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Application Drivers: Energy Consumption in Wireless Sensor Networks

Application Drivers: Energy Consumption in Wireless Sensor Networks. Kris Pister Prof. EECS, UC Berkeley. Outline. The Swarm Sources, storage, consumption Standards: Network types & usage models Looking forward, looking back. Vision 2010.

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Application Drivers: Energy Consumption in Wireless Sensor Networks

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  1. Application Drivers:Energy Consumption inWireless Sensor Networks Kris Pister Prof. EECS, UC Berkeley

  2. Outline • The Swarm • Sources, storage, consumption • Standards: Network types & usage models • Looking forward, looking back

  3. Vision 2010 1997 Question: What happens if sensors become tiny and wireless?

  4. Vision 2030 • Integrated components will be approaching molecular limits and/or may cover complete walls • Every object will have a wireless connection • The “trillions of radios story” will be a reality • The ensemble is the function • Function determined by availability of sensing, actuation, connectivity, computation, storage and energy • This brings virtualization to a new level

  5. The Swarm at The Edge of the Cloud TRILLIONS OF CONNECTED DEVICES Infrastructural core THE CLOUD THE SWARM [J. Rabaey, ASPDAC’08]

  6. The Swarm Perspective Moore’s Law Revisited: Scaling is in number of connected devices, no longer in number of transistors/chip The functionality is in the swarm! Resources can be dynamically provided based on availability It’s A Connected World Time to Abandon the “Component”-Oriented Vision [J. Rabaey, MuSyC 2009]

  7. Swarm Potentials “Tiny devices, chirping their impulse codes at one another, using time of flight and distributed algorithms to accurately locate each participating device. Several thousands of them form the positioning grid … Together they were a form of low-level network, providing information on the orientation, positioning and the relative positioning… It is quite self-sufficient. Just pulse them with microwaves, maybe a dozen times a second …” Pham Trinli, thousands of years from now Vernor Vinge, “A Deepness in the Sky,” 1999

  8. The Swarm Lab: A Whole Floor in Cory Hall POST-SILICON LAB Enabled by the move of the microlab to Sutardja-Dai Hall (Marvell Lab)

  9. The Swarm HiveAn Incubator for Swarm Applications and Platforms • Integrating our strengths in advanced sensing, innovative post-silicon substrates and packaging, ultra-low power computing and communications, wireless links and networks, and distributed systems … • To create entirely novel swarm solutions to applications such as the Unpad, health care, smart energy management, security, … In a multi-disciplinary open lab-workspace setting In close collaboration with other Berkeley Labs such as CITRIS, BWRC, BSAC, COINS, Marvell Lab, …

  10. Many Sources • Solar • Thermal • Vibration • Button-press • Electric fields • Magnetic fields • RF • Air flow • Hydrostatic pressure • Blood sugar • Bio-mechanical • Free hydrogen • … • Availability • Lifetime • Equivalent battery lifetime • @ same cost, same power

  11. Storage • Batteries • Temperature • Self discharge • Capacity vs. cycles, depth of discharge • Capacitors • Temperature • Leakage • $/J

  12. Hardware Requirements • Sensor+Analog • Energy/sample = power*(sample time) • Often either power or sample time is small • Microprocessor+Memory • Active – rarely dominant (1024 pt FFT?) • Leakage • Radio • Transmit • Receive

  13. Protocol Integration Application  Presentation  Session  Transport  Network  Data-Link  Physical  HTTP, SSH, Telnet, FTP “other” CoAP, XML, OpenADR, … IETF UDP ,TCP WSN RDP? RoLL RPL IPv6 IEEE802.3 IEEE802.11 6LoWPAN 802.15.4, 4e IEEE 802.15.4 Tomorrow’s Internet of Things Today’s Internet

  14. Protocol Requirements • PHY – Physical: RF band, bit rate, modulation, power • 802.15.4: 2.4GHz, 250kbps, OQPSK, 0..10 dBm • MAC – Medium Access • The layer with the biggest impact on power • Always listening • Long preambles • Time synchronized • NET+TRAN – Networking and Transport • Typically <1% uP • Impact is on number of packets sent • Route discovery & maintenance, end-to-end ACKs • APP – Application • Binary vs. XML coding

  15. Zigbee • The big three • Zigbee Pro / SE1.0 • Zigbee RF4CE • Home entertainment control • Guarantees that cell phones will have 15.4 radios • Zigbee IP / SE2.0 • http, TCP, TLS, DHCP, … • Zigbee Green Power • All use powered routers • Interoperability • Routing • Provisioning 17

