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Solar based WSN

Solar based WSN. Helimote [05] 20% duty cycle for one week Prometheus[05] Support 10 days using duty cycle from available power . Modeling of energy prediction.

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Solar based WSN

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  1. Solar based WSN • Helimote[05] • 20% duty cycle for one week • Prometheus[05] • Support 10 days using duty cycle from available power

  2. Modeling of energy prediction • A. Kansal, J. Hsu, S. Zahedi, and M. B. Srivastava, “Power management in energy harvesting sensor networks,” ACM Trans. Embed. Comput. Syst., 2007. • exponentially weighted moving-average (EWMA) • d • Past Predict Future (PPF) • N.Sharma, et al. ”Cloudy Computing: Leveraging Weather Forecast in Energy Harvesting Sensor System.” SECON’10 • Weather forecast • Linear regression • More accurate than PPF

  3. Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems Navin K. Sharma, Jeremy Gummeson, David Irwin, and PrashantShenoyConference on Sensor, Mesh and Ad Hoc Communications and Networks(SECON), 2010.

  4. Introduction • Energy harvesting system • Collectand store environmental energy • Sustain continuous operation w/o access to external power source • Often deployed in remote locations w/o power grid • Energy-neutral • Energy source produce power faster than consume • Depends on HW components or workload • energy-proportional HW and elastic workload • HW energy consumption scales linearly w/ workload intensity

  5. Challenges • unable to precisely change the intensity of inelastic workload and energy usage to match harvested energy • Inelastic workload • External requests • Internal object : fair steady node sensing rate • Highly sensitive to energy reserve and prediction

  6. Energy prediction • Weather forecast better than past to predict the future? • Analyze extensive historical weather data • Formulate forecast energy model

  7. Related work A.Kansal, et al, “Power Management in Energy Harvesting Sensor Network” Transactions on Embedded Computing System, 2007 • Past Predict the Future (PPF) • Xe(t) = αX(t) + (1- α)Xe(t-1) • Xe(t): average harvested energy of past days in the same slot • X(t): observed real energy at the same time slot • t : slot, eg. estimate per hr , t =1~24 • Strength.: accurate for sec. to min. • Weakness: unable for drastic weather changes

  8. PPF with solar observation • Sky condition shows significant inter-day and intra-day variations • accurate when time interval small (<2.4hr) • RMSE: root mean square error

  9. PPF with wind observation • Least accurate from 3hr to 1 week • Most accurate at <2min and >10days 3hr ~ 1 week

  10. Weather forecast model • Correlation forecast weather w/ local weather observations • Correlation local weather observations w/ energy harvesting by deployed panel and turbine • Predict how much energy harvesting in the future for given weather forecasts

  11. Data collection • National Weather Service (NWS) • Sky condition: cloud cover N% • wind speed • Forecast 3hr~72hr weather data per 3hr • Weather station deployed at UMASS (20 miles away) • Solar radiation(watt/m2) • Wind speed • Every 5 min • 2008~2009 • HOBO data logger • Battery voltage, battery current, current from energy source

  12. deployment • Solar panel 65W • Wind turbine 400W in 28 miles/hr • Charge controller • Load on 13.6V • Load off 12.1V • Battery 12V 105AH • Load: 60W bulb

  13. Solar power model • Compute solar power from radiation • Linear regression • Solarpower = 0.0444 * Radiation -2.65

  14. Solar power model • Computing the max possible solar power • Perfect clear and sunny day • MaxPower = a * (Time+b)2 + c • Power = MaxPower*(1 - skycondition)

  15. Wind power model • Power = 0.01787485 * (windspeed)3 – 3.4013 • Turbulent < fitted < steady

  16. Evaluation • Daily difference is small • Forecasting is accurate between 3hr~3day PPF RMSE

  17. Evaluation • Forecast vs PPF vs Conservative • Conservative • Reject all requests w/ durations greater than each node’s expected operation time based on its current reserve of stored energy • 2 cases study simulation • Lease request • Fair sensing rate

  18. Case study I • ViSETestbed • Sensor node: x86-processor connected to sensors • Lease external user access to a slice of its node • Make decision of lease request based on available energy • Node consumes 45~65W • Each request consumes 2.7W • asked at the beginning of each day • Simulation by 5 solar panel(60W) and 5 wind turbine(400W) • Evaluation • Number of leases approved / complete • Battery depletion

  19. Result I • PPF approved more leases than forecast but complete 2/3 of the leases • PPF also deplete its battery with 40%of days

  20. Case study II • Set Lexicographically fair sensing rate based on available energy • 5 TelosB motes in simple tree topology • Compute fair rate for each node every 24hour • 1 solar panel(60W) and 1 wind turbine(400W) • Evaluation • sensing rate • Battery depletion

  21. Result II • PPF sets a higher sensing rate, run out 50% of all days • Forecast depletes less than 5% of all days, but maintain sensing rate 80% of PPF • Conservative sets a rate ~45% of PPF

  22. Conclusion • Weather prediction based on NWS forecast is more accurate than PPF • Validation forecast model with two case studies • Wind power generation of increase when solar power generation decrease

  23. Difference with us • Perpetual operating on the mountain • Deployed on the mountain with drastic weather change • Battery effect • Modeling based on historical solar radiation data not forecast cloudy data

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