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

Abstract

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.