1. Quality-aware Data Collection in Energy Harvesting WSN Nga Dang Elaheh Bozorgzadeh Nalini Venkatasubramanian University of California, Irvine

2. Outline Introduction Energy harvesting Wireless Sensor Network Energy Harvesting Renewable Energy Energy Harvesting WSN Battery-operated vs. Energy Harvesting WSN Wireless Sensor Network Data Collection Quality of services Case study Approximated Data Collection Experiment

3. Introduction Energy harvesting Green design: harvesting energy from surrounding environments It’s not new! Wireless sensor network Data Collection Green use Replace battery Harvest renewable energy Self-sustainable

4. Renewable Energy Energy sources from natural or surrounding environments In 2006, 18% of global final energy consumption came from renewables (biomass and hydroelectricity) New renewables are growing rapidly Energy sources: wind, solar, motion, vibration, thermal Large scale systems: windmills, buildings Small scale systems: Wireless sensor motes Is it possible?

5. Energy Harvesting WSN Motes capable of harvesting solar and wind Ambimax/EverlastHeliomote: powering Mica/Telos Prometheus: Self-sustaining Telos Mote

6. Battery-operated vs. Energy Harvesting WSN Basic Comparison FeaturesBattery-Operated WSNEnergy Harvesting WSN Energy SourceCharged batterySurrounding environment Maintenance costHigh, require frequent recharge and replacement of battery Low, self-sustaining RequirementEnergy efficient, Long-life battery Energy-neutral Quality of serviceAs low as possible/acceptable As high as possible PredictabilityHigh, battery modelsLow, fluctuation

7. Energy Harvesting Prediction Solar energy is predictable “Adaptive Duty Cycling for Energy Harvesting Systems”,Jason Hsu et. al, International Symposium of Low Power Electrical Design’06 “Solar energy harvesting prediction algorithm”, J. Recas, C. Bergonzini, B. Lee, T. Simunic Rosing, Energy Harvesting Workshop, 2009 History data, seasonal trend, daily trend, weather forecast Prediction every 30 minutes with high accuracy

8. Outline Introduction Energy harvesting Wireless Sensor Network Energy Harvesting Renewable Energy Energy Harvesting WSN Battery-operated vs. Energy Harvesting WSN Wireless Sensor Network Data Collection Quality of services Case study Approximated Data Collection Experiment

9. Wireless Sensor Network Components: Server with unlimited resource and processing power Sensor mote with small processor, embedded sensor, ADC channels, radio circuitry and Battery! Data Collection – Each node records sensor value and sends update to base station – Server receives external queries, asking data from sensor nodes – Communication is costly – Battery capacity is limited Queries

10. Quality of Services Accuracy of data Query responsiveness Event-triggered quality requirement Emergencies: fire, explosion Threshold-based: high temperature vs. low temperature, humid vs. dry Timing-based: day vs. night Security-based: tracking authority vs. non-authority Energy Harvesting WSN Prediction of energy harvesting Use energy in a smart way to achieve best quality of services

11. Outline Introduction Energy harvesting Wireless Sensor Network Energy Harvesting Renewable Energy Energy Harvesting WSN Battery-operated vs. Energy Harvesting WSN Wireless Sensor Network Data Collection Quality of services Case study Approximated Data Collection Experiment

12. Approximated Data Collection Exploit error tolerance/margin Lots of applications can tolerate a certain degree of error Example: temperature of a given region (+/- 2 Celsius) Approximated Data Collection For each sensor data: e is a given margin u is value reading on sensor node v is cached value on server node Requirement: Battery-operated Maintain minimum data accuracy Minimize energy consumption to Energy harvesting WSN Adapt accuracy level according to available energy harvesting Distribute/spend energy in a smart way to maximize data accuracy |v – u| < e

13. Battery-operated WSN Experiment results Simulator results Maintain minimum data accuracy Minimize communication cost Low energy utilization 7% - 50%

14. Energy harvesting WSN Experiment Results Energy distribution Choose error bound that fits available energy level Qualitative data: error bound as low as 0.0 (100% accurate) Energy utilization: 26% - 75%

15. Future work Set up harvesting energy in our infrastructure Implement our energy harvesting management framework on this system for application requiring quality of services Carry out extensive field testing