Automatic Power Model Generation for Sensor Network Simulator

  • Jaebok Park
  • Hyunwoo Joe
  • Hyungshin Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4523)

Abstract

Energy consumption estimation in sensor network is a critical process for network lifetime estimation before actual deployment. Energy consumption can be estimated by simulating the sensor network with a power model. Power model is the key component for the accurate estimation. However, the power model is not publicly accessible and it is not easy to generate accurate fine-grain power model. In this paper we proposed a simplified but yet accurate power model for AVR-based sensor nodes. Also, we developed an automated power model generation tool. The tool generates an instruction-level power model that can be connected to sensor network simulators. We model the current consumption of ATmega128 instruction set which is the most widely used processor in sensor node. Loading, execution, and control of the measurement framework are managed by the tool. Using the tool, we can generate power models of various sensor nodes using ATmega128 as their processor. Experimental result shows that our tool successfully generated accurate power models of various sensor nodes including Mica2.

Keywords

sensor network power model energy consumption estimation embedded system ubiquitous computing 

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References

  1. 1.
    Shnayder, V., et al.: Simulating the power consumption of large-scale sensor network applications. In: SenSys04, pp. 188–200 (2004)Google Scholar
  2. 2.
  3. 3.
    Crossbow Technology, Inc. Mica2, http://www.xbow.com
  4. 4.
    ETRI, Research on low power measurement for sensor network. Project report (2005)Google Scholar
  5. 5.
    Sinha, A., et al.: JouleTrack – A Web based tool for software energy profiling. In: DAC (2001)Google Scholar
  6. 6.
    Tiwari, V., Malik, S., Wolfe, A.: Power analysis of embedded software:A first step towards software power minimization. IEEE Transactions on VLSI system 2(4), 437–445 (1994)CrossRefGoogle Scholar
  7. 7.
    Muttreja, A., et al.: Automated energy/performance macromodeling of embedded software. In: DAC2004, pp. 99–102 (2004)Google Scholar
  8. 8.
    Ye, W., et al.: The design and use of SimplePower. In: Proc. ACM/IEEE Design Automation Conf., pp. 340–345 (2000)Google Scholar
  9. 9.
    Brooks, D., et al.: Wattch: A framework for architectural-level power analysis and optimization. In: ISCA, pp. 83–94 (2000)Google Scholar
  10. 10.
    Burger, D., Austin, T.: The simplescalar tool set, version 2.0. Technical report, Computer Sciences Department, University of Wisconsin (June 1997)Google Scholar
  11. 11.
  12. 12.
    Octacomm Corp. Nano-24, http://www.octacomm.net
  13. 13.
    CESL Laboratory, CESL Mote, http://eslab.cnu.ac.kr
  14. 14.
    Hanback Corp. Zigbex, http://www.hanback.co.kr
  15. 15.
    Electronics telecommunication Research Institute. Nano-qplus, http://qplus.or.kr/english/jsp/transfer/transfer_03_1.jsp

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jaebok Park
    • 1
  • Hyunwoo Joe
    • 2
  • Hyungshin Kim
    • 2
  1. 1.Division of Electronics and Information Engineering, Chonbuk National University, JoenjuKorea
  2. 2.Department of Computer Science and Engineering, Chungnam National University, DaejeonKorea

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