applied sciences Article Intelligent Task Offloading in Fog Computing Based Vehicular Networks  
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However, to cope up with these latency requirements, C-V2X has to propose some modifications in its medium access control and physical layers.  
Vehicular communication relies on wireless technologies such as cellular communications, thus leading to intelligent transportation systems (ITS). ITS offers multiple smart services such as unmanned autonomous vehicle driving, online gaming, augmented and virtual reality. Most of these services require high computational capability and do not compromise delay. Moreover, an increase in high traffic applications along with simultaneous multiple tasks is expected from vehicles in the near future. To meet these challenges, the vehicles require high computational and battery sources. Though offloading high computational tasks and data traffic through the Internet cloud solves these challenges, computing at remote locations through centralized cloud-based Internet access may cause significant delays that are not acceptable in delay-sensitive applications of ITS.  
To mitigate these delay-sensitive issues, computing servers are deployed on each RSU location and are connected with each RSU to behave as a fog node. These fog nodes have abundant caching and computational capability to perform vehicles tasks with cache and computing capabilities [32; 33; 34; 35]. However, the fast speed with a limited coverage area provides shorter connectivity time for vehicles with RSUs and is a major constraint in task computation. Moreover, due to a large number of different varieties of tasks, a fog node cannot execute all tasks simultaneously. This causes a delay in the execution of some tasks that are not required for different applications and may badly affect the performance of the vehicular networks.  
In this work, an efficient task execution by fog node (\(ETE_{FN}\)) that gives task preference in accordance with time constraints is proposed. An algorithm along with a superframe structure is designed to fulfill these requirements. The salient features of the \(ETE_{FN}\) are given below:  
1. Screen out those offloaded tasks that are about to leave the RSU coverage area. The screen-out tasks are forwarded to a cloud server for task execution.
2. A utility function is designed for the rest of the offloaded task requests according to task preferences and the remaining time of attachment with the RSU.
3. If task requests are more than the execution capacity of the fog node, tasks are optimally scrutinized according to their utility function by applying the 0/1 knapsack algorithm. The scrutinized tasks are executed at the fog node, and the rest of the tasks are forwarded to the cloud for processing.  
The rest of the paper is organized as follows: Section 2 discusses fog computing and different research about task offloading schemes. The system model is discussed in Section 3 The proposed task execution policy is examined in Section 4. The performance of the proposed scheme is evaluated in Section 5, and Section 6 concludes the paper.