In this study, we propose new models for predicting
the average throughput in a 4x4 grid Constrained Application Protocol (CoAP)-based
IoT network using Support Vector Machine (SVM) and Multiple Linear Regression
(MLR). Two different CoAP congestion control mechanisms have been considered:
the default CoAP congestion control (CC) and the CoAP Simple Congestion
Control/Advanced (CoCoA). On the client-side, we run 3, 6, 9, 12 or 15 CoAP
clients requesting packets, sized with 12, 24, 36 or 48 bytes, from different
CoAP servers over 4x4 grid IoT network configured with packet delivery ratios
of 90, 95 or 100. In total, 60 different experimental scenarios, each of which
was run 10 times to determine the average throughput of default CoAP CC and
CoCoA clients, were created. Using 10-fold cross-validation, the performance of
the prediction models has been evaluated using several performance metrics. The
results show that combining packet delivery ratio and number of concurrently
sending clients in a model leads to the highest correlation with the average
CoAP throughput of the IoT network. Particularly, this model produces the
lowest prediction error among all SVM-based and MLR-based models, regardless of
whether the default CoAP CC or CoCoA is used as the congestion control
mechanism.
Bu çalışmada, Destek |
Primary Language | English |
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Subjects | Engineering |
Journal Section | Makaleler |
Authors | |
Publication Date | January 31, 2020 |
Submission Date | October 31, 2019 |
Acceptance Date | December 31, 2019 |
Published in Issue | Year 2020 Volume: 7 Issue: 1 |