COCO-QN: An Efficient Content-Aware Congestion Control Mechanism for Maintaining QoS in NDN

Named Data Networking (NDN) is a promising architecture for the Internet of Things (IoT), where massive devices and heterogeneous traffic demand efficient data transmission and strict Quality of Service (QoS). However, existing congestion control mechanisms in NDN struggle to achieve effective load balancing under multipath transmission and rarely consider the distinct QoS requirements of different content types. To address these limitations, this paper proposes an efficient Content-Aware Congestion Control Mechanism for maintaining QoS in NDN (COCO-QN). This mechanism achieves congestion control through probability-based forwarding, content-aware caching, and routing, providing QoS guarantees for real-time content while maintaining overall throughput. For IoT networks, COCO-QN enables the timely and reliable transmission of real-time content even under high network load. Meanwhile, congestion control and content-aware QoS contribute to stable network operation and reliable service delivery under dense IoT traffic conditions. Simulation results demonstrate that COCO-QN outperforms four state-of-the-art mechanisms. In mixed traffic scenarios with bursty non-real-time flows and real-time services, COCO-QN reduces the mean RTT of real-time traffic by 31.4% and jitter by 84.1% compared with the baseline, while maintaining aggregate throughput without degradation. These results confirm that COCO-QN provides efficient congestion control, stable data transmission, and QoS guarantees in NDN-based IoT networks.