all of the variables were measured at the same time--lqi and rssi. it does not take a mental leap to understand the findings in an open air environment.
prabal, I would be extremely enthusiastic to review your real world data on this topic. please send your studies to the list as I believe we all could profoundly benefit from the data behind your opinions. -Joe On 5/9/06, Prabal Dutta <[EMAIL PROTECTED]> wrote:
The Telos graphs are nice in that they show the relationship between some of these variables but some mental gymnastics are required to make the leap from what's shown to PRR vs RSSI and PRR vs LQI. And, given the variances involved, I'm not sure that everyone would come to the same conclusion. I would argue that from a PRR, RSSI, and LQI perspective, distance is a largely unnecessary nuisance variable. Distance (and position) ultimately affect RSSI and LQI, and perhaps knowing the distance helps in some way to determine PRR, but at the physical level, RSSI to interference and noise largely determine whether packets are received. So, if you can directly measure and correlate PRR, RSSI, and LQI, then it would make sense to do so and ignore any intermediate parameterization (like distance). - Prabal On 5/9/06, Robert Szewczyk <[EMAIL PROTECTED]> wrote: > There is a graph that shows a dependence between distance and PRR, > LQI, and RSSI in the paper about Telos design (figure 5 in > http://www.polastre.com/papers/spots05-telos.pdf) > While it does not actually show the regression, and distance is made > explicit, it should at least give you a flavor of what to expect out > of each type of measurement > > Rob > > On 5/9/06, Prabal Dutta <[EMAIL PROTECTED]> wrote: > > Yes, there is *some* relation: they're positively correlated. But on > > the CC2420 radio, LQI shows significant variance for a given packet > > reception rate (PRR). You may be much better off using RSSI, which > > shows far less variance for a given PRR. > > > > You can use (logistic) regression to determine the relationship the variables: > > - send a lot of packets between a lot of motes > > - compute the PRR as the fraction of packets received over the number > > sent for each tx, rx link pair > > - note the RSSI and LQI values > > - Plot (and regress) PRR vs RSSI and PRR vs LQI > > > > Hope that helps. > > > > - Prabal > > > > On 5/9/06, Venkat Manoj <[EMAIL PROTECTED]> wrote: > > > > > > Hi all, > > > > > > Can anybody tell me if there is some relation between the link quality > > > indiactor (LQI) and the probability of packet loss, between two motes? Or is > > > there some method to find out the relation? > > > > > > Thanks, > > > Venkat. > > > _______________________________________________ > > > Tinyos-help mailing list > > > [email protected] > > > https://mail.millennium.berkeley.edu/cgi-bin/mailman/listinfo/tinyos-help > > > > > > > > > > > > > _______________________________________________ > > Tinyos-help mailing list > > [email protected] > > https://mail.millennium.berkeley.edu/cgi-bin/mailman/listinfo/tinyos-help > > > _______________________________________________ Tinyos-help mailing list [email protected] https://mail.millennium.berkeley.edu/cgi-bin/mailman/listinfo/tinyos-help
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