Computation Offloading in MEC-Enabled IoV Networks: Average Energy Efficiency Analysis and Learning-Based Maximization

This paper investigates the energy efficiency of computation offloading strategies in multi-access edge computing-enabled (MEC-enabled) Internet-of-Vehicles (IoV) networks. First, the energy efficiency of computation offloading strategies in the MEC-enabled IoV network are derived in closed-form. Thereafter, a multi-agent deep reinforcement learning based (MADRL-based) energy efficiency maximization algorithm is proposed to enable computation offloading strategies to attain maximum energy efficiency in the MEC-enabled IoV network. It is shown through extensive analysis that the maximum attained energy efficiency hinges on the choice of task size and transmission timeout threshold, with a computation offloading strategy that jointly considers transmission and computation latencies outperforming existing strategies. It is also shown that the proposed MADRL-based energy efficiency maximization algorithm achieves near-optimal energy efficiency in the MEC-enabled IoV network, making it a promising solution towards achieving energy efficient MEC-enabled IoV networks.