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Client-Centric Strategies for dealing with Interference and Congestion in

IEEE 802.11 Wireless Networks

By: Udayan DasAdviser: Cynthia Hood

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Overview

Introduction – Wireless Background Related Work / Motivation Detecting Interference – Experimental Work Ongoing / Future Work

Mitigating Interference Conclusions

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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...

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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.

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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.

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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.

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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

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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.

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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?

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Interference – Detection - UDP

Experimental Set-up

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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).

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UDP - Results

PRR (Center)

84

86

88

90

92

94

96

98

100

-100 -90 -80 -70 -60 -50 -40

Interference (dBm)

PRR

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UDP - Results

PRR (1st Adjacent Channel)

84

86

88

90

92

94

96

98

100

-100 -90 -80 -70 -60 -50 -40

Interference (dBm)

PRR

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UDP - Results

PRR (2nd Adjacent Channel)

84

86

88

90

92

94

96

98

100

-100 -90 -80 -70 -60 -50 -40

Interference (dBm)

PRR

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UDP – Results

1.697 Mbps4.701.05-75

1.717 Mbps10.521.12-70

1.684 Mbps3.551.04-65

1.666 Mbps7.461.08-60

1.594 Mbps3.861.04-55

867.60 Kbps20.911.26-50

8.93 Kbps65.522.90-45

UDP ThroughputEstimated

ACK Drop RateUDP RetriesInterference (dBm)

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UDP - Results

UDP Throughput (kbps)

0.00200.00400.00600.00800.00

1000.001200.001400.001600.001800.002000.00

-80 -70 -60 -50 -40

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UDP - Results

ACK Drop Rate

0.0010.0020.0030.0040.0050.0060.0070.00

-80 -75 -70 -65 -60 -55 -50 -45 -40

Interference (dBm)

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TCP - Results

TCP Throughput

0.00

50.00

100.00

150.00

200.00

250.00

-90 -80 -70 -60 -50 -40 -30

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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.

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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

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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

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Mitigation – Typical Scenario “Choices are available!” –

About 60 APs

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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.

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

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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.

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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

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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

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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

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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

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Questions?

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References:1)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 2007.2)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 20063)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’04.4)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, 2006.5)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.

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References:6) 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.

7) 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.

8) 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

9) 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

10) F. Vacirca, and F. Cuomo, “Experimental Results on the Support of TCP over 802.11b: An Insight into Fairness Issues.” WONS 2006.

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References:11) 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

12) 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.

13) 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.

14) 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.

15) 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.

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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

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The End