Quality-aware Data Collection in Energy Harvesting WSN Nga Dang Elaheh Bozorgzadeh Nalini Venkatasubramanian University of California, Irvine
Quality-aware Data Collection in Energy Harvesting WSN Nga Dang Elaheh Bozorgzadeh Nalini Venkatasubramanian University of California, Irvine.
Dec 23, 2015
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Quality-aware Data Collection in Energy Harvesting WSN
Nga DangElaheh Bozorgzadeh
Nalini VenkatasubramanianUniversity of California, Irvine
OutlineIntroduction
Energy harvestingWireless Sensor Network
Energy HarvestingRenewable EnergyEnergy Harvesting WSNBattery-operated vs. Energy Harvesting WSN
Wireless Sensor NetworkData CollectionQuality of services
Case studyApproximated Data CollectionExperiment
IntroductionEnergy harvesting
Green design: harvesting energy from surrounding environments
It’s not new!
Wireless sensor networkData CollectionGreen use
Replace batteryHarvest renewable energySelf-sustainable
Renewable Energy Energy sources from natural or surrounding
environmentsIn 2006, 18% of global final energy consumption
came from renewables (biomass and hydroelectricity)New renewables are growing rapidly
Energy sources: wind, solar, motion, vibration, thermalLarge scale systems: windmills, buildingsSmall scale systems: Wireless sensor motes
Is it possible?
Energy Harvesting WSNMotes capable of harvesting solar and wind
Ambimax/Everlast Heliomote: powering Mica/Telos
Prometheus: Self-sustaining Telos Mote
Battery-operated vs. Energy Harvesting WSNBasic Comparison
Features Battery-Operated WSN
Energy Harvesting WSN
Energy Source Charged battery Surrounding environment
Maintenance cost
High, require frequent recharge and replacement of battery
Low, self-sustaining
Requirement Energy efficient,Long-life battery
Energy-neutral
Quality of service
As low as possible/acceptable
As high as possible
Predictability High, battery models
Low, fluctuation
Energy Harvesting PredictionSolar 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 forecastPrediction every 30 minutes with high accuracy
OutlineIntroduction
Energy harvestingWireless Sensor Network
Energy HarvestingRenewable EnergyEnergy Harvesting WSNBattery-operated vs. Energy Harvesting WSN
Wireless Sensor NetworkData CollectionQuality of services
Case studyApproximated Data CollectionExperiment
Wireless Sensor NetworkComponents:
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
Quality of ServicesQuality of Services
Accuracy of dataQuery responsivenessEvent-triggered quality requirement
Emergencies: fire, explosionThreshold-based: high temperature vs. low
temperature, humid vs. dryTiming-based: day vs. nightSecurity-based: tracking authority vs. non-authority
Energy Harvesting WSNPrediction of energy harvestingUse energy in a smart way to achieve best
quality of services
OutlineIntroduction
Energy harvestingWireless Sensor Network
Energy HarvestingRenewable EnergyEnergy Harvesting WSNBattery-operated vs. Energy Harvesting WSN
Wireless Sensor NetworkData CollectionQuality of services
Case studyApproximated Data CollectionExperiment
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
Battery-operated WSN Experiment results
Simulator results Maintain minimum data accuracy Minimize communication costLow energy utilization 7% - 50%
Energy harvesting WSNExperiment Results
Energy distributionChoose error bound that fits available energy levelQualitative data: error bound as low as 0.0 (100%
accurate)Energy utilization: 26% - 75%
Future workSet up harvesting energy in our
infrastructureImplement our energy harvesting
management framework on this system for application requiring quality of services
Carry out extensive field testing
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