Akademik Veri Yönetim Sistemi
El-Cezeri Fen ve Mühendislik Dergisi, cilt.7, sa.1, ss.295-303, 2020 (Scopus)
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.
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.