Communications and Energy-Harvesting in Nanosensor Networks

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Presented at NSF Workshop on Biological Computations and Communications by
Michele Weigle
November 9, 2012
Boston, MA

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  • waves of visible light oscillate with a period of ~ 2 fs200 fs – the swiftest chemical reactions
  • Communications and Energy-Harvesting in Nanosensor Networks

    1. 1. Communications and Energy-Harvesting in NanosensorNetworks Michele C. Weigle Intelligent Networking and Systems Lab (iNetS) Department of Computer Science Old Dominion University Norfolk, VA NSF Workshop on Biological Computations and Communications November 9, 2012
    2. 2. My Background• Vehicular Networking – the use of vehicles as sensors to detect traffic incidents on the road• Sensor Networks for Emergency Assistance – re-tasking existing sensor networks for use in emergency situations – investigating energy issuesNovember 9, 2012 NSF Workshop on Biological Computation and Communications 2
    3. 3. Why Not Go Smaller?November 9, 2012 NSF Workshop on Biological Computation and Communications 3
    4. 4. Nanosensor Networks• Framework articulated by Ian Akyildizs group at Georgia Tech• Investigated network properties, coding, MAC protocols, energy harvesting• Were just getting started, building on their work (many images from Akyildizand Jornet)November 9, 2012 NSF Workshop on Biological Computation and Communications 4
    5. 5. Applications of NanosensorNetworks• Biomedical• Environmental• Industrial and consumer goodsNovember 9, 2012 NSF Workshop on Biological Computation and Communications 5
    6. 6. Nanosensor Networks• Inspired by biological nanoscale networks• Communication – molecular – electromagnetic - our focusNovember 9, 2012 NSF Workshop on Biological Computation and Communications 6
    7. 7. Electromagnetic Communication• Graphene-based nanoantenna – graphenenanoribbons (GNR) formed by unzipping carbon nanotubes (CNT) http://www.jmtour.com/images/NatureUnzippingImages/TubeUnzipping.png• Radiates waves in the terahertz (0.1-10 THz) bandNovember 9, 2012 NSF Workshop on Biological Computation and Communications 7
    8. 8. Terahertz Band http://www.utdallas.edu/news/imgs/photos/terahertz-gap-graph-375_1.jpg• Supports very high transmission rates in the short range – up to a few terabits per second – distances below 1 meterNovember 9, 2012 NSF Workshop on Biological Computation and Communications 8
    9. 9. Pulse-Based Communication• Not feasible to generate high-power carrier signal used in classical communications – motivates the need for pulse-based communication• Femtosecond-long pulses (10-15second) proposed• This introduces major changes in classical networking protocolsNovember 9, 2012 NSF Workshop on Biological Computation and Communications 9
    10. 10. Jornet and AkyildizTS-OOK IEEE ICC, 2011(Time Spread On-Off Keying)• Example Encoding – 1 - 100 fs (0.1 picosecond) pulse – 0 - silence – 50 ps between bitsNovember 9, 2012 NSF Workshop on Biological Computation and Communications 10
    11. 11. TS-OOK Example• With femtosecond pulses, probability of collision is almost non-existent – senders transmit when they have data ready• With long inter-bit times, multiple senders can interleave transmissionsNovember 9, 2012 NSF Workshop on Biological Computation and Communications 11
    12. 12. Communication and Power• Max capacity of nano-battery - 800 pJ• Transmission of single pulse - 1 pJ• Reception of a single pulse - 0.1 pJNovember 9, 2012 NSF Workshop on Biological Computation and Communications 12
    13. 13. Message Coding• Encode the message such that there are more 0s transmitted than 1s – 0 is silence, costs no energy original 3-bit packet• Code weight (2 bits) (weight = 0.25) – average portion of 1s 00 000 01 001• Lower code weight, 10 010 more bits 11 100November 9, 2012 NSF Workshop on Biological Computation and Communications 13
    14. 14. Energy Harvesting• Nanosensors have the potential to harvest energy from their surroundings – solar, thermal, electromagnetic, vibration• Vibration seems to be the best method for nanosensors• Allows nanosensors to re-charge themselvesNovember 9, 2012 NSF Workshop on Biological Computation and Communications 14
    15. 15. Energy Harvesting• Time to charge depends on vibration rate (needs 2500 cycles to charge) – A/C vents (50 Hz) =~ 50 sec – human heart beat (1 Hz) =~ 42 min• Charging time is not linear• Arrival of energy is not predictable in all scenarios November 9, 2012 NSF Workshop on Biological Computation and Communications 15
    16. 16. Impact on Communication• Energy harvesting phase is orders of magnitude larger than communication phase• End-to-end delay significantly affected if forwarding nodes need to recharge before forwarding packetNovember 9, 2012 NSF Workshop on Biological Computation and Communications 16
    17. 17. Other Limitations• Limited resources (memory, power) for storage and modulation• Significant molecular absorption of pulses – expensive energy needed for retransmission – limited resources for error correction• Dense network scenarios (100 nodes in 1 cm2) need special multi-hop designNovember 9, 2012 NSF Workshop on Biological Computation and Communications 17
    18. 18. Our Focus • Model communications and energy-harvesting process • Develop and evaluate strategies for coding, packet size, bit repetition, and packet retransmission to produce efficient and power- aware network transmissionsJoint work with PhD student ShahramMohrehkesh and Dr. Stephan Olariu November 9, 2012 NSF Workshop on Biological Computation and Communications 18
    19. 19. Our Road Ahead• Were just at the beginning of our investigation• Development of customized protocol layers – pulse-based communication models • coding methods to send fewer 1s • error correction/detection methods: repetition, LDPC, hamming – energy harvesting-aware • MAC protocol • packet scheduling • packet formation – optimized model for throughput and delay, end2end delivery, reliability• Development of simulation environmentNovember 9, 2012 NSF Workshop on Biological Computation and Communications 19
    20. 20. Communications and Energy-Harvesting in Nanosensor Networks Michele C. WeigleIntelligent Networking and Systems Lab (iNetS) Department of Computer Science Old Dominion University Norfolk, VA mweigle@cs.odu.edu http://www.cs.odu.edu/inetsNSF Workshop on Biological Computations and Communications November 9, 2012

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