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 interrupt (radio is not turned off between transmission) Radio 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