Other scenarios

We considered three other scenarios to evaluate our methodology. In the first scenario, we turned on the autorate feature for each card. When the autorate algorithm is on, the transmission rate for unicast packets may vary over time, in response to changing noise levels etc. The rate selection algorithm is not standardized. The broadcast packets, however, are always sent at the lowest data rate (6Mbps for 802.11a). Note that in the baseline scenario, the unicast transmission rate was also fixed at 6Mbps. With autorate on, we would expect more mismatch between $BIR$ and $LIR$.

In the second scenario, we reduced the transmit power on each card to 50% of the full power. We fixed the transmission rate at 6Mbps. At 50% transmit power, the network has fewer links: only 128, instead of 152. Thus, the link pairs used in this scenario are different from the link pairs used in the previous, full-power scenarios. The average loss rate of these 128 links is 4.6%, while the average loss rate at full power was 2.9%. Since, the links are more lossy in this scenario, we would expect slightly higher mismatch between $BIR$ and $LIR$ in this setting.

All the experiments so far were done in 802.11a mode, using the NetGear cards. In the third scenario, we turned off the Netgear cards, and used Orinoco cards, set to operate in 802.11g mode (i.e. in 2.4GHz spectrum), at full power, with rate fixed at 1Mbps (i.e. the lowest data rate for 802.11g). We have an infrastructure mode 802.11b network in our building, which operates in the same frequency band. We tested this scenario at night, to minimize the impact of interference from the WLAN, however, we would still expect to see higher error in this scenario.

For each of these scenarios, we measured $BIR$ and $LIR$ of 75 link pairs, using back-to-back experiments as before. The CDF of absolute error in each of the three cases is shown in Figure 5. The results show that our methodology performs generally well in each scenario. As expected, the mismatch is somewhat high for the autorate scenario. In the other two cases, the median error is only 0.01. Even in autorate case the median error is only 0.03, while the mean is 0.065. These three experiments increase our confidence in the general applicability of our method.