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Client-Centric Strategies for dealing with Interference and Congestion in IEEE 802.11 Wireless Networks By: Udayan Das A

Client-Centric Strategies for dealing with Interference and Congestion in IEEE 802.11 Wireless Networks By: Udayan Das Adviser: Cynthia Hood. Overview. Introduction – Wireless Background Related Work / Motivation Detecting Interference – Experimental Work Ongoing / Future Work

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Client-Centric Strategies for dealing with Interference and Congestion in IEEE 802.11 Wireless Networks By: Udayan Das A

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  1. Client-Centric Strategies for dealing with Interference and Congestion in IEEE 802.11 Wireless Networks By: Udayan Das Adviser: Cynthia Hood

  2. Overview • Introduction – Wireless Background • Related Work / Motivation • Detecting Interference – Experimental Work • Ongoing / Future Work • Mitigating Interference • Conclusions

  3. Introduction - Wireless • Popularity of Wireless Devices – especially ones based on IEEE 802.11 Suite of Protocols. • IEEE 802.11: • In unlicensed 2.4-GHz ISM Band (802.11b/g) and unlicensed ISM/UNII Band (802.11a) – therefore there is the problem of interference from other devices • Shared Channel BW – Contention-Based mechanism – so there is the problem of Congestion. • Terminology: Stations/Nodes/Clients, APs, BSS...

  4. Introduction - Interference • Problem: Number of different devices use the unlicensed Bands: • Ex: Bluetooth devices, ZigBee devices, Cordless Phones, Wireless Mice & Keyboards, Baby Monitors etc. • Problem: Other devices release unwanted radiation in the Bands: • Ex: Microwave.

  5. Related Work • Wireless Monitoring: Li et al [2], Yeo et al [3], & Henderson/Kotz [4] discuss Tools and Techniques – HW, SW, Basic Issues. • Monitoring Case Studies: Yeo et al [5] & [6], Kotz/Essein [7] & Balachandran et al [8] discuss monitoring experiments in Public wireless LANs such as a campus LAN.

  6. Related Work • Effect of wireless variability on Higher Layers: Li et al [9] and Vacirca/Cuomo [10]. • Effect on TCP: Subramaniam et al [11] discuss using Simulation the effect of Interference on TCP. • Interference Bluetooth etc: Golmie et al [12] & [13] discuss interference effects theoretically.

  7. Related Work / Motivation • The effect of Interference has not been studied in detail. • Effects on Higher Layer Performance • Detecting Interference • Gummadi et al [14] has experimental work on interference and also discusses Mitigation • Similar to our work • Differences in methods used – Interference Sources etc. • Mitigation Methods not practical

  8. Motivation • To show experimentally the effect of Interference – interference can be detected • Once detected, we need to devise simple methods to counter it's negative effects. • Quick & Easy Implementation • Wide-spread deployment (acceptance) • Limited or No Change to IEEE 802.11 Standards • Intuitively, moving to a different channel is the best approach, and we propose methods to do this in a simple manner – looking at the problem from a client's perspective.

  9. Interference - Detection • Conducted a series of experiments to demonstrate the effect of interference on Wireless Network Performance: • Effect on Higher Layer Protocol (UDP/TCP) performance • How does degradation occur? – is it linear? Etc • What can we use to categorically say that interference exists on the channel?

  10. Interference – Detection - UDP • Experimental Set-up

  11. Interference – Detection - UDP • Experimental Set-up: • Ad-Hoc connection between two laptops using channel 6 – 2.437 GHz. • UDP Packet Generator – which constantly sends UDP packets to the receiver. Collect packet traces at both ends (using Ethereal). • Introduce Interference (Sine Wave from Wave Generator) at • center of Channel 6 • center of Channel 5 (2.432 GHz) – Adjacent Channel • center of Channel 4 (2.427 GHz) – Edge of Channel 6 • Similar experiment with TCP (Video Traffic).

