Recent advancement in communication among smart devices, vehicular fog computing introduces new dimensions for delay-sensitive applications. The traditional computing paradigm to install edge locations is no longer viable due to incurred latency while decision making, especially in delay-sensitive applications. In this paper, we propose a vehicle-to-vehicle task offloading framework that allows vehicles to utilize computation resources available at nearby vehicles. The objective is to bring fog computing near vehicles to achieve computational efficiency and improve quality of service. To overcome mobility issues, we implement Context-aware opportunistic offloading schemes based on speed, direction, and locality of vehicles. The schemes are compared to random offloading mechanism in terms of efficiency, task completion, failure rate, workload distribution, and waiting time. The results demonstrate a significant reduction in failure rate up to 10% with more tasks completed on vehicles within direct communication range.

智能设备之间的通信的最新进展是，车辆雾计算为对延迟敏感的应用程序引入了新的维度。传统的安装边缘位置的计算范例不再可行，因为在决策时会产生延迟，尤其是在对延迟敏感的应用中。在本文中，我们提出了一种车辆到车辆的任务卸载框架，该框架允许车辆利用附近车辆可用的计算资源。目的是使雾计算靠近车辆，以实现计算效率并提高服务质量。为了克服移动性问题，我们基于车辆的速度，方向和位置实施了上下文感知的机会性卸载方案。将该方案与效率，任务完成，失败率，工作量分配和等待时间。结果表明，在直接通信范围内的车辆上完成了更多任务后，故障率显着降低了10％。