Study on Adaptive Neural Network-based Congestion Control Algorithm in CoAP-based IoT Networks
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The scope of IoT services continues to expand with the development of lightweight devices, and network congestion is increasing significantly. In this study, we propose an adaptive neural network-based congestion control (ANN-CoCC) algorithm to solve the network congestion problem in the network environment of IoT service environments. Unlike existing congestion control methods that rely on static parameters or heuristic rules, ANN-CoCC applies a machine learning model to dynamically optimize key transmission parameters such as retransmission timeout (RTO), RTT distribution, and congestion window according to real-time network conditions. The experimental evaluation results under various packet loss rates and node densities show that the proposed algorithm improves throughput by more than 30%, reduces delay, and minimizes packet loss compared to existing methods such as CoCoA+, BDP-CoAP, and pCoCoA.
Abstract
The scope of IoT services continues to expand with the development of lightweight devices, and network congestion is increasing significantly. In this study, we propose an adaptive neural network-based congestion control (ANN-CoCC) algorithm to solve the network congestion problem in the network environment of IoT service environments. Unlike existing congestion control methods that rely on static parameters or heuristic rules, ANN-CoCC applies a machine learning model to dynamically optimize key transmission parameters such as retransmission timeout (RTO), RTT distribution, and congestion window according to real-time network conditions. The experimental evaluation results under various packet loss rates and node densities show that the proposed algorithm improves throughput by more than 30%, reduces delay, and minimizes packet loss compared to existing methods such as CoCoA+, BDP-CoAP, and pCoCoA.