Joint Task Offloading and Resource Allocation for Multi-Access Edge Computing Assisted by Parked and Moving Vehicles

In the Internet of Vehicles (IoV) scenarios, vehicles are equipped with computing resources to support vehicle-oriented IoV applications. Meanwhile, these computing resources can be also leveraged to enhance the task processing performance for the devices in Multi-access Edge Computing (MEC) scenarios, and alleviate the load of edge servers. Existing research works rarely consider the resource utilization of moving vehicles, which can be an important complement to the MEC schemes with the assistance of parked vehicles. In this paper, a joint task offloading and resource allocation scheme is proposed for a parked-and-moving-vehicles-assisted MEC scenario consisting of multiple devices, parked vehicles, and moving vehicles covered by a Base Station (BS) equipped with an edge server. The tasks of the devices can be either offloaded to the BS or further offloaded from the BS to the vehicles. The service time that a moving vehicle can provide its task offloading service before it moves out of the coverage of the BS, are taken into account of our system model. The aim of our scheme it to minimize the total priority-weighted task processing delay for all the devices through offloading the tasks to the edge server or to the vehicles, allocating the wireless channels of the BS, and allocating the computing resource of the edge server and the vehicles. A generalized benders decomposition and reformulation linearization-based iterative algorithm is designed to obtain the optimal solution to the optimization problem, and a two-stage heuristic algorithm is also given to provide near-optimal solutions with low computational complexity. The simulation results demonstrate the superiority of our scheme in seven different scenarios by comparing it with three other schemes.