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Accurate Prediction of Power Consumption in Sensor Networks

Accurate Prediction of Power Consumption in Sensor Networks. University of Tubingen, Germany In EmNetS 2005 Presented by Han. Outline. Goal Approach to build AEON Power evaluation of TinyOS Comparison with PowerTossim. Goal. To evaluate energy consumption of real codes

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Accurate Prediction of Power Consumption in Sensor Networks

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  1. Accurate Prediction of Power Consumption in Sensor Networks University of Tubingen, Germany In EmNetS 2005 Presented by Han

  2. Outline • Goal • Approach to build AEON • Power evaluation of TinyOS • Comparison with PowerTossim

  3. Goal • To evaluate energy consumption of real codes • Algorithms and programming styles influence power consumption • Predict network lifetime

  4. Approach • Build an energy model • Implement the energy model in an emulator • Use the emulator to analyze power consumption of real codes and verify

  5. Building energy model • Based on Mica2 platform • Write special TinyOS programs to turn on each hardware component each time • Measure the current draw

  6. Energy model

  7. Approach • Build an energy model • Implement the energy model in an emulator • Use the emulator to analyze power consumption of real codes and verify

  8. Implementation • AEON is implemented on top of AVRORA

  9. AVRORA • Developed by UCLA (IPSN’05) • Instruction-level simulator • Runs actual microcontroller program • Tossim use software to model hardware components • Lose timing and interrupt properties • AVRORA is 50% slower than Tossim

  10. Approach • Build an energy model • Implement the energy model in an emulator • Use the emulator to analyze power consumption of real codes and verify

  11. Validation • Average error 0.4% • deviation 0.24 • Predict 172 hours for CntToLedsAndRfm • 168 hours by Crossbow lifetime test Blink application

  12. Evaluation of Apps Executed for 60 seconds

  13. CntToLedsAndRfm Radio transmission Radio interrupt (radio is not turned off between transmission)

  14. HPLPowerManagement • Dynamically switch the CPU between six sleep modes based on the current load

  15. Low power listening (B-MAC) High data rate (wake up more frequently) Low data rate (wake up less frequently)

  16. Predicted savings

  17. Energy profiling • Map source code functions to the corresponding object code addresses (Surge)

  18. PowerTossim • Developed by Harvard (SenSys’04) • Build on top of Tossim • Based on nearly the same measurement • Benefit from the scalability of Tossim • Also lose some accuracy on capturing interrupts

  19. Comparison • For the same CntToLedsAndRfm application • PowerTossim predicts 2620mJ/min • AEON predicts 3023mJ/min • AEON claims that the additional energy is spent on reloading counter after timer interrupt

  20. Results from PowerTossim

  21. Conclusion • More accurate than PowerTossim (?) • The energy evaluation parts give quantitatively improvement of designed protocols • This tool would be useful in software development

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