ABSTRACT The Internet of Things (IoT) has revolutionized multiple sectors by enabling seamless device connectivity and generating vast data streams. OneM2M, a global standard for IoT service platforms, provides interoperability across heterogeneous devices and applications. However, as IoT networks scale, ensuring quality of service (QoS) becomes a critical challenge. OneM2M, while efficient in managing device communication, struggles to maintain high performance under traffic congestion, impacting key QoS parameters such as latency (RTT), success rate, CPU, and RAM usage. This study evaluates the impact of traffic overload on OneM2M by introducing three major IoT communication protocols—HTTP, MQTT and CoAP—under real‐world traffic injection scenarios. Performance analysis revealed that CoAP exhibits the best efficiency in terms of low RTT and minimal resource consumption, making it ideal for constrained IoT environments. Conversely, MQTT with QoS Level 2 ensures the highest reliability, which is crucial for mission‐critical applications like e‐health. HTTP, while widely supported, suffers from excessive overhead, limiting its scalability. To understand and predict system overload, we employ queueing theory modeling to simulate different traffic intensities and identify performance bottlenecks. Our model categorizes system behavior into three zones—preferable, acceptable, and critical—based on system utilization. This theoretical approach was validated through a real‐world scenario, confirming the accuracy of our model in estimating QoS degradation under high traffic loads. To prevent system overload and optimize QoS, we propose an automated traffic orchestration approach based on the MAPE‐K framework. This method dynamically manages IoT traffic by prioritizing data streams based on SLA requirements and leveraging cloud resources to offload excess traffic. Our findings demonstrate that cloud integration significantly enhances scalability while maintaining optimal QoS for high‐priority traffic. Additionally, after integrating cloud‐based traffic redirection, we conducted a cost estimation study to assess the economic feasibility of our approach. This ensures that our solution not only enhances system performance but also remains cost‐effective for large‐scale IoT deployments. These results provide valuable insights into protocol selection, traffic modeling, and cost‐aware resource management in OneM2M‐based IoT ecosystems, ensuring higher efficiency, scalability, and service reliability.
Jamal et al. (Tue,) studied this question.