Joint Computation Offloading and Data Caching in Multi-Access Edge Computing Enabled Internet of Vehicles

Internet of Vehicles (IoV) has attracted global research interests across extensive applications. Due to the significant increase in the number of vehicles accessing the Internet, there are several challenges in designing efficient task offloading and data caching strategies to improve the utilization of the network resource and provide the users with high-quality services. To this end, this study proposes the task offloading and resource allocation schemes, including the selection of execution mode, data transmission path, the assignment of the sub-channels, the strategies of caching and caching updating in a Multi-Access Edge Computing (MEC) enabled IoV system with multiple mobile vehicles equipped with the capacity of energy harvesting. Specifically, the downlink relevant data or the uplink offloaded data can be transmitted through either the Macro Base Station(MBS) or the Road Side Unit(RSU). Also, we consider two different situations: off-peak hours and peak hours, in which the execution mode is different. In off-peak hours, the tasks can directly offload to the MEC server, and the average execution delay minimization problem is modelled as an integer programming problem, which is solved by Simulated Annealing Genetic Algorithm (SAGA). In peak hours, the tasks can be either executed locally or offloaded to the MEC server, and the formulated problem are more complicated, which are solved by Deep Q Network (DQN). Finally, a series of simulations are conducted to demonstrate the efficiency of the proposed schemes.