Congestion Control In Resource-Constrained iot Networks Using RED and Machine Learning Binary Classifiers For Event-Driven Applications.

Abstract

Numerous industries have undergone radical change as a result of the Internet of Things' (IoT) innovative solutions for event-driven applications like real-time monitoring and emergency detection. Packet loss, congestion, and excessive latency are major challenges that affect the reliability and performance of IoT networks, especially those with limited resources. These problems require efficient congestion control techniques that are tailored to resource-constrained scenarios. A novel congestion control method is proposed in this work, which combines Random Early Detection (RED) with machine learning-based binary classifiers. The binary classifiers predict congestion states and dynamically modify RED thresholds based on important network metrics, such as queue length, packet latency, and packet loss rate. The RED algorithm is dynamically adaptive, datasets are prepared, and models are trained using techniques like Random Forest and Support Vector Machine. The goal of this approach is to minimize traffic surges. The findings demonstrate that the suggested method works noticeably better than conventional RED systems. Congestion estimations become more accurate and flexible due to the Random Forest classifier's high 95% accuracy rate. Through dynamic threshold changes, the system dramatically reduced packet loss and delays in simulations, demonstrating its usefulness for resource-constrained IoT networks supporting event-driven applications. Additionally, the suggested approach demonstrates durability and scalability in a variety of IoT applications. This paper advances the field of IoT by addressing the shortcomings of static models in dynamic IoT situations by offering an efficient and adaptable congestion control strategy. Future work will include additional optimization for different traffic patterns and network configurations as well as real-world implementation.