Embedded Power Optimization Method Based on User Behavior

Abstract

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

CLC Number:

TP3-05

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.

References

[1] Abhinav P, Abhilash J, Charlie H Y, Samuel P M.What is keeping my phone awake? Characterizing and Detecting No-Sleep Energy Bugs in Smartphone Apps.[C]// Proc. of the 10th International Conference on Mobile Systems, Applications, and Services. New York, USA: ACM, 2012: 267-280.
[2] 夏军, 郑建宏. 嵌入式系统的动态电源管理架构[J]. 单片机与嵌入式系统应用, 2005, 1(1): 8-10, 24.
[3] Su C, Tsui C, Despain A M.Low power architecture design and compilation techniques for high-performance processors[C]// Proc of Compcon Spring'94. San Francisco, CA, USA: IEEE, 1994: 489-498.
[4] Sreraman N, Govindarajan R.A vectorizing compiler for multimedia extensions[J]. International Journal of Parallel Programming (S0885-7458), 2000, 28(4): 363-400.
[5] 夏宏, 苏林萍. Cache低功耗技术研究[J]. 计算机工程与应用, 2005, 41(23): 118-120.
[6] Mahmut T Kandemir.Reducing Energy Consumption of Multiprocessor SoC Architectures by Exploiting Memory Bank Locality[J]. ACM Transactions on Design Automation of Electronic Systems (TODAES)(S1084-4309), 2006, 11(2): 410-441.
[7] Wu D.,Al-Hashimi, B.M., Eles, P, Scheduling and Mapping of Conditional Task Graph for the Synthesis of Low Power embedded Systems[J]. IEEE Proceedings of Computers and Digital Techniqu (S1350-2387), 2003, 150(5): 262-273.
[8] Zhuge Q, Shao Z, Xiao B, Sha E H-M. Design space minimization with timing and code size optimization for embedded DSP[C]// First IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, 2003, Newport Beach, CA, USA. USA: IEEE, 2003: 144-149.
[9] Suganuma T, Yasue T, Nakatani T.A region-based compilation technique for a Java just-in-time compiler[J]. ACM SIGPLAN Notic (S0362-1340), 2003, 38(5): 312-323.
[10] Dey A K.Providing architectural support for building context-aware are applications [D]. Atlanta, USA: Georgia Institute of Technology, 2000.
[11] Reddy S, Mun M, Burke J, Hansen D, Srivastava M.Using mobile phones to determine transportation modes[C]// ACM Trans. Sensor Netw. 6, 2, Article 13(February 2010). USA: ACM, 2010: 27.
[12] Yiqiang Chen, Juan Qi, Zhuo Sun,Qiong Ning.Mining user goals for indoor location based services with low energy and high qos[J]. Computational Intelligence (S0824-7935), 2010, 26(3): 318-336.
[13] Zhongtang Zhao, Yiqiang Chen, Junfa Liu, Zhiqi Shen, Mingjie Liu.Cross-People Mobile-Phone Based Activity Recognition [C]// IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence. Palo Alto, California 94303, USA: AAAI Press, 2011, Vol 3: 2545-2550.
[14] Yiqiang Chen, Zhongtang Zhao, Shuangquan Wang, Zhenyu Chen.Extreme Learning Machine based device displacement free activity recognition model[J]. Soft Computing (S1432-7643), 2012, 16(9): 1617-1625.
[15] 钱正. 大学生手机使用行为研究—基于武汉大学生手机调查数据的分析[J]. 广告大观(理论版), 2012, 1(1): 27-30.
[16] 张学工. 模式识别[M]. 第三版. 北京: 清华大学出版社, 2010, 8: 137-138.
[17] 雷小锋, 谢昆青, 林帆, 等. 一种基于K-Means局部最优性的高效聚类算法[J]. 软件学报, 2008, 19(7): 1683-1692.
[18] 汪中, 刘贵全, 陈恩红. 一种优化初始中心点的K-means算法[J]. 模式识别与人工智能, 2009, 22(2): 299-304.