  12. UDP - Results

  13. UDP - Results

  14. UDP - Results

  15. Interference (dBm)‏ UDP Retries Estimated ACK Drop Rate UDP Throughput -45 2.90 65.52 8.93 Kbps -50 1.26 20.91 867.60 Kbps -55 1.04 3.86 1.594 Mbps -60 1.08 7.46 1.666 Mbps -65 1.04 3.55 1.684 Mbps -70 1.12 10.52 1.717 Mbps -75 1.05 4.70 1.697 Mbps UDP – Results

  16. UDP - Results

  17. UDP - Results

  18. TCP - Results

  19. Results - Analysis • Clearly, the performance degradation is not linear with increasing interference. • After -50-dBm we see rapid degradation in performance. • This degradation alone cannot be used to identify interference, as this may have been caused by congestion. • Degradation is more rapid for UDP throughput, and for MAC-Layer Information – ACK Drop Rate. • BUT: it doesn't matter what causes performance degradation – Interference / Congestion.

  20. Results – Analysis & Conclusion • We have observed that beyond -50dBm connection shows a susceptibility to break-down. • In fact, at -45dBm connection does not remain alive for entire trace period. • We observed the same behavior for TCP. • “Break-down of connection is the best indicator of interference!” • Comparison with Gummadi et al [14]: • Similar performance degradation (at different power levels) • No effect of Adjacent Channel – PRISM Interferer • Do not mention connection breakdown

  21. Mitigation • Basic Philosophy: Moving to a different Channel is best. • Re-association decision: Find a new channel/AP to associate to- • Without cooperation from AP • With cooperation from AP • Packet Tracing will be used again to estimate channel conditions • Then a selection is made based on maximum available bandwidth • Usually choices are available

  22. Mitigation – Typical Scenario • “Choices are available!” – • About 60 APs

  23. Ongoing Work:Mitigation - Un-cooperated • Method: • Collect channel state information by doing a passive scan for 5 seconds, less (1-second as above) when running time-sensitive applications such as audio. • Calculate per channel usage by adding the DATA column for all APs on a particular channel. • Select an AP on the least utilized channel after yielding to AP selection (preferred network) policy. • Select an AP with the highest available signal strength among the possible candidates.

  24. Ongoing Work:Mitigation - Un-cooperated • Packet Tracing example using Kismet.

  25. Ongoing Work:Mitigation - Un-cooperated • Example: • Channel 1: Clients: 0; BW Usage: 0. • Channel 6: Clients: 4; BW Usage: 29.5 Kbps. • Channel 11: Clients: 0; BW Usage: 0. • Channel 1 or 11 is a better option than Channel 6. • However, channel 6 has most APs, and considering an AP policy (ex: IIT) channel 6 will still be selected; channel 6 usage is still low.

  26. Future Work:Mitigation – Cooperation from AP • AP records information on Network state: • Channel Utilization • Percentage Traffic: Audio, Video and Data • This information is broadcast in BEACON frame • Method: • Same as before, but calculations are based on AP advertised information

  27. Future Work:Mitigation – Cooperation from AP • Benefit: • Beacons are used, upto 5 Beacons can be used, the period is less than 1 second. • On the other hand, it gives a more long term view of network state because AP information is time averaged. • Therefore recent changes in Network State will have less effect. • Better to have Pessimistic Averaging scheme

  28. Conclusions • Demonstrated effect of Interference experimentally • Switching to a different channel is the best way to Mitigate interference and congestion • Packet Tracing can be used to make association decisions after interference/congestion has been detected

  29. Contributions: • Experimental study of the effect of interference on higher layer performance • Demonstrating how Packet-Tracing can be used to estimate Network/Channel State • This can be used in making association decisions • Publication: DySpan 2007: “Client Channel Selection for Optimal Capacity in IEEE 802.11 Wireless Networks.” with J.T. MacDonald & D. Roberson • Follow-Up Paper to be submitted to DySpan '08

