Efficient and Trusted Task Offloading in Vehicular Edge Computing Networks

In order to meet the ever-increasing task processing demands of computation-intensive and delay-sensitive applications in the era of autonomous driving, a promising approach is to adopt nearby roadside units (RSUs) or/and vehicles passing by to provide edge computing services, i.e., vehicular edge computing (VEC). However, due to the untrustworthiness of fast-moving vehicles, the vehicles' tasks may face false result attacks or processing timeout. Note that there is little research on the vehicle trust evaluation in VEC networks, especially taking processing delay minimization into consideration. Toward this end, this paper studies the joint optimization problem of vehicle trust evaluation and task processing delay, aiming to ensure the security of the vehicles with tasks and minimize the task offloading delay. To solve this problem, we propose an efficient and trusted VEC offloading scheme based on fuzzy comprehensive strategy (FCS) and adopt the concept of game theory to motivate nearby vehicles to share computing resources. Experimental results corroborate that our proposed scheme can accurately evaluate the trustworthiness of vehicles and improve service security in VEC networks. Moreover, it can significantly reduce task offloading delay.