accurate prediction of power consumption in sensor networks university of tubingen, germany in...
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![Page 1: Accurate Prediction of Power Consumption in Sensor Networks University of Tubingen, Germany In EmNetS 2005 Presented by Han](https://reader035.vdocuments.site/reader035/viewer/2022062807/5697c0231a28abf838cd418e/html5/thumbnails/1.jpg)
Accurate Prediction of Power Consumption in Sensor Networks
University of Tubingen, GermanyIn EmNetS 2005
Presented by Han
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Outline
• Goal
• Approach to build AEON
• Power evaluation of TinyOS
• Comparison with PowerTossim
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Goal
• To evaluate energy consumption of real codes– Algorithms and programming styles influence
power consumption– Predict network lifetime
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Approach
• Build an energy model
• Implement the energy model in an emulator
• Use the emulator to analyze power consumption of real codes and verify
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Building energy model
• Based on Mica2 platform
• Write special TinyOS programs to turn on each hardware component each time
• Measure the current draw
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Energy model
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Approach
• Build an energy model
• Implement the energy model in an emulator
• Use the emulator to analyze power consumption of real codes and verify
![Page 8: Accurate Prediction of Power Consumption in Sensor Networks University of Tubingen, Germany In EmNetS 2005 Presented by Han](https://reader035.vdocuments.site/reader035/viewer/2022062807/5697c0231a28abf838cd418e/html5/thumbnails/8.jpg)
Implementation
• AEON is implemented on top of AVRORA
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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
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Approach
• Build an energy model
• Implement the energy model in an emulator
• Use the emulator to analyze power consumption of real codes and verify
![Page 11: Accurate Prediction of Power Consumption in Sensor Networks University of Tubingen, Germany In EmNetS 2005 Presented by Han](https://reader035.vdocuments.site/reader035/viewer/2022062807/5697c0231a28abf838cd418e/html5/thumbnails/11.jpg)
Validation
• Average error 0.4%• deviation 0.24• Predict 172 hours for CntToLedsAndRfm
• 168 hours by Crossbow lifetime test
Blink application
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Evaluation of Apps
Executed for 60 seconds
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CntToLedsAndRfm
Radio interrupt (radio is not turned off between transmission)
Radio transmission
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HPLPowerManagement
• Dynamically switch the CPU between six sleep modes based on the current load
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Low power listening (B-MAC)
High data rate (wake up more frequently)
Low data rate (wake up less frequently)
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Predicted savings
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Energy profiling
• Map source code functions to the corresponding object code addresses (Surge)
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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
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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
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Results from PowerTossim
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Conclusion
• More accurate than PowerTossim (?)
• The energy evaluation parts give quantitatively improvement of designed protocols
• This tool would be useful in software development