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. 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. 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. 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! -------------------------------------------------------------------------------------------------------------------------------------------------- *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 What’s failing? Is the model accurate? Does the hardware behave as expected?

5. 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. 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 T accu_stall exactly

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

8. 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. 9 Measuring  Workload can affect the measure A validation curve to detect excessive workload

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

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

12. 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. 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. 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