1. SELECT: Self-Learning Collision Avoidance for Wireless Networks Chun-Cheng Chen, Eunsoo, Seo, Hwangnam Kim, and Haiyun Luo Department of Computer Science, University of Illinois, Urbana-Champaign IEEE Transactions on Mobile Computing, Vol. 7, No.3, 2008

2. Outline Introduction Introduction Hidden/exposed terminal problem in 802.11 networks Hidden/exposed terminal problem in 802.11 networks Motivation Motivation SELECT SELECT a self-learning collision avoidance mechanism a self-learning collision avoidance mechanism Performance evaluation Performance evaluation Conclusion Conclusion

3. Introduction Limited number of orthogonal channels restricts the deployment of 802.11 APs. Limited number of orthogonal channels restricts the deployment of 802.11 APs. –3 channels for 802.11b/g, 12 for 802.11a –Interference range is long compared with communication range

4. Introduction Recent published data shows 40% of 802.11 APs are operating on channel 6 Recent published data shows 40% of 802.11 APs are operating on channel 6 In Boston, a max number of 85 APs are detected in the interference range In Boston, a max number of 85 APs are detected in the interference range –At least 30 APs are directly interfering with each other

5. Hidden/exposed terminal problem Restrain by RTS Restrain by CTS Restrain by B’s CTS, Cannot reply E’s RTS C’s RTS collide with A->B

6. Drawbacks of hidden/ exposed receiver problem 1. Sender drops the head-of-line data packet –Resulting in a contention-induced packet loss 2. Unsuccessful RTS transmission, misled the sender to conclude –Receiver is unavailable (false link breakage is triggered) –Channel quality at the receiver side is low (Using low data transfer rate)

7. Drawbacks of hidden/ exposed receiver problem 3. Unsuccessful RTS attempts inflate sender’s contention window 4. Repeated RTS attempts prevent the sender’s neighbor from transmitting –Low channel utilization 5. Hidden/exposed terminal problem will persists until the clients move and contention relation changes

8. Motivation Use MICA2 CC1000 to simulate the operation of 802.11 devices Use MICA2 CC1000 to simulate the operation of 802.11 devices Exposed receiver Potential sender

9. RSS at motes C and D while A is transmitting to B

10. RSS vs. SR (successful ratio) C → D, G → H are active C → D, G → H are active E → F serves as an additional interference E → F serves as an additional interference A → B, A records the RTS successful ratio A → B, A records the RTS successful ratio

11. RSS vs. RTS SR at mote A

12. RSS vs. RTS SR over time

13. Summary of RSS vs. RS The RSS at the sender and the receiver has strong correlation The RSS at the sender and the receiver has strong correlation To estimate the RSS at the receiver from the sender is complex To estimate the RSS at the receiver from the sender is complex The sender can use its RSS as an indicator of the status at receiver The sender can use its RSS as an indicator of the status at receiver

14. Overview of SELECT Sender uses the detected RSS to map the receiver’s condition (successful ratio) Sender uses the detected RSS to map the receiver’s condition (successful ratio) RSS is divided into several intervals, each interval has a corresponding SR RSS is divided into several intervals, each interval has a corresponding SR RSS ≧ CS thred → channel busy RSS ≧ CS thred → channel busy SR ≧ threshold → transmit the data SR ≧ threshold → transmit the data SR ＜ threshold → pretend the transmission is failed SR ＜ threshold → pretend the transmission is failed

15. SELECT: self-learning collision avoidance RSS-SR mapping maintenance RSS-SR mapping maintenance RSS-SR mapping lookup RSS-SR mapping lookup Integration with 802.11 DCF Integration with 802.11 DCF Intelligent SR threshold setup Intelligent SR threshold setup

16. RSS-SR mapping maintenance To update the SR within an interval T win To update the SR within an interval T win Using a variable α (from 0 to 1) to indicate the weight of old data Using a variable α (from 0 to 1) to indicate the weight of old data –α~1: the stored data is very new –α~0: the stored data is almost useless Current time Last update time

17. RSS-SR mapping algorithm Calculate α Set update variable Update variable & timestamp

18. RSS-SR mapping lookup When a sender wants to send data to a receiver, the sender lookup the corresponding SR under current RSS When a sender wants to send data to a receiver, the sender lookup the corresponding SR under current RSS –Remove out-of-date data first

19. RSS-SR mapping lookup Channel Busy Return SR

20. Integration with 802.11 DCF When MAC module access the channel and the result is determined When MAC module access the channel and the result is determined –Udp_RSS_SR RSS_SR_Look-UP RSS_SR_Look-UP

21. Integration with 802.11 DCF: when backoff expired RSS ≧ CS thred → channel busy RSS ≧ CS thred → channel busy –Performs random backoff RSS < CSthred → channel idle RSS < CSthred → channel idle –SR ≧ threshold → transmit the data –SR ＜ threshold → pretend the transmission is failed, also performs random Backoff

22. Intelligent SR threshold setup (1) The authors assume the successful ratio (SR) of each RSS is distributed according to the measured RSS distribution The authors assume the successful ratio (SR) of each RSS is distributed according to the measured RSS distribution When can a station measure RSS? When can a station measure RSS? –During random backoff

23. Intelligent SR threshold setup (2) C rssi = number of measured signal strength falls within interval RSS i C rssi = number of measured signal strength falls within interval RSS i T=update interval T=update interval T rssi = the time that channel quality falls within interval RSS i T rssi = the time that channel quality falls within interval RSS i

24. Intelligent SR threshold setup (3) If SR i < threshold, station won’t transmit during period T If SR i < threshold, station won’t transmit during period T The lose of throughput The lose of throughput – △ rss j = time spend to transmit a packet within interval RSS j interval

25. Intelligent SR threshold setup (4) Try to maximize the expected throughput Try to maximize the expected throughput Total spend time Time saved by a node at the low-SR rss i Available throughput

26. Simulation setup Ns-2 2.28 Ns-2 2.28 Two-Ray Ground model Two-Ray Ground model Communication range: 115m Communication range: 115m RSS min =-100dBm RSS min =-100dBm RSS validation windows= 2 second RSS validation windows= 2 second CBR/UDP traffic CBR/UDP traffic

27. Exposed receiver Station 3 is an exposed receiver Station 3 is an exposed receiver

28. Result of exposed receiver (w/o RTS/CTS) # of drop packets at Node 2Throughput gain at Node 2

29. Result of exposed receiver (w/o RTS/CTS) Successful ratio at Node 2Throughput profile

30. Result of exposed receiver (with RTS/CTS) # of drop packets at Node 2Throughput gain at Node 2

31. Result of exposed receiver (with RTS/CTS) Successful ratio at Node 2Throughput profile

32. Hidden receiver Station 0 and 3 are hidden receivers to each other Station 0 and 3 are hidden receivers to each other

33. Normalized throughput x: with △ : w/o RTS/CTS 802.11 DCF 802.11 DCF SELECT SELECT

34. Normalized instantaneous throughput: 0->1

35. Random topology # of drop packets # of drop packets Throughput gain Throughput gain

36. Real experiment results by using MICA2 Throughput (pkt/second) RTS successful ratio

37. Conclusion The paper proposes SELECT The paper proposes SELECT –An effective and efficient self-learning collision mechanism SELECT improves throughput by up to 140 % and the successful ratio by 302 percent SELECT improves throughput by up to 140 % and the successful ratio by 302 percent

38. Thank you!!