research-article

Real-Time Power Management for Embedded M2M Using Intelligent Learning Methods

Author:

Anand PaulAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 13, Issue 5s

Article No.: 148, Pages 1 - 22

https://doi.org/10.1145/2632158

Published: 23 July 2014 Publication History

Abstract

In this work, an embedded system working model is designed with one server that receives requests by a requester by a service queue that is monitored by a Power Manager (PM). A novel approach is presented based on reinforcement learning to predict the best policy amidst existing DPM policies and deterministic markovian nonstationary policies (DMNSP). We apply reinforcement learning, namely a computational approach to understanding and automating goal-directed learning that supports different devices according to their DPM. Reinforcement learning uses a formal framework defining the interaction between agent and environment in terms of states, response action, and reward points. The capability of this approach is demonstrated by an event-driven simulator designed using Java with a power-manageable machine-to-machine device. Our experiment result shows that the proposed dynamic power management with timeout policy gives average power saving from 4% to 21% and the novel dynamic power management with DMNSP gives average power saving from 10% to 28% more than already proposed DPM policies.

References

[1]

A. Ahmad, S. Jabbar, A. Paul, and S. Rho. 2014. Mobility aware energy efficient congestion control in wireless sensor network. Int. J. Distrib. Sensor Netw. 2014.

[2]

R. I. Bahar, G. Albera, and S. Manne. 1998. Power and performance tradeoffs using various caching strategies. In Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED'98). 64--69.

Digital Library

[3]

R. S. Bajwa, M. Hiraki, H. Kojima, D. J. Gorny, K. Nitta, A. Shridhar, K. Seki, and K. Sasaki. 1997. Instruction buffering to reduce power in processors for signal processing. IEEE Trans. VLSI Syst. 5, 4, 417--424.

Digital Library

[4]

N. Bambos. 1998. Toward power-sensitive network architectures in wireless ommunications. IEEE Person. Comm. 5, 3, 50--58.

[5]

L. Benini and G. De Micheli. 1997. Dynamic Power Management: Design Techniques and CAD Tools. Kluwer Academic, Dordrecht, Netherlands.

Digital Library

[6]

L. Benini, A. Bogliolo, and G. De Micheli. 2000. A survey of design technique for system level dynamic power management. IEEE Trans. VLSI Syst. 8, 3.

Digital Library

[7]

L. Benini, R. Hodgson, and P. Siegel. 1998. System-level power estimation and optimization. In Proceedings of the International Symposium on Low Power Electronics and Design. 173--178.

Digital Library

[8]

L. Benini, G. Paleologo, A. Bogliolo, and G. De Micheli. 1999. Policy optimization for dynamic power management. IEEE Trans. Comput.-Aid. Des. 18, 6, 813--833.

Digital Library

[9]

E. Chung, L. Benini, and G. De Micheli. 1999a. Dynamic power management using adaptive learning trees. In Proceedings of the International Conference on Computer-Aided Design. 274--279.

Digital Library

[10]

E. Chung, L. Benini, and G. De Micheli. 1999b. Dynamic power management for nonstationary service requests. In Proceedings of the Design, Automation, and Test in Europe Conference. 77--81.

Digital Library

[11]

R. Golding, P. Bosch, and J. Wilkes. 1996. Idleness is not sloth. Tech. rep. HPL-96-140, HP Laboratories.

[12]

S. Irani, S. Shukla, and R. Gupta. 2002. Competitive analysis of dynamic power management strategies for systems with multiple power saving state. In Proceedings of the Design, Automation, and Test in Europe Conference. 117.

Digital Library

[13]

Y. Lu, T. Simunic, and G. De Micheli. 1999. Software controlled power management. In Proceedings of the 7^th International Workshop on Hardware/Software Codesign. 157--161.

Digital Library

[14]

Y. Lu, E. Chung, T. Simunic, L. Benini, and G. De Micheli. 2000. Quantitative comparison of pm algorithms. In Proceedings of the Design, Automation, and Test in Europe Conference. 20--26.

Digital Library

[15]

A. Paul. 2013a. Dynamic power management for ubiquitous network devices. Adv. Sci. Lett. 19, 7, 2046--2049.

[16]

A. Paul. 2013b. Graph based m2m optimization in an iot environment. In Proceedings of the ACM Research in Adaptive Convergent Systems Conference. 45--46.

Digital Library

[17]

A. Paul, Y. C. Jiang, and J. F. Wang. 2010. Computation aware scheme for visual signal processing. J. Softw. 5, 6, 573--578.

