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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.

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Presentation on theme: "1 C-MAC: Model-driven Concurrent Medium Access Control for Wireless Sensor Networks Mo Sha ; Guoliang Xing ; Gang Zhou ; Shucheng Liu ; Xiaorui Wang City."— Presentation transcript:

1 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 2 Outline Motivation Power control and interference models Design of C-MAC Performance evaluation

3 3 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 Data-intensive Sensing Applications

4 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 5 s1s1 r1r1 s2s2 Collision Background of CSMA r2r2 S1: Sender 1 S2: Sender 2 R1: Receiver 1 R2: Receiver 2 Packet may be corrupted

6 6 s1s1 r1r1 s2s2 r2r2 Traffic Demand Sense Channel (CCA Check) If Channel is Clear, Transmit Traffic Demand Sense Channel (CCA Check) If Channel is not Clear, Random Delay (1) (2) S1: Sender 1 S2: Sender 2 R1: Receiver 1 R2: Receiver 2 Background of CSMA

7 7 s1s1 r1r1 s2s2 Collision Is Packet Corrupted? r2r2 S1: Sender 1 S2: Sender 2 R1: Receiver 1 R2: Receiver 2 Is each packet corrupted?

8 8 A Case of Concurrency s1s1 r1r1 s2s2 r2r2 Power is fixed to be 15. power increases from level 1 to 31 (1) Run1: CSMA disabled (2) Run2: CSMA enabled Chipcon 2420 radio; 31 tunable power levels; 256 kbps transceiver; TinyOS-1.X. Link 1Link 2 Tmote Sky mote

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

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

11 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 12 Outline Application and Related Work Motivation Models Design of MAC protocol Evaluation

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

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

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

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

17 17 PRR vs. Signal-to-Interference-Noise Ratio (SINR) Classical model doesn't capture the gray region office, no interfererparking lot, no interfereroffice, one interferer  Noise +  Interference Received Signal Strength (RSS) 0~3 dB is "gray region" Packet Reception Ratio (%)

18 18 Probabilistic SINR Model PRR(SINR i ) (1≤ i ≤ m) PRR(0.5)PRR(1)PRR(1.5)PRR(2) ….. 01234SINR (dB) Packet Reception Ratio (%) 20 40 60 80 100 Classical deterministic SINR Model Our probabilistic SINR model

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

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

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

22 22 Concurrency Check 1.Overhear m packets (say, belonging to K links) 2.For each of link u  v, predict the PRR if s transmits with min power PRR v (SNR v ) 3.If the PRR of any link would drop below α (i.e., 20%), fails RSS v (P u ) RSS v (P smin ) + I r +N r SNR v = stored in data packet RSS v (P smin ) = a v P smin + b v compute from v's RSS model compute PRR v (SNR v ) from v 's interference model

23 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 Σ PRR v ( SNR v ) – |K| assuming 100% PRR for all active links RSS r (P) Interference r +Noise r SNR r = obtained from handshaking RSS model PRR model

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

25 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 26 System Throughput

27 27 System Delay

28 28 Energy Consumption

29 29 Thanks!

30 30 RTS/CTS –Support data-intensive sensing applications. habitat monitoring [1], structural monitoring [2] and etc. –Not for low-load applications. Block Transmission [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 31 Components Concurrent Transmission Engine Power Decay Model SINR Model Interference Assessment Online Model Estimation Concurrency Check Throughput Prediction Traffic Snooping

32 32 Conflict –Senders can not concurrent transmit whatever the sending power is, when s1s1 r1r1 s2s2 r2r2 Multi-Channel

33 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 34 Signal-to-Interference-Noise Ratio (SINR) model Classical deterministic SINR model:  Noise +  Interference Received Signal Strength (RSS) 01234SINR (dB) Packet Reception Ratio (%) 20 40 60 80 100 PRR = 1 If 0 Otherwise

35 35 Time Sequence Sender Receiver Jammer 1 Jammer n … syn packet data packet Time RSS Measurements Noise Level Measurement jam packet Send event Receive/measure event data packet jam packet

36 36 System Experiment Performance with different block size Throughput

37 37 System Experiment Performance with different block size Delay

38 38 System Experiment Performance with different block size Energy Consumption

39 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 40 Motivation Experiment Golden Zone: Power of Sender 1 is 15. Power of Sender 2 is between 9 and 16. Golden Zone


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