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Challenge: Ultra-Low-Power Energy-Harvesting Active Networked Tags ( EnHANTs )

Challenge: Ultra-Low-Power Energy-Harvesting Active Networked Tags ( EnHANTs ). Authors Maria Gorlatova , Peter Kinget , Ioannis Kymissis , Dan Rubenstein, Xiaodong Wang, Gil Zussman Presenter Velin Dimitrov. EnHANTS. Small Flexible Self-Reliant (energy)

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Challenge: Ultra-Low-Power Energy-Harvesting Active Networked Tags ( EnHANTs )

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  1. Challenge: Ultra-Low-Power Energy-Harvesting Active Networked Tags (EnHANTs) Authors Maria Gorlatova, Peter Kinget, IoannisKymissis, Dan Rubenstein, Xiaodong Wang, Gil Zussman Presenter Velin Dimitrov

  2. EnHANTS • Small • Flexible • Self-Reliant (energy) • Attached to non-networked objects • Books, Clothing, Produce, etc.

  3. EnHANTs Between sensor networks and RFID tags in capabilities

  4. Why EnHANTs? RFID -> Identify EnHANTs -> Actively search Enables continous monitoring and querying Misplaced book detector Detect people in disasters

  5. Internet of Things “enablers for the Internet of Things” Today computers—and, therefore, the Internet—are almost wholly dependent on human beings for information. Nearly all of the roughly 50 petabytes (a petabyte is 1,024 terabytes) of data available on the Internet were first captured and created by human beings—by typing, pressing a record button, taking a digital picture or scanning a bar code. Conventional diagrams of the Internet ... leave out the most numerous and important routers of all - people. The problem is, people have limited time, attention and accuracy—all of which means they are not very good at capturing data about things in the real world. And that's a big deal. We're physical, and so is our environment ... You can't eat bits, burn them to stay warm or put them in your gas tank. Ideas and information are important, but things matter much more. Yet today's information technology is so dependent on data originated by people that our computers know more about ideas than things. If we had computers that knew everything there was to know about things—using data they gathered without any help from us—we would be able to track and count everything, and greatly reduce waste, loss and cost. We would know when things needed replacing, repairing or recalling, and whether they were fresh or past their best. The Internet of Things has the potential to change the world, just as the Internet did. Maybe even more so.

  6. Capabilities Network – Multihop Ultra Low Power – nJ/bit Harvest Energy Energy Adaptive Exchange messages (IDs) Trasmit 1-10m Thin and flexible

  7. Existing Research • Scheduling of query responses of RFID • Sensor networks • IEEE 802.15.4a • Based on Impulse Radio UWB • Ranging • 26 Mb/s • Backward Compatible

  8. Existing Research Dynamic activation of energy-harvesting sensors Outsourcing packet retransmissions of energy restricted nodes Texas Instruments solar energy harvesting

  9. Energy Harvesting Temperature Differences Electromagnetic Energy Airflow Vibrations Solar Motion

  10. Rigid Solar Cells Solar – 100 to 0.1 mW/cm^2 Office/retail/lab settings brighter than residential Single and polycrystalline solar cells 10-20% efficient Efficiency depends on energy availability

  11. Flexible Solar Cells Organic semiconductors Constant efficiency w.r.t brightness 1.5-1% efficiency

  12. Solar Energy Use Example 10 cm^2 organic semiconductor cell Outdoors -> 10 mW Single bit -> 1nJ Achievable data rate is 10 Mb/s Indoors -> 10 uW Achievable data rate is 10 Kb/s

  13. Piezoelectric Harvesting Energy captured mechanically Users have some control PVDF – 4 uJ/cm^2 per 1.5% deflection

  14. Piezoelectric Example 10 cm^2 of material Strained 60 times/sec Generates 2.4 mW At 1nJ/bit -> 2.4 Mb/s

  15. Energy Storage • Compact/efficient, low self discharge • Ability to measure and control • Rechargeable batteries • Thin-film batteries -> flexible • Supplied voltage must exceed internal chemical potential to charge • Voltage upconversion

  16. Energy Storage Capacitors Receive any voltage exceeding Cycle time More difficult to add charge as the capacitor reaches capacity Self discharging Battery -> 1000 J/cm^3 Capacitor -> 1-10 J/cm^3

  17. Energy Abstraction C -> Energy storage capacity E -> Current energy level r -> Energy charge rate e -> Energy consumption rate

  18. Communciations • UWB impulse radio (IR) • Current state of art (2009) • 50 pJ/bit transmit • 500 pJ/bit receive • 100 Kb/s to 1 Mb/s

  19. Communications Paradigm Shift Conventional modulated systems require transmitter active for entire duration of signal transmission UWB uses pulses so transmit requires very little power No difference in listening to media and receiving information

  20. Challenge: Inaccurate Clocks Accurate clocks consume too much energy Ultra low power ring oscillators can be used but drift significantly Protocol redesign? Clock redesign?

  21. High Power Mode • Spend more energy when it is available • More accurate clocks • Help other tags synchronize • Run radios more to discover nodes • Open research area

  22. Independent State - Communications Tags do not maintain contact Energy use is decided on a per-tag basis Transmit/Receive duty cycling Transmit only mode for low energy

  23. Paired State - Communications If tags receive and transmit cycles align, they can pair Similar to low power modes of IEEE 802.11 and Bluetooth Keep-alives are the bursts Burst frequency <-> clock drift

  24. Communicating State Tags exchange (C,E,r,e) Low energy dictates how much of this info can be exchanged How this occurs exactly is key to minimizing energy

  25. Multiple EnHANTs • Leverage the environment • Synchronization via a channel always open • Can be used to influence joint energy decisions • Higher layer challenges • Polling/Pushing • Security/Privacy

  26. Energy Management and Optimization • Deterministic model • Periodic energy source • Office lights • Highest energy consumption rate:

  27. Economic Production Quantity

  28. Example 10 cm^2 solar cell with 1% efficiency Dim shelf -> 50 mW/cm^2 Maximum energy consumption 2.08 uW Translates to 2.08 Kb/s throughout day at 1nJ/bit

  29. Stochastic Model Order point, order quantity model

  30. Differences with Inventory • More random, fewer known parameters • Storage capacity • Cannot manufacture energy, can manufacture goods • Both supply and demand are stochastic • Dependency of energy means tags are like a network of factories

  31. EnHANT Prototype Custom UWB IR transceiver on MICA2 Synchronized OOK 2.18 nJ/bit 18 kbps at 1-3 meters Bit error rate under 0.001 CSMA Energy harvesting module -> battery, solar cell, and charging circuit

  32. EnHANT Prototype

  33. Testbed

  34. EnOcean  ISO/IEC 14543-3-10 30 m range Wireless light switches Solar 14 byte packets 125 kbps $1.4 B sales in 2013

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