An Adaptive Learning Framework for Real‐Time Service Provisioning in Vehicular Networks Using Mobile Edge Computing

ABSTRACT In this paper, propose an adaptive learning‐based MEC framework for real‐time roadside service provisioning in vehicular networks. This novel decision‐supporting framework is used to optimize the task offloading, resource allocation and service scheduling in dynamic vehicular environment combined the machine learning technology. It is under the condition that it cooperates with RSUs to compute and send tasks. Referring to decomposition the offloading and resource management problem into sub‐problems, addressing them via the deep reinforcement learning, this mixed‐integer nonlinear optimization problem is established. The results in simulations demonstrate that the proposed model offers significantly higher success rates than baselines under various traffic loads, processing rates, number of MEC servers and computation resource requirements, which confirms its robustness and potential applications for ITS.