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C-MAC: Model-driven Concurrent Medium Access Control for Wireless Sensor Networks

C-MAC: Model-driven Concurrent Medium Access Control for Wireless Sensor Networks. Mo Sha ; Guoliang Xing ; Gang Zhou ; Shucheng Liu ; Xiaorui Wang City University of Hong Kong; Michigan State University College of William and Mary; University of Tennessee Knoxville. Outline. Motivation

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C-MAC: Model-driven Concurrent Medium Access Control for Wireless Sensor Networks

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  1. C-MAC: Model-driven Concurrent Medium Access Control for Wireless Sensor Networks Mo Sha ; Guoliang Xing ; Gang Zhou ; Shucheng Liu ; Xiaorui Wang City University of Hong Kong; Michigan State University College of William and Mary; University of Tennessee Knoxville

  2. Outline • Motivation • Power control and interference models • Design of C-MAC • Performance evaluation

  3. Data-intensive Sensing Applications • Habitat monitoring, structural monitoring etc. • Sample the environment at high rates • Ex: sample @100 Hz for finding structural defect • Limited storage capacity • High network throughput

  4. MACs for Wireless Sensor Nets • CSMA-based MAC protocols • S-MAC, T-MAC, B-MAC,X-MAC… • Conservative, low throughput • TDMA-based MAC protocols • TRAMA, DCQS, DRAND… • High maintenance overhead • Hybrid MAC protocols • SCP, Funneling-MAC and Z-MAC • Not designed for high throughput

  5. Background of CSMA • S1: Sender 1 • S2: Sender 2 • R1: Receiver 1 • R2: Receiver 2 s2 s1 r2 r1 Collision Packet may be corrupted

  6. Background of CSMA • S1: Sender 1 • S2: Sender 2 • R1: Receiver 1 • R2: Receiver 2 (1) (2) Traffic Demand Traffic Demand s1 Sense Channel (CCA Check) s2 Sense Channel (CCA Check) If Channel is Clear, Transmit If Channel is not Clear, Random Delay r1 r2

  7. Is Packet Corrupted? • S1: Sender 1 • S2: Sender 2 • R1: Receiver 1 • R2: Receiver 2 s2 s1 r2 r1 Collision Is each packet corrupted?

  8. A Case of Concurrency Power is fixed to be 15. Tmote Sky mote r1 s2 Chipcon 2420 radio; 31 tunable power levels; 256 kbps transceiver; TinyOS-1.X. Link 2 Link 1 s1 r2 power increases from level 1 to 31 (1) Run1: CSMA disabled (2) Run2: CSMA enabled

  9. Experimental Result Golden Zone Golden Zone: Power of Sender 1 is 15. Power of Sender 2 is between 9 and 16.

  10. Observations • Concurrent TXs are possible despite contention • CSMA tries avoids interference by disabling concurrency • Back-off and channel reservation

  11. Key Questions • How to enable concurrency? • Carefully control TX power for each sender • How to control TX power? • Empirical power control and interference models • Power decay model • Signal-to-Interference-Noise ratio (SINR) model

  12. Outline • Application and Related Work • Motivation • Models • Design of MAC protocol • Evaluation

  13. Power Decay Model RSS = P / distα log(RSS) = log(P)- α log(dist) • Classical exponential decay model:

  14. Power Decay Model RSS = P / distα log(RSS) = log(P)- α log(dist) not accurate! • Classical exponential decay model: • Experimental setup • One sender, multiple receivers at different positions • Experiments in 4 different environments • Office corridor grass fieldparking lot

  15. Power Decay Model • Near-linearRSSdBm vs. transmission power level • Overhead can be reduced Received Signal Strength (dBm) Transmission Power Level

  16. Pair-wise Power Decay Model • Received signal strength (RSS) at r when s transmits with power Ps is given by RSSr(s) = a x Ps+ b • a and b are interpolated using multiple measurements • a is estimated once • b is updated periodically s Ps r RSSr(s)

