Skip to main content
SpringerLink
Log in

Reinforcement Learning-Based Dynamic Power Management for Energy Harvesting Wireless Sensor Network

  • Conference paper
Next-Generation Applied Intelligence (IEA/AIE 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5579))

Abstract

In this study, a dynamic power management method based on reinforcement learning is proposed to improve the energy utilization for energy harvesting wireless sensor networks. Simulations of the proposed method on wireless sensor nodes powered by solar power are performed. Experimental results demonstrate that the proposed method outperforms the other power management method in achieving longer sustainable operations for energy harvesting wireless sensor network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2004)

    MATH  Google Scholar 

  2. Benini, L., Bogliolo, A., Micheli, G.D.: A Survey of Design Techniques for System-level Dynamic Power Management. IEEE Transactions on VLSI Systems 8(3), 299–316 (2000)

    Article  Google Scholar 

  3. Benini, L., Bogliolo, A., Paleologo, G.A., Micheli, G.D.: Policy Optimization for Dynamic Power Management. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 18(6), 813–833 (1999)

    Article  Google Scholar 

  4. Chung, E.Y., Benini, L., Bogliolo, A., Lu, Y.H., Micheli, G.D.: Dynamic Power Management for Non-stationary Service Requests. IEEE Transactions on Computers 51(11), 1345–1361 (2002)

    Article  MathSciNet  Google Scholar 

  5. Hwang, C.H., Wu, C.H.: A Predictive System Shutdown Method for Energy Saving of Event-driven Computation. In: Proc. of IEEE/ACM International Conference on Computer-Aided Design, pp. 28–32 (1997)

    Google Scholar 

  6. Jeong, K.S., Lee, W.Y., Kim, C.S.: Energy Management Strategies of a Fuel Cell/Battery Hybrid System Using Fuzzy Logics. Journal of Power Sources 145, 319–326 (2005)

    Article  Google Scholar 

  7. Kansal, A., Hsu, J., Srivastava, M., Raghunathan, V.: Harvesting Aware Power Management for Sensor Networks. In: Proc. of ACM/IEEE Design Automation Conference, pp. 651–656 (2006)

    Google Scholar 

  8. Kansal, A., Hsu, J., Zahedi, S., Srivastava, M.B.: Power Management in Energy Harvesting Sensor Networks. ACM Transactions on Embedded Computing Systems 6(4), Article 32 (2007)

    Google Scholar 

  9. Li, D., Chou, P.H.: Maximizing Efficiency of Solar-powered Systems by Load Matching. In: Proc. of ISPLED, pp. 162–167 (2004)

    Google Scholar 

  10. Moser, C., Thiele, L., Brunelli, D., Benini, L.: Adaptive Power Management in Energy Harvesting Systems. In: Proc. of Design, Automation & Test in Europe Conference & Exhibition, pp. 1–6 (2007)

    Google Scholar 

  11. Pao, J.W.: The Evaluation of Operation Performance of a Photovoltaic System. Master thesis, Department of Electrical Engineering, National Chung Yuan University, Taiwan (2002)

    Google Scholar 

  12. Qui, Q., Pedram, M.: Dynamic Power Management Based on Continuous-time Markov Decision Process. In: Proc. of Design Automation Conference, pp. 555–561 (1999)

    Google Scholar 

  13. Raghunathan, V., Chou, P.H.: Design and Power Management of Energy Harvesting Embedded Systems. In: Proc. of ISLPED, pp. 369–374 (2006)

    Google Scholar 

  14. Raghunathan, V., Kansal, A., Hsu, J., Friedman, J., Srivastava, M.: Design considerations for solar energy harvesting wireless embedded systems. In: Proc. of Information Processing in Sensor Networks, pp. 457–462 (2005)

    Google Scholar 

  15. Zhuo, J., Chakrabarti, C., Lee, K., Chang, N.: Dynamic Power Management with Hybrid Power Sources. In: Proc. of Design Automation Conference, pp. 871–876 (2007)

    Google Scholar 

  16. Honsberg, C., Bowden, S.: Photovoltaics CDROM, http://www.udel.edu/Igert/pvcdrom/index.html

  17. Square One WIKI - Solar Position: Calculator, http://squ1.org/wiki/Solar_Position_Calculator

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Chiayi University, No.300, Syuefu Road, 60004, Chiayi City, Taiwan

    Roy Chaoming Hsu, Cheng-Ting Liu & Wei-Ming Lee

Authors
  1. Roy Chaoming Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheng-Ting Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei-Ming Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Tainan, 700, Tainan, Taiwan

    Been-Chian Chien

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

  4. Department of Computer Science, Texas State University-San Marcos, 601 University Drive, 78666-4616, San Marcos, TX, USA

    Moonis Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chaoming Hsu, R., Liu, CT., Lee, WM. (2009). Reinforcement Learning-Based Dynamic Power Management for Energy Harvesting Wireless Sensor Network. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02568-6_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02567-9

  • Online ISBN: 978-3-642-02568-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation