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Wireless Sensor Networks for High Fidelity Sampling

Wireless Sensor Networks for High Fidelity Sampling. Sukun Kim Qualifying Examination Dec 1, 2005. High Fidelity Sampling. Three classes of WSN applications Monitoring environments Great duck island [11], Redwood forest [12] Focus on low-duty cycle and low power consumption

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Wireless Sensor Networks for High Fidelity Sampling

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  1. Wireless Sensor Networks for High Fidelity Sampling Sukun Kim Qualifying Examination Dec 1, 2005

  2. High Fidelity Sampling • Three classes of WSN applications • Monitoring environments • Great duck island [11], Redwood forest [12] • Focus on low-duty cycle and low power consumption • Monitoring objects – High Fidelity Sampling • machine health monitoring [13], condition-based monitoring, earthquake monitoring [14], structural health monitoring [15] • Focus on fidelity (quality) of sample • Interactions with space and objects • Lighting control [16] • Focus on control

  3. Structural Health Monitoring Challenges • High Fidelity Data • High Frequency Sampling with Low Jitter • Time Synchronized Sampling • Large-scale Multi-hop Network • Reliable Command Dissemination • Reliable Data Collection FTSP [8] Mint [9] Drip [10]

  4. Reliable Data Collection- Problem Statement • Every data from every node needs be collected to PC over a multi-hop network without loss • High throughput • Small number of packet injections to network • Overcome interference • Assumptions • Powerful receiver, resource constrained sender • Receiver (PC) can arbitrate flow • Low congestion • Low loss rate

  5. Hypothesis (Proposed Solution) • High Fidelity Data – Low-cost low-power MEMS accelerometer board with proper signal processing and calibration, produces data of meaningful fidelity • High Frequency Sampling with Low Jitter – WSN mote and TinyOS with guaranteed worst-case jitter, provide real time operation of meaningful level • Reliable Data Collection – Rate-based alternating-flow protocol with complex receiver and simple sender and pipelining, achieve reliable collection efficiently

  6. Table of Contents • Related Work • High Fidelity Data • High Frequency Sampling with Low Jitter • Reliable Data Collection • Future Work

  7. Structural Health Monitoring System Preliminary customized systems: Kruger, et al [1], Qiang, et al [2], Engel, et al [3], Caicedo, et al [4]

  8. TCP on Wireless Networks • Blind link-level-retransmission (LLR) can decrease throughput – DeSimone, et al [17] • Support for mobile host • I-TCP, Balakrishnan, et al [18] • Support for wireless ad-hoc network – WTCP, ATP • Rate-based transmission • Selective ACK contains congestion information • No sender timeout for retransmission – WTCP

  9. Reliable Transfer on WSN • Reliable diffusion • PSFQ, RMST, Garuda, Drip, Deluge • Congestion Control • ESRT, CODA, Fusion, Ee, et al [19] • Better best-effort convergence • RBC • Reliable convergence • Wisden • Sender sends data at static rate • In a routing tree, mote sends NACK to get missing packet from child for efficiency • PC sends NACK to source mote for e2e reliability • Incorrectly tuned rate and topology change make the network collapse • Compared to hardware, low bandwidth

  10. Table of Contents • Related Work • High Fidelity Data • In IWSHM ‘05 • High Frequency Sampling with Low Jitter • Reliable Data Collection • Future Work

  11. Accelerometer Board • Signal processing: averaging in software • Calibration for manufacturing variation and temperature • System noise floor: 30(μG/√Hz) • Gives desired quality in static, dynamic test • Two accelerometers for two axis • Thermometer, 16bit ADC, Low-pass filter ADXL 202E Silicon Designs 1221L

  12. Table of Contents • Related Work • High Fidelity Data • High Frequency Sampling with Low Jitter • In IWSHM ‘05 • Reliable Data Collection • Future Work

  13. Analysis of Jitter Sampling Other jobs like EEPROM write Time Non-preemptible portion Preemptible portion Probability • 1. Remove unnecessary blocking atomic section, interrupts • Turn off unnecessary components 2. Verify maximum blocking section is small enough P2/T2 P1/T1 Jitter 0 C W T2+C T1+C

  14. Verification of Jitter (6.67KHz) 10μs 0μs 0μs 10μs Time Series Histogram • Jitter is within 10µs (6.67%), 0.2% at 200Hz • Tradeoff: turning off radio • WSN mote and TinyOS are not inherently limited in real time operation • It is a matter of the hardness of real time requirement and the tradeoff for the loss of functionality

  15. Table of Contents • Related Work • High Fidelity Data • High Frequency Sampling with Low Jitter • Reliable Data Collection • Straw • Future Work

