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Quality-aware Data Collection in Energy Harvesting WSN

Quality-aware Data Collection in Energy Harvesting WSN. Nga Dang Elaheh Bozorgzadeh Nalini Venkatasubramanian University of California, Irvine. Outline. Introduction Energy harvesting Wireless Sensor Network Energy Harvesting Renewable Energy Energy Harvesting WSN

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Quality-aware Data Collection in Energy Harvesting WSN

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  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/Everlast Heliomote: powering Mica/Telos Prometheus: Self-sustaining Telos Mote

  6. Battery-operated vs. Energy Harvesting WSN • Basic Comparison

  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. SimunicRosing, 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 • 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 WSNExperiment 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

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