  16. Zigbee • Powered Routers • RX current dominates • Scavengers: find lowest RX current • Low Power Leaf Nodes • TX current dominates above 1pkt/min • Scavengers: find lowest TX energy per packet • Leakage current dominates below 0.5pkt/min • Scavengers: don’t bother. Use a coin cell or • Scavengers: real opportunity – be efficient at < 1mW

  17. Mote-on-chip radio current vs. sample date RX Current 0dBm TX Current CEL Freescale Ember Ember MSP430 +CC2420 TI TI Freescale Jennic Jennic Dust Networks Dust Networks 19

  18. Time Synchronized Mesh Protocol (TSMP & TSCH) • Basis of several Industrial Automation Standards • IEC 62591 (WirelessHART) • ISA100.11A • WIA-PA (China) • MAC is standardized in 802.15.4E (TSCH) • Multiple network vendors: Dust, Nivis, STG, … 20

  19. Power Sources: Battery & Energy Scavenging Siemens GE • Battery • 4-20 mA loop • Solar Emerson • Battery • 4-20 mA loop • Thermal • Battery • Vibration • Routing node power: 50uA…100uA @ 3.6V • C-cell lithium lifetime: 7 years • Scavenger lifetime: ? 21

  20. Power-optimal communication A B A wakes up and listens B transmits B receives ACK A transmits ACK Worst case A/B clock skew • Assume all motes share a network-wide synchronized sense of time, accurate to << 1ms • For an optimally efficient network, mote A will only be awake when mote B needs to talk Expected packet start time 22

  21. Packet transmission and acknowledgement Radio TX startup ACK RX Packet TX Radio TX/RX turnaround Mote Current (2011): 50 mJ (2007): 200 mJ Energy cost (2003): 800 mJ 23

  22. Idle listen (no packet exchanged) Empty receive Radio RX startup Mote Current (2011): 15 mJ (2007): 60 mJ Energy cost (2003): 200 mJ 24

  23. TSCH Power (2009) • Leaf nodes • Report 1/min • 20mA • Report 1/sec • 130mA • Routing nodes • 1child, 1/min reporting • 36mA • 5 descendents, 1/min reporting • 84mA • 10 descendents, 10 second sample rate • 220mA

  24. (2002) Power and Energy • Sources • Solar cells ~0.1mW/mm2, ~1J/day/mm2 • Combustion/Thermopiles • Storage • Batteries ~1 J/mm3 • Capacitors ~0.01 J/mm3 • Usage • Digital computation: nJ/instruction • Analog circuitry: nJ/sample • Communication: nJ/bit 10 pJ 20 pJ/sample 11 pJ RX, 2pJ TX (optical) 10 nJ/bit RF

  25. (2002) Projected RF mote capabilities • 10s of meters range at 100 kbps • Encryption • Pair-wise time of flight ranging ~ 1m • Time synchronization to • ~ ns pair-wise • ~ ms locally • ~ ms entire network • ~ ppm drift

  26. (2002) Energy and Lifetime • 1 mAh ~= 1 micro*Amp*month (mAm) • Lithium coin cell: 220 mAm (CR2032, $0.16) • AA alkaline ~ 2000 mAm • 100kS/s sensor acquisition: 2mA • 1 MIPS custom processor: 10mA • 100 kbps, 10-50 m radio: 300mA • 1 month to 1 year at 100% duty • 10 year lifetime w/ coin cell  1% duty • Sample, think, listen, talk, forward… 10 times/second!

  27. (2002) ~8mm3 laser scanner Two 4-bit mechanical DACs control mirror scan angles. ~6 degrees azimuth, 3 elevation 1Mbps

  28. (2002) Theoretical Performance 5m Ptotal = 100uW Pt = 10uW q½ = 1mrad BR = 5 Mbps Areceiver = 0.1mm2 Pr = 10nW (-50dBm) Ptotal = 50uW SNR = 15 dB 20pJ/bit!

  29. Conclusion • Energy scavenging Wireless Sensor Networks are in production deployments today • Energy per operation is 10mJ --100mJ in production • 10nW * 20 minutes is 10mJ • There’s at least another order of magnitude reduction still to come

  30. Evolving information flow in WSN DB Business logic Custom APP APP Manager LBR IPv6, native DB fmt. Proprietary network & data fmt. Network stack Network stack Application Serial API Sensor mP Sensor Application Sensor 32

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