The Internet of Vehicles (IoV) technology is progressively maturing because of the growth of private cars and the establishment of intelligent transportation systems. The development of smart cars has, therefore, been followed by a parallel rise in the volume of media and video games in the automobile and a massive increase in the need for processing resources. Smart cars cannot process the enormous quantity of requests created by vehicles because they have limited computing power and must maintain many outstanding jobs in their queues. The distribution of edge servers near the customer side of the highway may also accomplish real-time resource requests, and edge servers can assist with the shortage of computational power. Nevertheless, the substantial amount of energy created while processing is also an issue we must address. A joint task offloading strategy based on mobile edge computing and fog computing (EFTO) was presented in this paper to address this problem. Practically, the position of the processing activity is first discovered by obtaining the computing task's route, which reveals all the task's routing details from the starting point to the desired place. Next, to minimize the time and time expended during offloading and processing, a multi-objective optimization problem is implemented using the task offloading technique F-TORA based on the Takagi–Sugeno fuzzy neural network (T-S FNN). Finally, comparative trials showing a decrease in time consumed and an optimization of energy use compared to alternative offloading techniques prove the effectiveness of EFTO.

随着私家车的增长和智能交通系统的建立，车联网（IoV）技术逐渐成熟。因此，随着智能汽车的发展，车内媒体和视频游戏的数量也随之增加，对处理资源的需求也大幅增加。智能汽车无法处理车辆产生的大量请求，因为它们的计算能力有限，并且必须在队列中保留许多未完成的作业。在高速公路客户侧附近分布边缘服务器也可以完成实时资源请求，边缘服务器可以协助解决计算能力的不足。然而，加工过程中产生的大量能源也是我们必须解决的问题。为了解决这一问题，本文提出了一种基于移动边缘计算和雾计算（EFTO）的联合任务卸载策略。实际上，首先通过获取计算任务的路线来发现处理活动的位置，这揭示了任务从起点到期望地点的所有路线细节。接下来，为了最大限度地减少卸载和处理过程中花费的时间和时间，使用基于 Takagi-Sugeno 模糊神经网络（TS FNN）的任务卸载技术 F-TORA 来实现多目标优化问题。最后，对比试验显示，与替代卸载技术相比，消耗时间减少，能源使用优化，证明了 EFTO 的有效性。