Joint task offloading and resource allocation in vehicle-assisted multi-access edge computing

Multi-access Edge Computing (MEC) has significant advantages in improving resource efficiency of Internet of Things (IoT) and 5G networks, however its limited resources cannot meet the demand of data communication and computation capability during off-peak time. Incentivizing intelligent vehicles with idle computation resources as vehicle edge nodes (VENs) to provide computation offloading for nearby user equipments (UEs) is an appealing idea. Thus, we propose a vehicle-assisted MEC (VMEC) paradigm, where tasks can be offloaded to MEC server and VENs. In this paper, we first establish a differentiated pricing model based on different states of resources and a dynamic incentive model according to the demands of UEs. Then, we formulate a Stackelberg game between UEs and MEC service provider (MEC SP) to obtain the optimal offloading strategy and pricing scheme. A gradient-based resource allocation iteration algorithm (GRAIA) is designed for the Nash equilibrium solution. Finally, considering the matching between UEs and vehicles, we present a reverse auction-based task scheduling algorithm (RATSA) to choose VENs. The simulation results demonstrate that the proposed scheme can achieve significant performance improvement and is superior to the existing schemes in improving system utility.

Automated summary

The proposed vehicle-assisted MEC (VMEC) paradigm incentivizes intelligent vehicles with idle computation resources to provide computation offloading services, aiming to improve system utility.