  16. Overview PC Mote • PC application arbitrates flow • Determines who sends when • Triggers one flow at a time • Adjust RTT, adjust transmission rate to avoid interference • Cross-layer information Application Application read(dest, *start, size) Straw Straw Multi-hop Routing Routing layer is assumed to deliver packets end-to-end

  17. Protocol 1. Data Request PC Mote Complex Simple • Selective NACK • No need for flow control, rate-based transmission • No congestion control • Pipelining, no link level retransmission • Alternating flow, no concurrent bidirectional flow 2. Data Transfer 3. Request missing holes 4. Transfer missing holes Selective NACK Straw Straw Rate = if (Depth < Interference Radius) then (UART Delay) + Depth * (Radio Delay) else (Interference Radius) * (Radio Delay)

  18. Optimization • Transfer the checksum of the whole data to guarantee the integrity • Parallelize reading from the memory and sending to the network Send Network Read Memory

  19. Test Result 93.2% 299B/s 91.8% 304B/s • 10KB of data • 500 packets • Mica2dot, 36 bytes/pkt • Comparison to routing layer • 630B/s for 1 hop • Up to 91.4% efficiency • 352B/s for 2 hops • Up to 86.4% efficiency 91.4% 296B/s 95.6% 560B/s 96.6% 576B/s * End-to-end Raw Reliability Effective Bandwidth (Byte/s)

  20. Channel Capacity Utilization • Hardware capacity limit • UART: 57.6Kbps • Radio: 19.2Kbps • 1 hop: 14.4Kbps • Measured actual capacity usage • UART: 27.8Kbps • Radio: 9.74Kbps • Routing: 5.46Kbps (1 hop) • Reliable: 4.7Kbps (1 hop) 33% Mica2, 36bytes/pkt

  21. 212.7μs 33% data transfer 24% overhead for transferring data 43% header transfer & overhead Mica2, 36bytes/pkt

  22. Effect of Packet Size on Bandwidth RAM • Doubled packet size: 36B  72B • Payload: 20B  56B (2.8 times) • Packets/sec: 29.4  20.9 (71%) • Bandwidth doubled: 588B/s  1172B/s* (1.99 times) • RAM usage jump from 3437B to 4733B for SHM application (Sentri) • 36 packet buffers • Basic services (Comm + TimeSync + Routing + Bcast + Reliable) can go beyond 4KB of RAM *Loss rate was 0.2%

  23. Why so much RAM (packet buffer)? 2. Forwarding Queue (20 out of 36) 1. At least one at each end Component (12 out of 36) Cmpnt6 Cmpnt5 Forward Cmpnt4 Straw Cmpnt3 Cmpnt2 Forward Routing Bcast Drip Cmpnt1 Forward Forward QueuedSend Sharing packet buffer? GenericComm Reliability of system versus Efficient use of resource Sensornet Network Layer

  24. Reliable Data Collection- Problem Statement Revisited • Every data from every node needs be collected to PC over a multi-hop network without loss • High throughput • Small number of packet injections to network • Overcome interference • Assumptions • Powerful receiver, resource constrained sender • Receiver (PC) can arbitrate flow • Low congestion • Low loss rate IF

  25. Testbed In SECON ‘04 Source Destination 5 hops, 26.28% link loss rate (78.23% E2E), 300 packets, separated by 1 sec, on BVR

  26. 8 original messages

  27. 8 original messages Overhead End-to-end retransmission (Straw): 8.6% at 96.6%(1hop) success rate 13.6% at 91.8%(2hops) success rate Erasure code: 12.5%, 25%, …

  28. Table of Contents • Related Work • High Fidelity Data • High Frequency Sampling with Low Jitter • Reliable Data Collection • Future Work

  29. Link Level Retransmission + Pipelining • Link level retransmission is effective when loss rate is high • Pipelining is effective for long path • Combining two can intensify interference - higher correlated losses Throughput = (e2e success rate) * (pkts/s at sender)

  30. Congestion Control • In case Straw is used together with constant upstream traffic, congestion control will be needed • Congestion control from the receiver • Include congestion information in NACK packet • Sender adjusts rate using congestion information

  31. quarter-span mid-span 260ft 9 7 5 1 2 11 16ft 12 Berkeley 14 SF Bay 10 8 13 4 3 L5 L4 L3 L2 L1 Base Station Deployment at Footbridge Using Sentri (structural health monitoring toolkit)

