Queue-Aware Congestion Avoidance in IoHT: Enabling Future Integration With Large Models for Transmission Optimization

Abstract

The internet of healthcare things (IoHT) has advanced considerably, improving healthcare operations and patient monitoring by continuously collecting data from health sensors attached to patients. Current congestion detection techniques are insufficient for early detection since senders often remain unaware of the size of the residual queue. The real-time transmission of critical health data is essential, yet frequent congestion at intermediate nodes can lead to increased packet loss, delays, and diminished system reliability. To tackle these challenges, we propose a robust and low-complexity QACA algorithm tailored specifically for patient-centric IoHT networks, which dynamically adjusts the frequency of acknowledgments based on real-time queue occupancy thresholds. By integrating interval-based acknowledgments with a priority-based queuing strategy, QACA ensures that high-priority medical data is transmitted promptly, even in the face of heavy network loads. Simulation results indicate that QACA significantly improves performance over the analytical model and DCCA regarding packet loss and packet delay reduction. Moreover, the current framework may be enhanced in future work with the use of LMs to add predictive estimation of queue status, traffic classification, and an intelligent transmission scheduling, thus paving the way toward scalable and intelligent congestion management in next-generation IoHT systems.