Intelligent Edge-Cloud Data Management with a Predictive Smart Offloading Proxy for 5G Internet of Vehicles

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.

SDN-Assisted Low-Latency Resource Management for V2X Communication in Heterogeneous Cellular Networks

The proposed framework separates network control from forwarding, maintains a global view of vehicular network state, classifies V2X flows by service criticality, and dynamically selects routes and bandwidth allocations using delay, congestion, handover, and priority constraints.

Multi-Queue Priority-Based Scheduling for Real-Time Edge Computing Applications

The rapid proliferation of Internet of Things (IoT) devices has intensified demands for low-latency, resource-efficient task scheduling at the network edge. Conventional policies such as Round-Robin and First-Come-First-Serve (FCFS) fail to satisfy the Quality-of-Service (QoS) requirements of Industrial-IoT and autonomous-vehicle workloads. This paper presents Multi-Queue Priority-Based Scheduling (MQPBS), a lightweight algorithm that classifies tasks into three dynamic priority queues (High, Medium, Low) using deadline-aware heuristics, applies Shortest-Job-First (SJF) intra-queue ordering, and employs an aging mechanism to prevent starvation. Extensive simulation over task sets of 200–1000 tasks demonstrates that MQPBS reduces average waiting time by up to 17.6%, improves throughput by up to 10.8%, lowers energy consumption by 20%, and cuts the Deadline Miss Ratio (DMR) compared with the Priority-Aware Task-Scheduling (PaTS) baseline. Ablation experiments confirm the independent contribution of each algorithmic component. Scalability and sensitivity analyses further validate the robustness of MQPBS under heterogeneous arrival patterns and varying load intensities. The results establish MQPBS as a scalable, reliable scheduler for next-generation edge infrastructures.

Multi-application operator placement in cloud-edge infrastructure for big data stream processing

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.

Energy-Optimized Federated Aggregation for Predictive Networking in Vehicular Cloud Architectures

An Energy-Optimized Federated Aggregation architecture of Predictive Networking in Vehicular Cloud Architectures (EOFA-PNVC) integrating client selection, gradient compression, and an energy-aware weighting scheme with a forecasting head that handles short-horizon state prediction of networks is suggested.