1. 1/24 Passive Interference Measurement in Wireless Sensor Networks Shucheng Liu 1,2, Guoliang Xing 3, Hongwei Zhang 4, Jianping Wang 2, Jun Huang 3, Mo Sha 5, Liusheng Huang 1 1 University of Science and Technology of China, 2 City University of Hong Kong, 3 Michigan State University, 4 Wayne State University, 5 Washington University in St. Louis

2. 2/24 Outline Motivation Understanding the PRR-SINR interference model Passive Interference Measurement (PIM) protocol Testbed evaluation

3. 3/24 Data-intensive Sensing Applications Real-time target detection & tracking, earthquake monitoring, structural monitoring etc. –Ex: accelerometers must sample a structure at 100 Hz 100 seismometers in UCLA campus [Estrin 02] acoustic sensors detecting AAV http://www.ece.wisc.edu/~sensit/

4. 4/24 Challenges Wireless sensors have limited bandwidth Excessive packet collisions in high-rate apps –Energy waste and poor communication quality Interference mitigation schemes –TDMA, link scheduling, channel assignments… –Rely on accurate interference models

5. 5/24 Interference Models Protocol model –Perfect comm. range –Binary packet reception PRR-SINR model –Packet reception ratio vs. signal to interference plus noise ratio PRR=100% Ganesan 2002

6. 6/24 Empirical Study on PRR-SINR Model Measurement in different timesMeasurement at different locations Significant spatial and temporal variation  Real-time interference model measurement is necessary

7. 7/24 A State-of-the-Art Measurement Method Time Send event Receive/measure event Sender Receiver Interferer Synchronization Noise Level Measurement SINR measurement Received? Measuring multiple (PRR,SINR) pairs for many nodes  Prohibitively high overhead!

8. 8/24 Outline Motivation Understanding PRR-SINR model Passive Interference Measurement (PIM) protocol Performance evaluation

9. 9/24 Key Observations Data traffic generates many packet collisions Spatial diversity leads to different SINRs SINR=1dB SINR=2dB SINR=5dB

10. 10/24 Overview of PIM base station M-node Measure M-node’s PRR-SINR model R-node selection Information collection Interference detection Model generation R-node 1R-node 2 Interference link Data link

11. Information Collection node idpkt idtimestampRSS Aggregator M-node R1p1t1TX R2p2t2TX RSS measurements of collision-free packets p1 p2 Mp1t1RSS(p1) Mp2t2RSS(p2) R-node 1R-node 2 Received Signal Strength

12. Information Collection node idpkt idtimestampRSS Aggregator m-node R1r11t1 R2p2t2TX TX/RX statistics of colliding packets p3p4 R1p1t1TX R1p3t3TX Mp1t1RSS(p1) Mp2t2RSS(p2) Mp4t3RSS (p3+p4) R2p4t3TX Receive with collision r-node 1r-node 2

13. Information Collection node idpkt idtimestampRSS Aggregator m-node R1r11t1 R2p2t2TX Colliding packets for TX/RX statistics p5p6 R1r11t1 R1p1t1TX R1p3t3TX Mp1t1RSS(p1) Mp2t2RSS(p2) Mp4t3RSS (p3+p4) R2p4t3TX Lost due to collision R1p5t4TX R2p6t4TX r-node 1r-node 2

14. Interference Detection 1.Detect interferer with collected timestamps 2.Remove fake collisions Packets may overlap without interference! Remove using measured RSS information node idpkt idtimestampRSS R1r11t1R1p1t1TX Mp1t1RSS(p1) R2p2t2TX Mp2t2RSS(p2) R1r11t1R1p3t3TX R2p4t3TX Mp4t3RSS (p3+p4) R1p5t4TX R2p6t4TX p4 collides with p3, but received by M p6 collides with p5, lost at M

15. Model Generation 1.Derive SINR for collision of p3, p4 SINR(p3+p4) = RSS(p4) – RSS(p3) – Noise = RSS(p2) – RSS(p1) – Noise node idpkt idtimestampRSS R1r11t1R1p3t3TX R2p4t3TX Mp4t3RSS (p3+p4) R1p5t4TX R2p6t4TX p4 collides with p3, but received by M p6 collides with p5, lost at M 2.Compute PRR PRR = 50% Mp1t1RSS(p1) Mp2t2RSS(p2)

16. 16/24 R-Node Selection Minimize the number of r-nodes used to measure the (PRR,SINR) pairs of all M-nodes Proved to be NP-hard Designed a efficient greedy algorithm M-nodeSINRInterfering R-node set M11 dB{R1, R2}, {R4, R5} M12 dB {R1, R3}, {R2, R3}, {R3, R4, R5} M21 dB {R2, R3}, {R3, R4}, {R1, R4, R5} M22 dB {R1, R3}, {R1, R5}, {R3, R5} R-Nodes Set {R1, R2, R3}

17. 17/24 Experimental Setup Implemented on TelosB with TinyOS-2.0.2 Both a 13-node portable testbed and a 40-node static testbed Compared with the ACTIVE method

18. 18/24 Accuracy of PIM Create a model using 5 min statistics Predict the throughput of from another sender Baseline methods Active method w/ 256 and 1024 control packets Analytical model in Tinyos2.1

19. 19/24 Overhead of PIM

20. 20/24 Conclusions Empirical study of PRR-SINR interference model Passive interference measurement –Significantly lower overhead –High accuracy of PRR-SINR modeling –Real time interference modeling Performance evaluation on real testbeds

21. 21/24 Accuracy of PIM

22. 22/24 Thanks!

23. 23/24 Remove Fake Interfering Packets Rule 1: If a interfering packet set of node v maintains the same SINR when removing packet w, then the forwarder/sender of w is a fake r-node of node v. Rule 2: If node u is a fake r-node of node v, then any packet sent by u does not interfere with any packet received by v.

24. 24/24 Example Fake r-node of N4: –N7 –N5 m-nodeSINRInterfering r-node set N41{N1, N2}, {N1, N2, N5}, {N1, N2, N5, N7} N42… ………

25. 25/24 Average Errors Over Time

26. 26/24 Average Errors with Duty Cycles

27. 27/24 Overview The system architecture of PIM records the time when an r- node forwards each packet records the time when an m- node receives each packet chosen to help measure the PRR-SINR model of the m-node whose PRR-SINR models are to be measured records the RSS values of the received packets. collects information and generates the PRR-SINR models of m-nodes

28. 28/24 Overview The system architecture of PIM detects interferer using collected information generates PRR-SINR models of m-nodes decreases overhead by identifies interferers of m-nodes

29. 29/24 Information Collection Timestamping –Record the time of forwarding/sending and receiving packet RSS measurement –Record the RSS value of received packet All the recorded informations are then transmitted to the aggregator

30. 30/24 Why PRR-SINR Model? Packet-level physical interference model Easy to estimated based on packet statistics Directly describes the impact of dynamics –Environmental noise –Concurrent transmissions average power of ambient noise probability of receiving packet received signal power of packet received signal power of interfering transmissions collisions s1s1 r s2s2