功耗嵌入式系统、机器学习。
基金资助:
国家自然科学基金(61373176); 教育部博士点基金(20116101110016); 陕西省自然科学基金(2012JQ8047); 陕西省工业攻关项目(2014K05-42)

Embedded Power Optimization Method Based on User Behavior

Wang Hai, Gao Ling, Chen Dongqi, Ren Jie

School of Information Science and Technology, Northwest University, Xi'an 710127, China

Received:2014-07-10 Revised:2014-10-26 Published:2020-09-02

摘要/Abstract

摘要： 随着以手机、平板电脑等为代表的嵌入式设备的飞速发展,嵌入式低功耗问题已经成为了一个研究热点。经常有用户在户外面临手机没电所带来的问题,目前主要给嵌入式设备供电的电池却受到其体积、重量等因素的制约,只能提供有限的电量。从用户行为识别出发,用机器学习的方法识别设备用户当前的行为状态,根据既定的策略来获取该行为状态下用户对设备的使用习惯,进而通过主动关闭设备不使用的部件或者提醒用户应用动态交互优化策略来降低设备功耗。

关键词: 嵌入式系统, 低功耗, 行为识别, 机器学习

Abstract: In recent years, with the rapid development of embedded device represented by mobile phone and tablet computer, low power technology has been one of the hotspots in the embedded research field. Because the battery capacity of embedded device is limited due to its restricted volume and weight, there are often users suffering the problem that their phone battery being dead. There are many research directions in embedded low power field at present. The relationship between low power and user behavior recognition was aimed, which started with recognizing user behavior using machine learning and then obtains the user’s daily usage habits in specific behavior. Part of device components could be turned off or Dynamic Interactive Optimize Strategy was applied to reduce the power consumption.

Key words: embedded system, low power, user behavior recognition, machine learning

中图分类号:

TP3-05

王海, 高岭, 陈东棋, 任杰. 一种基于用户行为的嵌入式功耗优化方法[J]. 系统仿真学报, 2015, 27(2): 320-326.

Wang Hai, Gao Ling, Chen Dongqi, Ren Jie. Embedded Power Optimization Method Based on User Behavior[J]. Journal of System Simulation, 2015, 27(2): 320-326.

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