  32. Time plot, vertical sensors at L1-L5 Frequency plot, vertical sensors at L1-L5 8 V2 V2 V4 6 V13 V4 V7 V13 4 10 4 V9 V7 V9 2 Acceleration (mg) 0 2 10 abs(FFT(.)) -2 -4 0 10 -6 -8 0 1 2 3 4 5 6 7 8 9 10 -2 Time (sec) 10 0 2 4 6 8 10 12 14 16 18 20 Frequency (Hz) V9 V7 V2 Berkeley SF Bay V13 V4 Plots of calibrated data

  33. 1.00 0.74 0.19 -0.73 -0.99 Model Properties Match with SAP bridge model First Vertical Mode of Vibration

  34. Timeline • Mar 2006 for SenSys – Deployment on the Golden Gate Bridge • April 2006 – Study correlation between (link level retransmission + pipelining) and interference • Jul 2006 – Implement link level retransmission + pipelining • Dec 2006 – Congestion control with rate adjustment

  35. Questions and Discussions

  36. Backup Slides

  37. References [1] M. Kruger and C. U. Grosse. Structural health monitoring with wireless sensor networks. Otto-Graf-Journal, 15:77–90, 2004. [2] P. Qiang, G. Xun, and Z. Chang-you. A wireless structural health monitoring system in civil engineering. The Third International Conference on Earthquake Engineering (3ICEE), Nanjing, China, October 18-20, 2004. [3] J. M. Engel, L. Zhao, Z. Fan, J. Chen, and C. Liu. Smart brick - a low cost, modular wireless sensor for civil structure monitoring. International Conference on Computing, Communications and Control Technologies (CCCT 2004), Austin, TX USA, August 14-17, 2004. [4] J. M. Caicedo, J. Marulanda, P. Thomson, and S. J. Dyke. Monitoring of bridges to detect changes in structural health. the Proceedings of the 2001 American Control Conference, Arlington, Virginia, June 2527, 2001. [5] B. S. Jr., M. Ruiz-Sandoval, and N. Kurata. Smart sensing technology: Opportunities and challenges. Journal of Structural Control and Health Monitoring, in press, 2004. [6] J. P. Lynch. Overview of wireless sensors for real-time health monitoring of civil structures. Proceedings of the 4th International Workshop on Structural Control (4th IWSC), New York City, NY, USA, June 10-11, 2004. [7] N. Xu, S. Rangwala, K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin. A wireless sensor network for structural monitoring. the Proceedings of the ACM Conference on Embedded Networked Sensor Systems, November 2004.

  38. References (continued) [8] A. DeSimone, M. C. Chuah, O. Yue, Throughput performance of transport-layer protocols over wireless LANs. In Proceedings of IEEE Globecom 93, Houston, USA, 1993. [9] A. Bakre, B. R. Badrinath, I-TCP: indirect TCP for mobile hosts, Proceedings of the 15th International Conference on Distributed Computing Systems (ICDCS'95). [10] H. Balakrishnan, S. Seshan, and R. H. Katz, Improving reliable transport and handoff performance in cellular wireless networks. ACM Wireless Networks, December 1995.

  39. Reliable Data Collection- Problem Statement • Every data from every node needs be collected to PC over a multi-hop network without loss in a way that gives high throughput with small number of packet injections • The collection must overcome interference with the flow in the same and the opposite direction

  40. Reliable Data Collection- Problem Statement • Every data from every node needs be collected to PC over a multi-hop network without loss in a way that gives high throughput with small number of packet injections • The collection must overcome interference with the flow in the same and the opposite direction Data ACK or NACK

  41. Accelerometer Board Design • Two accelerometers for two axis • Thermometer • 16bit ADC ADXL Silicon Designs

  42. Signal Processing • As an analog signal processing low-pass filter is used, which filters high frequency noise • Low-pass filter with threshold frequency 25Hz is used • As a digital signal processing, averaging is used • If noise follows Gaussian distribution, by averaging N numbers, noise decreases by a factor of sqrt(N)

  43. Sensor Calibration

  44. Temperature Calibration F C 81.1 27.3 67.1 19.5 53.0 11.7 Temperature 3.9 39.0 mG 27.5 0 Thanks to Crossbow -27.5 Acceleration

  45. Power Consumption • 3 of Tadiran 5930 (lithium-ion, 3.6V, 19Ah, $17, D size) are used

  46. Power Consumption (cont) • With optimal sleeping, 30 days • Board itself consumes significant amount of energy Power source Power source Switch Switch Sensor Mote Mote ADC Sensor ADC

  47. Verification of Jitter – Time Series (1KHz, 5KHz, 6.67KHz) 10μs 0μs • Peak to Peak is time to fill up buffer • Spiky portion is time to write buffer to flash • Can sample as long as the former is larger than the latter

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