Energy-Delay Minimization of Task Migration Based on Game Theory in MEC-Assisted Vehicular Networks

Roadside units (RSUs), which have strong computing capability and are close to vehicle nodes, been widely used process delay- computation-intensive tasks of nodes. However, due their high mobility, vehicles may drive out the coverage RSUs before receiving task processing results. In this paper, we propose a mobile edge computing-assisted vehicular network, where can offload nearby via vehicle-to-vehicle (V2V) link or RSU vehicle-to-infrastructure link. These also migrated by V2V an infrastructure-to-infrastructure (I2I) avoid scenario cannot receive processed from RSUs. Considering mutual interference same offloading migrating tasks, construct decision-based game minimize computation overhead. We prove that always achieve Nash equilibrium convergence exploiting finite improvement property. then migration (TM) algorithm includes three task-processing methods two task-migration methods. Based on TM algorithm, overhead minimization (COMO) is presented. Extensive simulation results show proposed COMO algorithms reduce increase success rate processing.