On Aug 7, 2009, at 1:27 PM, Razvan Musaloiu-E. wrote:
Hi! About 2 months ago, a commit [1] to CpmModelC.nc that eliminates theproblem of receiving packets (non-zero PRR) for negative SNR also made a significant change to the hardcoded PRR/SNR curve. This was not adequatelydocumented in the commit message and many people might not be aware of this. Here is a plot that shows the differences: http://bit.ly/znrwQThe points represent the data collected by Yin Chen, a member of our lab. In his tests the noise level was controlled by using a noise generator so it covers a wide range. Similar data was also reported by the followingpaper: http://portal.acm.org/citation.cfm?id=1460427So there are reasons to believe that even the current curve we have in thetree is not the most accurate one. Caution is advised! :-) [1] http://hinrg.cs.jhu.edu/git/?p=tinyos-2.x.git;a=commit;h=afe96d7e2a3747bba450e3db88a89569a0ba53b6
The methodology Yin used to collect the data set in the above figure is not appropriate for calculating SNR/PRR curves. There are three separate issues, all of which have a common concern: averaging. While the data sets in the above paper are suitable for its conclusions, they have problems when it comes to simulation.
1) It combines many node pairs: each pair can have a different SNR/PRR curve. This is why an SNR of 3dB can have a PRR of 95% or 5%.
2) Signal is a not a controlled variable, and it is averaged. A lack of control means that it will vary due to environmental conditions, and the fact that it is averaged over received packets leads to sampling bias. It is not clear in the paper if the plot averages the RSSI of packets along a link or averages the per-packet SNR. Since N is considered to be constant (more on that below), the two are equivalent. But if the signal strength has variation, and you observe the SNR only of received packets, you can observe an SNR higher than the PRR would suggest.
3) Noise/interference are not controlled. Taking the average of N assumes that it is a gaussian variable; while N is, external interference I can disrupt this measurement. This might explain why the SNR/PRR curve is lower. E.g., if 10% of my packets have a high interference, my averaged N will go up significantly in a way that will lead the SNR curve to lead to incorrect conclusions.
There are ways to give a sense of the degree of these methodological limitations. The spread of discrete points in the Figure is a good approach for 1). A plot of the S distribution could help with 2). A plot of the N distribution could help with 3).
The current curve in TOSSIM was generated from a data set that Kannan collected using a variable attenuator, shielded cabling, and 2 micaZ motes. I've attached a plot of the associated data. The error bar along the X-axis is the standard deviation of signal strength. Using a variable attenuator and shielded cables leads these values to be very stable. The red bar shows the noise floor, which he calculated as the mode of RSSI readings taken when there were no transmissions. Since these were measured in a closed system, they are not affected by external interference. The prior curve was generated with an inferior data set, taken from an uncontrolled environment (such as Yin's).
rssiprr.pdf
Description: Adobe PDF document
The principal flaw of Kannan's data set is that it is for a single sender-receiver pair. A better way to do this would be to perform the experiment on a large number of pairs. However, sampling from such a data set would be non-trivial. Zuniga showed that radio hardware effects mean that you do not want to sample uniformly, rather use a hardware covariance matrix. I haven't heard of someone studying this for the CC2420. This approach addresses a flaw in TOSSIM in TinyOS 1.x: it sampled from a PRR/distance distribution independently, such that it did not model poor receivers.
It would be useful to understand why Yin's SNR/PRR data points are to the left of Kannan's curve. My first intuition would be that averaging N is pushing it up due to external interference. The paper does not say what channel the data in Figure 1 in Yin's paper was measured with. If it was overlapping with WiFi, then interference spikes could bias the N average.
The long and the short of this is that measurement methodologies are critical.
Phil
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