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.
The increasing deployment of latency-sensitive Internet of Things (IoT) applications has intensified the need for intelligent task scheduling mechanisms in fog computing environments. Conventional scheduling approaches, including heuristic and centralized machine learning techniques, often fail to adapt to dynamic workload variations and mobility-induced network changes, resulting in increased task latency. This paper proposes a Predictive Latency-Aware Federated Deep Reinforcement Learning (PLA-FDRL) framework for adaptive task scheduling in IoT-enabled fog networks. The proposed framework integrates latency prediction, mobility-aware fog node selection, and federated deep reinforcement learning to proactively allocate tasks to optimal fog resources. Each fog node independently trains a Deep Q-Network (DQN) using local observations and periodically participates in federated aggregation without sharing raw data. A latency-aware reward function jointly minimizes transmission, queueing, processing, and migration delays. 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. Results indicate that the proposed framework improves responsiveness and scalability while preserving data privacy.
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 resources, time-varying wireless channels, and complex dependency structures among subtasks. This paper proposes a Quantum Graph Attention Network-based Deep Reinforcement Learning (QGAT-DRL) framework for dependency-aware task offloading in collaborative edge–cloud systems. Computation tasks generated by user equipments (UEs) are represented as directed acyclic graphs (DAGs) to capture subtask dependencies, while a quantum-enhanced graph attention mechanism exploits superposition and entanglement to encode high-order correlations across wireless and computing layers with improved representational efficiency. The offloading policy is optimized through a Proximal Policy Optimization (PPO) algorithm to jointly minimize task completion latency and energy consumption. Simulation results demonstrate that QGAT-DRL achieves faster convergence, lower latency, and superior energy efficiency compared with state-of-the-art baselines, demonstrating the promise of hybrid quantum–classical learning for scalable resource orchestration in next-generation edge–cloud networks.
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 resource efficiency.
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 limited exploitation of cross-layer dependencies. In this paper, we propose a shared data intelligence–driven multi-task prediction framework that jointly models mobility, handover, and channel quality indicator (CQI) within a unified learning architecture. By leveraging a common feature space and a single processing pipeline, the proposed framework simultaneously generates multiple correlated predictions, thereby reducing computational overhead. A correlation analysis using real-world datasets demonstrates that mobility, CQI, and handover events exhibit inherent inter-dependencies, justifying the use of a shared representation. Furthermore, a processing time comparison shows that the proposed approach achieves approximately 10.12% reduction compared to conventional independent prediction models by eliminating redundant feature extraction and repeated model execution. These results validate that shared data intelligence is an effective and scalable solution for efficient multi-task prediction in real-time 6G network environments.
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-access Edge Computing (MEC) addresses this gap by relocating computation closer to end users, but the core question of where and how each task should be executed remains open: rulebased and single-objective offloading strategies fail to simultaneously balance service latency, energy efficiency, and user experience under dynamic, large-scale conditions. In this paper we propose TARLOT (Two-Agent Reinforcement Learning Offloading Tasks), a cooperative framework for threetier IoT–MEC–Cloud environments. TARLOT decouples the offloading decision from the resourceallocation problem and assigns each to a dedicated Q-learning agent, so that the two subproblems are specialised independently while still being optimised jointly. The framework is evaluated on PureEdgeSim under heterogeneous IoT workloads, device densities ranging from 200 to 2,400, and diverse application profiles, and is compared against five widely-used baselines (Random, Round-Robin, Trade-Off, Pure-Edge, and Pure-Cloud). At 2,400 devices, TARLOT delivers an average service time of 1.1 s (against 4.3 s for Pure-Cloud), a Quality of Experience of 0.77 (against 0.22 for Pure-Cloud), a task-failure rate below 2 % (against nearly 14 % for Pure-Cloud), and a per-device energy consumption of only 3.6 W (against 11.2 W for Pure-Cloud) — roughly a 68 % reduction. Balanced CPU utilisation across the local, edge, and cloud tiers further confirms that TARLOT prevents resource bottlenecks, establishing it as a practical solution for next-generation large-scale IoT deployments.
Resource-constrained edge devices require efficient and adaptive health monitoring to ensure reliable operation under dynamic workloads. This paper proposes an LLM-driven, context-aware framework that integrates real-time system metrics, historical data, and task-specific importance levels for anomaly detection and prediction. Specifically, the framework forecasts the semantic health state of the edge device five minutes ahead based on recent monitoring observations, enabling proactive intervention before critical operating conditions are reached. Multidimensional metrics, including CPU, memory, temperature, network load, and process information, are transformed into structured time-series representations and used as input to pre-trained machine learning models. A large language model (LLM) acts as an orchestration layer, dynamically selecting the most appropriate predictive model based on system context and resource constraints. This enables adaptive trade-offs between accuracy, latency, and computational cost. Experimental results on Raspberry Pi devices show that the proposed approach achieves comparable or improved performance while reducing resource usage compared to static methods.