Hybrid CNN-LSTM Network for Adaptive QoS Optimization in MQTT-Based IoT Systems

Abstract

Internet of Things (IoT) connections depend on the Message Queuing Telemetry Transport (MQTT) protocol; however, it might be difficult to determine the best Quality of Service (QoS) level in dynamic network contexts.  Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are combined in this study's adaptive deep learning framework to optimize MQTT QoS in real time.  To depict a variety of IoT circumstances, such as resource limitations, high load, network instability, and regular operations, we created a thorough labeled dataset of 50,000 synthetic samples.  In all investigated scenarios, the hybrid CNN-LSTM architecture maintained 71.44% resource efficiency while achieving 92.7% validation accuracy in QoS prediction.  Our system showed great confidence in adapting to essential applications (98.83%), low-resource environments (99.78%), and high-load conditions (99.99%). For industrial IoT deployments in smart manufacturing, healthcare monitoring systems, and critical infrastructure management—where dependable communication under fluctuating resource constraints is crucial for operational efficiency and safety—this adaptive QoS optimization framework shows great promise. The suggested architecture greatly improves network performance while preserving reliability by providing a workable solution for autonomous QoS control in resource-constrained IoT installations.