TL;DR
Experimental results demonstrate that the proposed framework significantly reduces end-to-end latency, improves energy efficiency, and increases task throughput compared to existing methods.
Experimental evaluation under dynamic IoT workloads demonstrates significant reductions in average task latency and response time compared with FCFS, Round Robin, centralized DQN, and conventional federated reinforcement learning schedulers.
Edge–cloud collaboration has become essential for managing the limited computational capacity of edge servers while meeting the dynamic and latency-critical demands of wireless devices in 5G and beyond networks. However, efficient offloading of interdependent tasks remains challenging due to heterogeneous computing res...
A novel hybrid framework that combines a Graph Attention Network (GAT)-based fault prediction model with a Generative Adversarial Network (GAN)-driven task migration decision model is proposed, achieving substantial reductions in task execution time and energy consumption while improving predictive accuracy and resourc...
The emergence of AI-native 6G networks necessitates efficient and proactive resource management mechanisms to support highly dynamic and data-intensive services. Existing approaches typically employ independent prediction models for mobility, handover, and channel quality, leading to redundant data processing and limit...
The rapid proliferation of Internet of Things (IoT) devices has placed unprecedented pressure on the network edge, where applications such as augmented reality, real-time analytics, and autonomous navigation demand low latency and tight energy budgets that traditional cloud-centric architectures cannot meet. Multi-acce...
An LLM-driven, context-aware framework that integrates real-time system metrics, historical data, and task-specific importance levels for anomaly detection and prediction is proposed, enabling proactive intervention before critical operating conditions are reached.
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