  30. Questions?

  31. References: • J. T. MacDonald, U. Das, and D. A. Roberson, “Client Channel Selection for Optimal Capacity in IEEE 802.11 Wireless Networks.” In proceedings of the IEEE International Symposium on Dynamic Spectrum Access Networks (DySpan 2007), Dublin, Ireland, April 2007. • F. Li, M. Li, R. Lu, H. Wu, M. Claypool, and R. Kinicki, “Tools and Techniques for Measurement of IEEE 802.11 Wireless Networks.” In Proceedings of the Second International Workshop On Wireless Network Measurement (WiNMee), Boston, MA, USA, April 2006 • J. Yeo, S. Banerjee and A. Agrawala. “Measuring Traffic on the Wireless Medium: Experience and Pitfalls.” CS-TR 4421. Department of Computer Science, University of Maryland, Wise’04. • T. Henderson and D. Kotz. “Measuring Wireless LANs.” In R. Shorey, A. L. Ananda, M. C. Chan, and W. T. Ooi, editors, Mobile, Wireless and Sensor Networks: Technology Applications and Future Directions, pages 5–27. New York, NY, 2006. • J. Yeo, M. Youssef, and A. Agrawala, “A Framework for Wireless LAN Monitoring and its Applications.'' in ACM Workshop on Wireless Security (WiSe 2004) in conjunction with ACM MobiCom 2004, Philadelphia, PA, USA, Oct. 2004.

  32. References: • J. Yeo , M. Youssef , T. Henderson , A. Agrawala, “An Accurate Technique for Measuring the Wireless Side of Wireless Networks.” Papers presented at the 2005 workshop on Wireless traffic measurements and modeling, p.13-18, June 05-05, 2005, Seattle, Washington. • D. Kotz , K. Essien, “Analysis of a Campus-wide Wireless Network.” Proceedings of the 8th annual international conference on Mobile computing and networking, September 23-28, 2002, Atlanta, Georgia, USA. • A. Balachandran , G. M. Voelker , P. Bahl , P. V. Rangan, “Characterizing User Behavior and Network Performance in a Public Wireless LAN.” Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems, June 15-19, 2002, Marina Del Rey, California • F. Li, J. Chung, M. Li, H. Wu, M. Claypool, and R. Kinicki, “Application, Network and Link Layer Measurements of Streaming Video over a Wireless Campus Network.''Proceedings of the 6th Passive and Active Measurement Workshop (PAM), Boston, Massachusetts, USA, Apr. 2005 • F. Vacirca, and F. Cuomo, “Experimental Results on the Support of TCP over 802.11b: An Insight into Fairness Issues.” WONS 2006.

  33. References: • V. Subramaniam, K. K. Ramakrishnan, S. Kalyanaram, and L. Ji, “Impact of Interference and Capture Effects in 802.11 Wireless Networks on TCP.” Proceedings of the second international workshop on Wireless traffic measurements and modeling, 2006 • N. Golmie, R.E.V. Dyck, A. Soltanin, A. Tonnerrre, and O. Rebala, “Interference Evaluation of Bluetooth and IEEE 802.11b Systems.” Wireless Networks, 9(3):201–211, 2003. • N. Golmie and F. Mouveaux. “Interference in the 2.4 GHz ISM Band: Impact on the Bluetooth Access Control Performance.” In ICC, Helsinki, June 2001. • R. Gummadi, D. Wetherall, B. Greenstein, S. Seshan, “Understanding and Mitigating the Impact of RF Interference on 802.11 Networks.” In Proceedings of the ACM SIGCOMM 2007, Kyoto, Japan, Aug 2007. • A. P. Jardosh, K. N. Ramachandran, K. C. Almeroth, and E. M. Belding-Royer, ``Understanding Congestion in IEEE 802.11b Wireless Networks.'' In Proceedings of the Internet Measurement Conference (IMC), Berkeley, CA, USA, Oct 2005.

  34. Further Work: • Focus on Bit-Errors – Develop Interference Models • Case Studies on Implemented Mitigation Schemes • Move to beyond Client-Centric Philosophy • When Interference is detected, AP can make switching decision and inform Clients through the BEACON

  35. The End

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