Passive Interference Measurement in Wireless Sensor Networks

1. Passive Interference Measurement in Wireless Sensor Networks Shucheng Liu1,2, Guoliang Xing3, Hongwei Zhang4, JianpingWang2, Jun Huang3, Mo Sha5, Liusheng Huang1 1University of Science and Technology of China, 2City University of Hong Kong, 3Michigan State University, 4Wayne State University, 5Washington University in St. Louis

2. Outline Motivation Understanding the PRR-SINR interference model Passive Interference Measurement (PIM) protocol Testbedevaluation

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

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

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

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

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

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

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

11. Information Collection Aggregator • RSS measurements of collision-free packets Received Signal Strength R-node 1 R-node 2 p1 p1 p2 p2 M-node

12. Information Collection Aggregator • TX/RX statistics of colliding packets r-node 1 r-node 2 p3 p4 m-node Receive with collision

13. Information Collection Aggregator • Colliding packets for TX/RX statistics r-node 1 r-node 2 p5 p6 m-node Lost due to collision

14. Interference Detection • Detect interferer with collected timestamps • Remove fake collisions • Packets may overlap without interference! • Remove using measured RSS information p4 collides with p3, but received by M p6 collides with p5, lost at M

15. Model Generation • Derive SINR for collision of p3, p4 • SINR(p3+p4) = RSS(p4) – RSS(p3) – Noise • = RSS(p2) – RSS(p1) – Noise • Compute PRR • PRR = 50% p4 collides with p3, but received by M p6 collides with p5, lost at M

16. 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 R-Nodes Set {R1, R2, R3}

17. 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. 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. Overhead of PIM

20. 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. Accuracy of PIM

22. Thanks!

23. 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. Example • Fake r-node of N4: • N7 • N5

25. Average Errors Over Time

26. Average Errors with Duty Cycles

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

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

29. 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. received signal power of packet probability of receiving packet Why PRR-SINR Model? s2 • Packet-level physical interference model • Easy to estimated based on packet statistics • Directly describes the impact of dynamics • Environmental noise • Concurrent transmissions s1 r collisions received signal power of interfering transmissions average power of ambient noise