Integrating Deep Learning Models for QoS Optimization in IoT Using the OneM2M Platform

The integration of IoT systems into diverse domains has increased the demand for efficient Quality of Service (QoS) optimization to handle the vast and dynamic data flows. In this work, we explore the potential of deep learning models to enhance QoS parameters such as latency, throughput, and resource utilization within the OneM2M standard. We specifically investigate AutoEncoders (AE), Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs). Each model was designed and fine-tuned to address specific aspects of QoS optimization. Experimental results demonstrate the ability of these models to achieve high accuracy, efficient reconstruction, and improved classification performance on QoS metrics. This research presents a novel approach to leveraging deep learning models in IoT systems compliant with the OneM2M standard, facilitating intelligent, data-driven QoS management.