On May 10, 2006, at 3:08 PM, Joe Polastre wrote:
What is preventing the release of your data? Levis and his student
have a paper already available in emnets, my paper was out last year,
so why can't you release your data? they're called technical reports
for a reason.
bottom line--you need to show compelling data to be taken seriously.
Here's a link to the camera-ready of Kannan's EmNets paper, which I
think has reasonably convincing data. It shows (most of) my evidence
that leads me to the same conclusion as Prabal: LQI is not a very
good link estimator. Full stop.
http://csl.stanford.edu/~pal/pubs/emnets06.pdf
High level points:
o LQI is correlated with packet loss
o individual LQI readings have high variance
o in our experiments, it takes a window of >50 packets to obtain an
average LQI within 10 units of the observed mean
o 10 units of LQI error is the difference between a 20% and 50% PRR,
or a 50% and 80%.
Note that if you send >50 packets, you can estimate (with DSN) PRR
within 2-4%. The low variance of RSSI makes it a much better
indicator of environmental changes. The *one* advantage that LQI has
over RSSI is that it is much more chip-independent, in that hardware
variations such as receive sensitivity (CC2420 advertises within 3dBm
but we've observed much greater variations) are factored out. But
it's not clear whether that's very useful in the end. The only way to
know is to build and compare link estimators, which no-one has done.
The long and the short of it is that the data in the Telos paper is
very misleading. As the X-axis is distance there is no way to know
the correlation between the values for individual directed node
pairs. The variance across readings in individual pairs makes LQI a
very inaccurate estimator. As far as I know, there has not been any
evaluation of whether LQI is effective, and by that I mean a
comparative evaluation against other techniques. The plots in the
Telos paper have led many network protocols to mistakenly use LQI
when it is not very good at all.
Phil
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