  17. PRR vs. Signal-to-Interference-Noise Ratio (SINR) • Classical model doesn't capture the gray region 0~3 dB is "gray region" Packet Reception Ratio (%) parking lot, no interferer office, no interferer office, one interferer Received Signal Strength (RSS) > b Noise +å Interference

  18. Probabilistic SINR Model • PRR(SINRi ) (1≤ i ≤ m) 100 80 60 Packet Reception Ratio (%) 40 20 0 1 2 3 4 SINR (dB) Classical deterministic SINR Model Our probabilistic SINR model

  19. SINR Models in Different Settings different signal strength different # of interferers

  20. Outline • Application and Related Work • Motivation • Models • Design of MAC protocol • Evaluation

  21. Received Data from App Traffic Snooping Concurrency Check fail Dropped Random Delay max count reached pass fail Interference Assessment no improvement Throughput Prediction Data Transmission

  22. Concurrency Check • Overhear m packets (say, belonging to K links) • For each of link uv, predict the PRR if s transmits with min power PRRv(SNRv) • If the PRR of any link would drop below α (i.e., 20%), fails stored in data packet compute PRRv(SNRv)from v's interference model RSSv(Pu) SNRv = RSSv(Psmin) + Ir+Nr compute from v's RSS model RSSv(Psmin) = av Psmin + bv

  23. Throughput Prediction • s tries to transmit to r • s overhears m packets (belonging to K links) • s finds power P that maximizes • If negative, abort, otherwise transit a block of B packets RSS model PRR model RSSr(P) Σ PRRv( SNRv ) – |K| SNRr = Interferencer+Noiser assuming 100% PRR for all active links obtained from handshaking

  24. Outline • Application and Related Work • Motivation • Models • Design of MAC protocol • Evaluation

  25. Performance Evaluation • Implemented in Tmote testbed with TinyOS-1.x • 16 Tmotes deployed in a 25x24 ft office • 8 senders and 8 receivers

  26. System Throughput

  27. System Delay

  28. Energy Consumption

  29. Thanks!

  30. Block Transmission • RTS/CTS • Support data-intensive sensing applications. • habitat monitoring [1], structural monitoring [2] and etc. • Not for low-load applications. [1] R. Szewczyk, A. Mainwaring, J. Polastre, J. Anderson, and D. Culler. An analysis of a large scale habitat monitoring application. In SenSys, 2004. [2] N. Xu, S. Rangwala, K. K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin. A wireless sensor network for structural monitoring. In SenSys, 2004.

  31. Components Traffic Snooping Concurrency Check Power Decay Model Concurrent Transmission Engine Interference Assessment Online Model Estimation SINR Model Throughput Prediction

  32. Conflict • Senders can not concurrent transmit whatever the sending power is, when r2 Multi-Channel s1 s2 r1

  33. Recent studies on multi-channel • Figures in this slide are from • [1] Yafeng Wu et al, Realistic and Efficient Multi-Channel Communications in Wireless Sensor Networks, INFOCOM 2008.

  34. Received Signal Strength (RSS) > b Noise +å Interference Signal-to-Interference-Noise Ratio (SINR) model • Classical deterministic SINR model: 100 80 60 PRR = 1 If 0 Otherwise Packet Reception Ratio (%) 40 20 0 1 2 3 4 SINR (dB)

  35. Time Sequence data packet data packet Sender syn packet Noise Level Measurement RSS Measurements Receiver Jammer1 jam packet jam packet … Jammer n jam packet jam packet Time Send event Receive/measure event

  36. System Experiment Performance with different block size Throughput

  37. System Experiment Performance with different block size Delay

  38. System Experiment Performance with different block size Energy Consumption

  39. Related Work • CSMA-based MAC protocols • S-MAC, T-MAC, B-MAC and X-MAC…. • TDMA-based MAC protocols • TRAMA, DCQS and DRAND… • Hybrid MAC protocols • SCP, Funneling-MAC and Z-MAC

  40. Motivation Experiment Golden Zone Golden Zone: Power of Sender 1 is 15. Power of Sender 2 is between 9 and 16.

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