Jamal Et-Tousy

A novel federated learning approach for enhancing QoS and scalability in IoT networks

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

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...

Comparative QoS Analysis Between ITU-T Requirements and an Enhanced oneM2M Middleware: A Proof of Concept

An autonomic control architecture based on the Monitor-Analyze-Plan-Execute with Knowledge loop is integrated with a Random Forest classifier that predicts four discrete QoS operational states with 91.9% accuracy, making the compliance gap explicit and quantifiable.