Multi-Time-Segment Dynamic Power Management for Environmentally Embedded Systems

doi:10.7652/xjtuxb201701023

您当前的位置：

首页 >

文章列表页 >

Multi-Time-Segment Dynamic Power Management for Environmentally Embedded Systems

更新时间：2025-07-09

- Multi-Time-Segment Dynamic Power Management for Environmentally Embedded Systems
- Vol. 51, Issue 1, Pages: 147-152(2017)
- 作者机构：
  
  天津大学电子信息工程学院,天津,300072
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201701023
  CLC： TP393
- Online First：10 January 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
Multi-Time-Segment Dynamic Power Management for Environmentally Embedded Systems[J]. 2017, 51(1): 147-152.
DOI：

Multi-Time-Segment Dynamic Power Management for Environmentally Embedded Systems[J]. 2017, 51(1): 147-152. DOI： 10.7652/xjtuxb201701023.

摘要

针对现有以无线传感器网络为代表的环境嵌入式系统的功耗管理方法不能很好地兼顾系统性能和稳定持久工作的问题

提出了一种能量驱动的多时段动态功耗管理方法。该方法综合考虑系统当前及未来N个时段的可用能量

并引入能量预测最大误差指数

实时地决定系统下一时段的工作状态

在满足能量约束的前提下最大化系统性能。通过实验模拟了太阳能供电环境嵌入式系统在不同天气下的工作情况

结果表明:使用该动态功耗管理方法的环境嵌入式系统的采集能量利用率达到99.79%

并且在不同天气下均能持续工作

即该方法实现了系统性能和工作稳定性的同时优化。

Abstract

Aiming at the existing methods for managing power of the environmentally embedded systems represented by wireless sensor network

which cannot simultaneously ensure the system performance and stability

this paper proposes a multi-time-segment dynamic power management method. This method could determine the operational mode of the systems in real time by considering the energy available in the current and the future N time-segments and the effect of energy prediction error

resulting in maximized system performance within the energy constraint. Experiment simulates a solar energy powered environmentally embedded system working under different weathers

and the results demonstrate that the system using the proposed method can make its energy utilization reach 99.79%

and work continuously under diverse weathers. Hence the proposed method achieves simultaneous optimization of the system's performance and stability.

关键词

Keywords

references

SADEGHIOON A M, METJE N, CHAPMAN D, et al. SmartPipes: smart wireless sensor networks for leak detection in water pipelines [J]. Journal of Sensor & Actuator Networks, 2014, 3(1): 64-78.

LOPES P R, TRINDADE J, GONÇALVES F, et al. A wireless sensor network for monitoring volcano-seismic signals [J]. Natural Hazards Earth System Sciences, 2014, 14(12): 3123-3142.

HE B, LI Y, HUANG H, et al. Spatial-temporal compression and recovery in a wireless sensor network in an underground tunnel environment [J]. Knowledge Information Systems, 2014, 41(2): 449-465.

MOSER C, THIELE L, BRUNELLI D, et al. Adaptive power management for environmentally powered systems [J]. IEEE Transactions on Computers, 2010, 59(4): 478-491.

齐小刚, 陆赞赞, 郑耿忠, 等. 一种低能耗低时延的睡眠调度算法 [J]. 西安电子科技大学学报, 2015, 42(1): 124-129

QI Xiaogang, LU Zanzan, ZHENG Gengzhong, et al. Low-power and low-delay sheep scheduling algorithm based on the data aggregation tree [J]. Journal of Xidian University, 2015, 42(1): 124-129.

王毅, 张德运, 陈海荣. 无线传感器网络汇聚节点动态功耗管理方法 [J]. 西安交通大学学报, 2008, 42(2): 138-142

WANG Yi, ZHANG Deyun, CHEN Hairong. Dynamic power management of sink node of wireless sensor networks [J]. Journal of Xi'an Jiaotong University, 2008, 42(2): 138-142.

刘超, 刘丽萍, 安新升, 等. 基于模糊控制的太阳能驱动无线传感器网络路由算法 [J]. 传感技术学报, 2014, 27(9): 1238-1246.

LIU Chao, LIU Liping, AN Xinsheng, et al. A fuzzy routing algorithm for solar powered wireless sensor networks [J]. Chinese Journal of Sensors and Actuators, 2014, 27(9): 1238-1246.

沈玲, 夏银水, 叶益迭. 基于指数平均模型的无线传感器网络动态功耗管理 [J]. 传感技术学报, 2014, 27(11): 1551-1556.

SHEN Ling, XIA Yinshui, YE Yidie. Dynamic power management based on exponential average model for WSN [J]. Chinese Journal of Sensors and Actuators, 2014, 27(11): 1551-1556.

褚超强, 马德, 罗小华. 一种自适应的动态功耗管理系统 [J]. 电子技术, 2016(1): 1-6.

CHU Chaoqiang, MA De, LUO Xiaohua. A self-aware dynamic power management system [J]. Electronic Technology, 2016(1): 1-6.

LAI Z, LAM K T, WANG C L, et al. PoweRock: power modeling and flexible dynamic power management for many core architectures [J]. IEEE Systems Journal, 2016, PP(99): 1-13.

OKAMURA H, DOHI T. Dynamic power management with optimal time-out policies [J]. IEEE Systems Journal, 2015, PP(99): 1-11.

LIU Qiang, MAK T, ZHANG Tao, et al. Power adaptive computing system design for solar-energy-powered embedded systems [J]. IEEE Transactions on Very Large Scale Integration Systems, 2014, 23(8): 1402-1414.

Oak Ridge National Laboratory. Rotating shadowband radiometer [EB/OL]. [2014-10-20]. http: ∥www. nrel.gov/midc/ornl_rsr/.DO.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Prolonging Lifecycle of Energy Balance Routing in Real-Time Wireless Sensor Networks

Secure Directed Diffusion Protocol Based on Random Key Predistribution Model

Directed Diffusion Algorithm Based on Dynamic Threshold

Dynamic Data Transmission Mechanism Based on Prediction Revision

Proportional Fairness Scheduling Algorithm Used for Wireless Sensor Network

Related Author

赵保华 1

李培龙 1

高存皓 1

周颢 1

宋飞 1

谷雨 1

Related Institution

Anhui Province Key Laboratory of Software in Computing and Communication

Department of Computer Science and Technology, University of Science and Technology of China

School of Mathematics and Computer Science, Ningxia University

School of Electronics and Information Engineering, Xi'an Jiaotong University

School of Mathematics and Computer Science, Ningxia University

⁰