Accurate Prediction of Power Consumption in Sensor Networks

University of Tubingen, GermanyIn 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– Algorithms and programming styles influence

power consumption– Predict network lifetime

Approach

• Build an energy model

• Implement the energy model in an emulator

• Use the emulator to analyze power consumption of real codes and verify

Building energy model

• Based on Mica2 platform

• Write special TinyOS programs to turn on each hardware component each time

• Measure the current draw

Energy model

Approach

• Build an energy model

• Implement the energy model in an emulator

• Use the emulator to analyze power consumption of real codes and verify

Implementation

• AEON is implemented on top of AVRORA

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

Approach

• Build an energy model

• Implement the energy model in an emulator

• Use the emulator to analyze power consumption of real codes and verify

Validation

• Average error 0.4%• deviation 0.24• Predict 172 hours for CntToLedsAndRfm

• 168 hours by Crossbow lifetime test

Blink application

Evaluation of Apps

Executed for 60 seconds

CntToLedsAndRfm

Radio interrupt (radio is not turned off between transmission)

Radio transmission

HPLPowerManagement

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

Low power listening (B-MAC)

High data rate (wake up more frequently)

Low data rate (wake up less frequently)

Predicted savings

Energy profiling

• Map source code functions to the corresponding object code addresses (Surge)

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

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

Results from PowerTossim

Conclusion

• More accurate than PowerTossim (?)

• The energy evaluation parts give quantitatively improvement of designed protocols

• This tool would be useful in software development