Fuzzy-Logic-Based QoS Control in oneM2M: Adaptive Edge-to-Cloud Traffic Offloading

Abstract

Maintaining a stable Quality of Service (QoS) in oneM2M deployments is challenging because edge-to-cloud traffic in IoT systems is bursty and resource demand changes rapidly. We propose a fuzzy-logic QoS controller, integrated into a MAPE-K autonomic loop, that adaptively decides the share of traffic offloaded from the local oneM2M platform to the cloud as a function of CPU usage, Round-Trip Time (RTT), and incoming traffic rate. The controller uses a 27-rule Mamdani inference engine, formally defined trapezoidal membership functions, and centroid defuzzification, and is integrated with the open-source Mobius platform. Compared with an unmanaged baseline under peak load, our approach reduces operating cost by 43.5%, RTT by 55.9%, and increases the request success rate by 19.4%, while keeping CPU and RAM usage in the 40–50% range. A qualitative comparison with static-threshold and recent fuzzy/learning-based offloading methods, together with a discussion of scalability to hundreds of edge nodes, positions the controller as a practical and cost-effective option for oneM2M-compliant IoT platforms.