Mobility-Aware Partial Task Offloading Scheme for Vehicular Edge Computing

Vehicular edge computing (VEC) leverages roadside units (RSUs) to provide low-latency and energy-efficient computation for vehicular networks. However, existing offloading schemes still face challenges under high vehicle mobility, dynamic network conditions, uneven RSUs loads, and different Quality of Service (QoS) requirements. To address this, we propose a mobility-aware partial task offloading (MAPRO) framework that partitions tasks for parallel processing across multiple RSUs while considering mobility, network dynamics, and QoS demands. A hybrid framework combining Proximal Policy Optimization (PPO) and numerical optimization efficiently solves offloading, resource allocation, and power control. Experiments on real trajectories show that MAPRO outperforms state-of-theart methods.