Task Offloading in SDN-Fog Architecture Utilizing Hybrid Deep Learning Models for Internet of Medical Things

SDNCampus: Application Classification Dataset for SDN Enabled Campus Networks

The exponential growth and sophistication of campus networks tailored with advanced applications has resulted into network congestion and Quality of Service (QoS) degradation. Meanwhile, the emerging paradigms of Software Defined Networking (SDN) and Deep Learning (DL) are promising technologies that enable network re-...

JATO: Deep Reinforcement Learning-based Joint Optimization for Task Offloading and Adaptive Transmission in Multimedia IoT Systems

JATO is presented, a framework to jointly tackle the problems of adaptive task offloading and transmission optimization using Deep Reinforcement Learning, and offers a mono-faceted solution, learning a policy to simultaneously determine the best offloading target and the transmission quality.

AI-Native Deep Learning Framework for Ultra-Reliable Low-Latency IoT Networks with Federated Edge Intelligence

PISMA: Programmable In-Switch Telemetry and Multi-Stage Adaptive Deep Learning for Threat Mitigation in SD-IoT

The convergence of Software-Defined Networking and the Internet of Things (SD-IoT) enhances network orchestration but exposes centralized controllers and application-layer services to sophisticated, multi-stage cyber threats such as structural web injections and stealthy application-layer floods. This study aims to dev...

DLCOF: A DEEP LEARNING–INFUSED CROSS-LAYER OPTIMIZATION FRAMEWORK WITH FUZZY LOGIC FOR ENERGY-EFFICIENT ROUTING IN WIRELESS NETWORKS

The wireless networks require dynamic routing systems that can maintain energy efficiency, reliability, and spectrum usage. This is typically not the case in traditional routing strategies because the strategies are usually configured at the static level and have inadequate coordination across the layers. The proposed...

AI Analytics in SDN Network Management: A Review

Software-defined networking (SDN) fundamentally reshapes network management by separating control logic from data forwarding, enabling centralized, programmable control at massive scale. Yet this shift brings new challenges including centralized controller vulnerabilities, unpredictable traffic patterns, and quality...