[18]

A. Paul, Y. C. Jiang, J. F. Wang, and J. F. Yang. 2012. Parallel reconfigurable computing based mapping algorithm for motion estimation in advanced video coding. ACM Trans. Embed. Comput. Syst. 11, S2.

Digital Library

[19]

Z. Ren, B. H. Krogh, and R. Marculescu. 2005. Hierarchical adaptive dynamic power management IEEE Trans. Comput. 54, 4, 409--420.

Digital Library

[20]

C. H. C. Ribeiro. 2002. A tutorial on reinforcement learning techniques. http://www.ppgia.pucpr.br/&sim;fabricio/ftp/Aulas/Mestrado/AS/Artigos-Apresentacoes/Aprendizagem&percnt;20por&percnt;20Reforco/TAIC-tutorial_RL.pdf.

[21]

E. Shih, V. Bahl, and M. Sinclair. 2003. Reducing energy consumption of wireless, mobile devices using a secondary low-power channel. Tech. rep., MIT Laboratory for Computer Science. March.

[22]

S. Shukla and R. Gupta. 2001. A model checking approach to evaluating system level power management for embedded systems. In Proceedings of the IEEE Workshop on High Level Design Validation and Test (HLDVT'01). 53.

Digital Library

[23]

T. Simunic. 2000. Dynamic management of power consumption. In Power Aware Computing, Springer, 101--125.

Digital Library

[24]

T. Simunic, L. Benini, and G. De Micheli. 2000. Power management of laptop hard disk. In Proceedings of the Design, Automation, and Test in Europe Conference. 736.

Digital Library

[25]

T. Simunic, L. Benini, and G. De Micheli. 1999. Event-driven power management. In Proceedings of the International Symposium on System Synthesis. 18--23.

Digital Library

[26]

A. Singaravelu and S. Sivasubramanian. 2012. Probability and Queueing Theory by Singaravelu 19^th Ed. Meenakshi Agency.

[27]

A. Sinha and A. Chandrakasan. 2001. Dynamic power management in wireless sensor networks. IEEE Des. Test Comput. 18, 2, 62--74.

Digital Library

[28]

R. S. Sutton and A. G. Barto. 1998. Reinforcement Learning -- An Introduction. MIT Press, Cambridge, MA.

Digital Library

[29]

K. C. Veluvolu and Y. C. Soh. 2011. Fault reconstruction and state estimation with sliding mode observers for lipchitz nonlinear systems. IET Proc. Control Theory Appl. 5, 11, 1255--1263.

[30]

L. Zhong and N. K. Jha. 2004. Dynamic power optimization of interactive systems. In Proceedings of the IEEE International Conference on VLSI Design. 1041--1047.

Digital Library

[31]

L. Zhong and N. K. Jha. 2006. Dynamic power optimization targeting user delays in interactive systems. IEEE Trans. Mobile Comput. 5, 11, 1473--1488.

Digital Library

Cited By

Ramachandran MPatan RKumar AHosseini SGandomi A(2023)Mutual Informative MapReduce and Minimum Quadrangle Classification for Brain Tumor Big DataIEEE Transactions on Engineering Management10.1109/TEM.2021.307301870:8(2644-2655)Online publication date: Aug-2023
https://doi.org/10.1109/TEM.2021.3073018
Manoj Kumar TKarthigaikumar P(2022)Implementation of a High-Speed and High-Throughput Advanced Encryption StandardIntelligent Automation & Soft Computing10.32604/iasc.2022.02009031:2(1025-1036)Online publication date: 2022
https://doi.org/10.32604/iasc.2022.020090
Tai KHong JTsai CLin CHung Y(2022)Virtual reality for car-detailing skill development: Learning outcomes of procedural accuracy and performance quality predicted by VR self-efficacy, VR using anxiety, VR learning interest and flow experienceComputers & Education10.1016/j.compedu.2022.104458182(104458)Online publication date: Jun-2022
https://doi.org/10.1016/j.compedu.2022.104458
Show More Cited By

Index Terms

Real-Time Power Management for Embedded M2M Using Intelligent Learning Methods
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

Recommendations

Hybrid power management in real time embedded systems: an interplay of DVFS and DPM techniques

Energy-aware scheduling of real time applications over multiprocessor systems is considered in this paper. Early research reports that while various energy-saving policies, for instance Dynamic Power Management (DPM) and Dynamic Voltage & Frequency ...
Deriving a near-optimal power management policy using model-free reinforcement learning and Bayesian classification
DAC '11: Proceedings of the 48th Design Automation Conference

