DVFS for Energy Harvesting Systems

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					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
   Battery-operated vs. Energy Harvesting WSN
 Wireless Sensor Network
   Data Collection
   Quality of services
 Case study
   Approximated Data Collection
   Experiment
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
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?
Energy Harvesting WSN
 Motes capable of harvesting solar and wind




   Ambimax/Everlast                          Heliomote: powering Mica/Telos




                  Prometheus: Self-sustaining Telos Mote
Battery-operated vs. Energy Harvesting
WSN
 Basic Comparison

    Features             Battery-Operated WSN Energy Harvesting
                                              WSN
    Energy Source        Charged battery            Surrounding
                                                    environment
    Maintenance cost     High, require frequent     Low, self-sustaining
                         recharge and replacement
                         of battery
    Requirement          Energy efficient,          Energy-neutral
                         Long-life battery
    Quality of service   As low as                  As high as possible
                         possible/acceptable
    Predictability       High, battery models       Low, fluctuation
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
                                                    Solar Energy
                1200

                1000

                 800

                 600
                                                                                                                          Global Irr
                 400

                 200

                   0
                        1
                            201
                                  401
                                        601
                                              801
                                                    1001
                                                           1201
                                                                  1401
                                                                         1601
                                                                                1801
                                                                                       2001
                                                                                              2201
                                                                                                     2401
                                                                                                            2601
                                                                                                                   2801




                 -200
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
   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
                                                    Queries
  – Communication is costly
  – Battery capacity is limited
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
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
     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:
                           |v – u| < e
• 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
         Battery-operated WSN
         Experiment results
          Simulator results
          Maintain minimum data accuracy
          Minimize communication cost
          Low energy utilization 7% - 50%

                           Energy Utilization                                                Error of data
         700000                                                             6

         600000                                                             5
         500000
                                                                            4
         400000
Energy




                                                  Energy Harvest
                                                                            3
         300000                                   Energy Consume
                                                  Linear (Energy Consume)   2
         200000

         100000                                                             1

              0                                                             0
                  1   6 11 16 21 26 31 36 41 46                                 1   6   11    16   21   26   31   36   41   46
                  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%

                           Energy Utilization                                         Error of data
         700000
                                                                     6
         600000
                                                                     5
         500000
                                                                     4
         400000
Energy




                                                    Energy Harvest   3
         300000
                                                    Energy Consume
         200000                                                      2

         100000                                                      1
              0
                                                                     0
                  1   6   11 16 21 26 31 36 41 46
                                                                         1   6   11   16   21   26   31   36   41   46
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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posted:7/14/2011
language:English
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