Instrumentation & Measurement Magazine 25-6 - 29

Fig. 7. Effect of data size on transfer learning's performance in terms of (a) NPRE and (b) accuracy.
which was an Intel core i9-9900k workstation running Ubuntu
18.04 with 64 GB of RAM and Nvidia GeForce RTX 2080 as
GPU. As a reference point, it took 17 hours to train Deme with
the KING dataset. Please note that this is training time, which
happens only once. The prediction itself is instantaneous. Table
2 lists the results, including accuracy and the Ninetieth
Percentile of Relative Error (NPRE). NPRE is commonly used
by researchers to report network delay measurement efficiency.
As an example, an NPRE of 0.33 means that 90% of the
relative errors (Actual-Predicted)/Actual are below 0.33.
It is evident that the accuracy of Deme with transfer
learning on the expanded dataset is the best, at 93%, and the
only one above the required 90% accuracy. It is also interesting
to note that Deme with no changes actually performed
better than Deme trained form scratch with the new data.
The reason is that deep learning models require a large number
of data points to achieve good performance, usually in
the millions. Since the Swarmio dataset is quite small compared
to KING, the training from scratch did not achieve
good results. Fig. 6 presents the distribution of the relative
errors for all approaches with the expanded Swarmio dataset.

Instrumentation & Measurement Magazine 25-6

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