Methodology for Characterizing Performance of IEEE 802.11 Nodes in Multi-hop Environments

1. “Methodology to characterize the performance of IEEE 802.11 nodes to be deployed in multi-hop environments” “Marc Portoles Comeras, Andrey Krendzel, Josep Mangues-Bafalluy” “Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)” April 20, 2007

2. Outline • Introduction • A study case: what fails? • Accounting for errors in detecting other transmissions:  • Measuring  • Experimental results • Using the value in practice • Conclusions

3. Introduction • Motivation: • A large body of theoretical work on wireless mesh networking has still not been experimentally tested • When going experimental, however, unexpected issues are prone to arise • Previous work has shown that good theory can be too promptly discarded when the hardware being used is not correctly calibrated • The objective here is to analyze possible inaccuracies in the wireless hardware that may distort measurements in experimental wireless mesh networking

4. A study case: what fails? • Saturation throughput study: 802.11 leads to asymmetry • Theoretical model* prediction: B receives 4-6% of time share • Experimental results:B is active more than 20% of time! • What’s failing? • Is the model accurate? • Does the hardware behave as expected? -------------------------------------------------------------------------------------------------------------------------------------------------- *Claude Chaudet et al. “Study of the Impact of Asymmetry and Carrier Sense Mechanism in IEEE 802.11 Multihops Networks through a Basic Case”, in proceeding MSWIN 2004, Venezia, Italy

5. Accounting for errors in detecting other transmissions • Active 802.11 node can be in any of three states: • active: sending data • idle: not detecting activity in the medium • stall: detecting the medium busy or receiving data • The maximum data that a backlogged station can send,

6. Accounting for errors in detecting other transmissions • Let’s define a metric  = 1 - Pr{node senses medium idle when it is being used} • Maximum data that a non-ideal station sends • In case we could compute Taccu_stall exactly

7. Measuring  • A scenario to measure the metric  • Requirement: Taccu_stall must be easy to compute • Flow 1 and Flow 2 characteristics: • Independent, identical and multicast • In this case  Taccu_stall = Tactive •  can be obtained through measuring the maximum transmission rate that Flow 2 achieves

8. Measuring  • Workload can affect the measure • If the NUT is not able to handle the data rates that it is required to (Flow 1 + Flow 2) the measure of metric  is not reliable • We need a method to detect whether the NUT is loosing data due to excessive workload

9. Measuring  • Workload can affect the measure • A validation curve to detect excessive workload

10. Experimental results • A corrrect measure of the metric without workload interference

11. Experimental results • An example of workload interference in the measure

12. Using the value in practice • Applying  to the initial example • There is a high probability (11% in our experiments) that B does not correctly detect transmissions from A and C • Strong bias in the measure • Possible solutions are: • Modify the model to include hardware inaccuracies • Choose alternative hardware or correctly tune the one that is being used.

13. Conclusions • This paper • Shows how wireless hardware solutions may fail in detecting transmissions from other stations • Proposes a novel metric to account for the probability that a station fails in detecting other transmissions • Proposes a measurement methodology to obtain a value of the metric • The study also draws attention on the importance of correctly characterizing hardware behavior before rising conclusions out of measurement observations.

14. Thanks for your kind attention! • Questions? Marc Portoles Comeras Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) – Barcelona Contact e-mail: marc.portoles@cttc.es