Energy and Delay Minimization Based on Game Theory in MEC-Assisted Vehicular Networks

As a new technology in the fifth generation (5G) mobile networks, mobile edge computing (MEC) can reduce network computations and shorten task-processing delay by offloading the tasks to nearby vehicles with idle resources. However, such technology needs more vehicles to participate in task processing, increasing the network computations. In this paper, we propose a MEC-assisted vehicular network where vehicles can offload their tasks via vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) links. The vehicles in the same links will interfere with each other during offloading tasks, which affects energy consumption and delay. To minimize the network computation overhead and extend the battery lifetime of the vehicles, task offloading decision-making is optimized in this paper. We investigate the problem of MEC computation offloading in the vehicular networks and propose a game-based computation offloading (GBCO) algorithm and an optimal offloading (OO) algorithm. We demonstrate that the proposed algorithms can achieve the Nash equilibrium (NE) and converge after the finite improvement property (FIP). Simulation results show that the proposed GBCO algorithm can increase the convergence rate and the proposed OO algorithm can reduce the energy consumption.