To cope with the variations and uncertainties that emanate from hardware and application characteristics, dynamic power management (DPM) frameworks must be able to learn about the system inputs and environment and adjust the power management policy on ...
Reinforcement learning based dynamic power management with a hybrid power supply
ICCD '12: Proceedings of the 2012 IEEE 30th International Conference on Computer Design (ICCD 2012)

Dynamic power management (DPM) in battery-powered mobile systems attempts to achieve higher energy efficiency by selectively setting idle components to a sleep state. However, re-activating these components at a later time consumes a large amount of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 13, Issue 5s

Special Issue on Risk and Trust in Embedded Critical Systems, Special Issue on Real-Time, Embedded and Cyber-Physical Systems, Special Issue on Virtual Prototyping of Parallel and Embedded Systems (ViPES)

November 2014

501 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/2660459

Editor:
Sandeep K. Shukla
Virginia Tech, USA

Issue’s Table of Contents

Copyright © 2014 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 23 July 2014

Accepted: 01 October 2013

Received: 01 June 2013

Published in TECS Volume 13, Issue 5s

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

31
Total Citations
View Citations
552
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)1

Reflects downloads up to 13 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ramachandran MPatan RKumar AHosseini SGandomi A(2023)Mutual Informative MapReduce and Minimum Quadrangle Classification for Brain Tumor Big DataIEEE Transactions on Engineering Management10.1109/TEM.2021.307301870:8(2644-2655)Online publication date: Aug-2023
https://doi.org/10.1109/TEM.2021.3073018
Manoj Kumar TKarthigaikumar P(2022)Implementation of a High-Speed and High-Throughput Advanced Encryption StandardIntelligent Automation & Soft Computing10.32604/iasc.2022.02009031:2(1025-1036)Online publication date: 2022
https://doi.org/10.32604/iasc.2022.020090
Tai KHong JTsai CLin CHung Y(2022)Virtual reality for car-detailing skill development: Learning outcomes of procedural accuracy and performance quality predicted by VR self-efficacy, VR using anxiety, VR learning interest and flow experienceComputers & Education10.1016/j.compedu.2022.104458182(104458)Online publication date: Jun-2022
https://doi.org/10.1016/j.compedu.2022.104458
Alsamhi SAlmalki FAl-Dois HBen Othman SHassan JHawbani ASahal RLee BSaleh H(2021)Machine Learning for Smart Environments in B5G NetworksComputational Intelligence and Neuroscience10.1155/2021/68051512021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6805151
Dass ASrivastava SChaudhary G(2021)Air pollution: A review and analysis using fuzzy techniques in Indian scenarioEnvironmental Technology & Innovation10.1016/j.eti.2021.10144122(101441)Online publication date: May-2021
https://doi.org/10.1016/j.eti.2021.101441
Zhang XWang FHan DMeng HWei BWang SLi YXue MYang Q(2019)Accurate analysis of frequency splitting in wireless power transfer by coupled solution of FEM and coupled mode theoryJournal of Intelligent & Fuzzy Systems10.3233/JIFS-179420(1-7)Online publication date: 1-Oct-2019
https://doi.org/10.3233/JIFS-179420
Lei GLuo XCai LGao LDai NXu QTan Z(2019)Research on smart EFK algorithm for electric vehicle battery packs management systemJournal of Intelligent & Fuzzy Systems10.3233/JIFS-179400(1-6)Online publication date: 23-Sep-2019
https://doi.org/10.3233/JIFS-179400
Rahmatov NPaul ASaeed FHong WSeo HKim J(2019)Machine learning–based automated image processing for quality management in industrial Internet of ThingsInternational Journal of Distributed Sensor Networks10.1177/155014771988355115:10(155014771988355)Online publication date: 29-Oct-2019
https://doi.org/10.1177/1550147719883551
Thilagam SKarthigaikumar P(2019)Implementation of Interior Noise Control System Using Digital Adaptive Filter for On-Road Car ApplicationsWireless Personal Communications: An International Journal10.1007/s11277-018-6023-8104:1(339-356)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s11277-018-6023-8
Jeon GPasupa KAnisetti MAhmad A(2019)Image enhancement in embedded devices for internet of thingsConcurrency and Computation: Practice and Experience10.1002/cpe.539833:3Online publication date: 14-Jun-2019
https://doi.org/10.1002/cpe.5398
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents