Mobility-Aware Offloading with SINR Prediction and Selective Replication in Fog-IoT Networks

A Modified Lyapunov-Based MEC Offloading Algorithm for Severity-Aware QoS in 5G/B5G IoT Systems

The rapid growth of latency-sensitive and computation-intensive IoT applications in 5G and Beyond-5G (B5G) networks has increased the demand for efficient Multi-access Edge Computing (MEC) offloading strategies. Current MEC frameworks have several limitations: 1) binary QoS modeling without considering deadline violation severity, 2) a lack of severity-aware optimization in IoT applications, 3) insufficient consideration of different task criticality, and 4) poor handling of dynamic latency and energy trade-off in large-scale IoT environments. This study proposes a modified Lyapunov-based severity-aware MEC offloading framework for heterogeneous 5G/B5G IoT systems. The proposed framework utilizes task deadlines, task criticality, queue states, wireless channel conditions, and MEC resource availability as input for adaptive offloading optimization. A QoS Violation Severity Index is introduced to jointly capture deadline violation magnitude and task criticality. Furthermore, severity-aware virtual queues are integrated with a modified Lyapunov Drift-Plus-Penalty optimization framework to dynamically minimize QoS violation severity while balancing latency and energy consumption. Experimental evaluation demonstrates that the proposed framework significantly reduces average task delay to 82 ms, energy consumption to 6.0 mJ, and QoS violation rate to 4.8%, while improving long-term system stability compared with existing MEC offloading approaches in dynamic 5G/B5G IoT environments.

Lightweight IoT Node Offloading Framework for Real-Time Edge Analytics

The rapid growth of Internet of Things (IoT) deployments has intensified the need for efficient, decentralized computation management at the network edge. This paper presents a lightweight, neighbor-aware one-hop task offloading framework designed for resource-constrained IoT networks. The proposed adaptive scheme combines Exponential Weighted Moving Average (EWMA) load estimation with a queue-depth gate to prevent unnecessary offloading under transient load spikes, and an assignment-pressure mechanism to distribute tasks more evenly across neighboring nodes. We evaluate the framework using a custom-developed discrete-event simulator on a 90-node ringplus-chord topology with heterogeneous hotspot and light nodes, comparing against three baselines: local-only execution, random offloading, and least-loaded neighbor selection. Results show that a load-aware but pressure-unaware least-loaded strategy surprisingly produces the highest load variance (377.25), worse than random offloading (114.84), due to severe task funneling toward persistently fast nodes. The proposed scheme eliminates task drops entirely, achieves an average latency of 148.7 ms, and reduces task-count variance to 44.33 - an $8.5 \times$ improvement over the least-loaded baseline and 4.6× over local-only execution - while requiring only 28.21% of tasks to be offloaded. These results demonstrate that assignment-pressure tracking is essential for fair load distribution in energy-limited IoT deployments.

MOTPS: Multi-Objective Optimization and Trajectory Prediction for Task Offloading in SDN-Based Vehicular Edge Networks

PPO-Based Task Offloading for Deadline-Aware IoV Services in a Real-Data-Driven Environment

Task offloading is a key enabler for delay-sensitive Internet of Vehicles (IoV) services, where vehicular applications must be executed under strict latency constraints. This paper proposes a Proximal Policy Optimization (PPO)-based binary offloading framework that selects between Multi-access Edge Computing (MEC) and Cloud execution. Unlike purely simulation-based approaches, the proposed framework is built on a real-data-driven environment derived from vehicular mobility traces and measured service-delay observations collected from the Modena Automotive Smart Area (MASA) testbed. The RL agent observes mobility and delay-related features and learns a deadline-aware offloading policy through reward-driven interaction with the environment. Experimental results under a 50 ms deadline show that PPO achieves the best overall trade-off between task acceptance and delay control, while providing a more stable service behavior than fixed baselines.

A two-layer optimized architecture for energy- and QoS-aware fog computing in distributed IoT ecosystems

Findings confirm that system stability and service quality are bounded by fog density, QoS-aware routing, and real-time load regulation, rather than by mere resource scaling.

Topology-Aware GNN-DQN for Optimal Task Offloading in Edge–Fog–Cloud Systems

The Internet of Things (IoT) has grown rapidly in recent years, enabling the interconnection of a large number of heterogeneous and distributed devices. This number is expected to exceed 70 billion according to Statista. With this massive scale, fulfilling complex IoT applications that require combinations of multiple objects remains a real challenge. Moreover, several Quality of Service (QoS) requirements must be satisfied, making the problem of selecting appropriate IoT services NP-hard. In such environments, task offloading is a key mechanism to efficiently distribute computational workloads across edge, fog, and cloud resources. However, selecting the optimal offloading decision remains a difficult NP-hard problem due to system heterogeneity and conflicting objectives. In this paper, we propose a GNN-DQN-based approach for task offloading in edge–fog–cloud environments. Unlike prior GNN-DQN approaches limited to single- or dual-tier architectures, our framework explicitly models heterogeneous node types and inter-tier communication links, enabling more balanced and scalable resource allocation. Experimental results show that GNN-DQN achieves a mean latency of 2.64 s, representing improvements of 70.2% over Random, 7.8% over DQN-only, and 3.5% over Greedy. A GNN-A2C baseline is also included to broaden the comparison with a modern DRL method. Despite sharing the same GNN encoder, it underperforms GNN-DQN across all metrics, confirming the superiority of the DQN learning backbone. These results highlight the effectiveness of integrating graph-based representation with reinforcement learning, while also revealing a trade-off between latency optimization and energy efficiency.