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Enhancing Energy Awareness in Wireless Sensor Networks

This research focuses on measuring and monitoring energy consumption in wireless sensor networks for improved efficiency. It discusses setup, analysis, and validation methodologies while outlining the need for energy-awareness in routing and management. The study utilizes Mica2 motes and ATMEGA devices for accurate and inexpensive monitoring. Various errors and calibration techniques are explored to ensure precise energy measurements. Furthermore, the PowerTOSSIM simulator is evaluated for its effectiveness, highlighting divergence and potential improvements. The study concludes with recommendations for enhancing energy-based sampling, increasing accuracy, and improving simulation models.

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Enhancing Energy Awareness in Wireless Sensor Networks

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  1. Towards Runtime Support for Energy Awareness in Wireless Sensor Networks Thomas Trathnigg and Reinhold Weiss Institute for Technical Informatics Graz University of Technology Graz, A-8010 Austria {trathnigg,rweiss}@iti.tugraz.at

  2. Outline • Introduction • Measuring Energy in WSNs • Measurement Setup • Error Analysis • Validate PowerTOSSIM • Conclusion + Outlook

  3. Introduction • Lifetime of a wireless sensor network depends on the energy consumption of each node • Limited energy-budget • Battery-powered • Energy harvesting • Energy-awareness • Energy-aware routing • Dynamic Power Management • …

  4. Introduction • Monitor the energy consumption of each mote • Online monitoring • Simulator calibration • Requirements • accurate • low-power • small • inexpensive

  5. Mica2 Motes • ATMEGA 128L • 7.3 Mhz 8-bit CPU • 128 KB code, 4 KB RAM • 433, 868 or 916 Mhz, 76.8 Kbps FSK radio transceiver • 512 KB flash for logging • Sandwich-on sensor boards • Powered by 2 AA batteries

  6. Typical Current Profile of Mica2 • 14-bit 100MHz dual-channel Digitizer (National Instruments) • Fast changes in current profile due to cpu and radio state changes • Clamp-on current probes • „Fuel Gauges“ • Based on peridodical ADC sampling • Energy-Driven Sampling • Based on an approach published by Chang et al. • Detecting software hotspots on a PDA

  7. Measurement Setup

  8. Error Analysis • Non-ideal behavior of electrical components • discharge time of capacitor • 500ns, 1000:1 ratio at 35mA; 0.1% error • Voltage at the mote • voltage drop caused by shunt resistor • <2% error • Current-sense amplifier • error is below 2% in the range 3 to 66mA • Bandwith of current-sense amplifier • fastest current change measured on mica2 mote 2.4mV/s • We expect the error of our setup to be below 5%

  9. Calibration + Verfication • Determined the amount of energy a ramp depicts (54J) • Verification • Stable voltage supply 3V • Constant current load • 60s measurments • Comparision of measurement with calculated result

  10. Error of Measurement Setup

  11. Validate PowerTOSSIM • TinyOS 1.1.15 • PowerTOSSIM • Support only for mica2 • used CPU cycle counting • mica2 (868MHz) • Deluge disabled • Several TinyOS Applications measured for 60s

  12. Typical TinyOS Applications

  13. Analysis of PowerTOSSIM Divergence • Possible reasons • Measurement errors • Inaccuracies in the simulation PowerTOSSIM simulates at 4MHz, mica2 motes operate at 7.38MHz • Power-model of PowerTOSSIM systematic errors values of the power-model may be inaccurate 443MHz vs 868MHz Other hardware differences

  14. Energy Consumption of Different Motes • No mica2 motes at 443MHz available, so we checked only other hardware differences • 8 different mica2 motes • 4% max. difference for Blink • 3.4% max. difference for CntToRfm

  15. Conclusion + Outlook • Approach of energy-based sampling is feasible • size • cost • low-power, should be improved • accuracy • range must be increased • Improve/calibrate energy model of PowerTOSSIM • Redesign of measurement setup • measurement range • low-power • integration on mica2 sensorboard

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