Incentive Mechanism for Task Offloading and Resource Cooperation in Vehicular Edge Computing Networks: A Deep Reinforcement Learning-Assisted Contract Approach
Nan Zhao, Yiyang Pei, Dusit Niyato
Abstract
Vehicular edge computing network emerges as a key technique to offload vehicles’ tasks to the nearby roadside unit (RSU). Considering that the RSU may not always meet computation requirements of task vehicles (TVs), utilizing idle computing resources of the surrounding resource vehicles (RVs) becomes a feasible solution to enhance the TVs’ experience. Due to the self-interested property and limited resource of RVs, an efficient incentive mechanism should be designed to encourage RVs to participate in task offloading and resource cooperation. In this work, a deep reinforcement learning-assisted contract incentive mechanism is investigated by considering TVs’ task offloading requirements, RVs’ computation resources, and the RSU’s transmission time incentive. To truthfully reveal TV-RV task-resource coordination types, a contract is designed with computation task-transmission time contract items. The joint task offloading and resource cooperation optimization issue is formulated to maximize the RSU’s utility with incentive compatible (IR), individual rationality (IC), and task offloading constraints. The optimal transmission time strategy is first derived from IR and IC constraints. To obtain the task offloading and task data size strategies, a Markov decision process is formulated. A multiagent parametrized deep Q-network scheme is developed to handle the discrete-continuous hybrid action space problem. Numerical simulations show the feasibility and effectiveness of our proposed incentive method to solve the joint task offloading and resource cooperation problem.