Enhancing Task Efficiency in Vehicular Fog Computing: Leveraging Mobility Prediction and Min-Max Optimization for Reduced Latency

Vehicular fog computing (VFC) has emerged as a groundbreaking architecture within the Internet of Vehicles (IoV), offering the immense potential to significantly reduce computation time for diverse vehicular applications. However, the inherent high mobility of vehicles poses a formidable challenge in coordinating VFC operations within the rapidly changing topology of vehicular networks. In this research endeavor, we delve into the intricacies of joint task assignment and resource allocation optimization, considering the profound impact of mobility in vehicular fog computing. Our objective is to minimize overall task latency by formulating the problem as a MinMax optimization challenge. To tackle this non-convex problem effectively, we decompose it into two sub-problems: one-to-one matching and bandwidth resource allocation. Moreover, capitalizing on the relatively stable moving patterns exhibited by vehicles over short periods, we introduce a mobility prediction-based scheme to enhance solution quality. Through comprehensive simulations, we demonstrate the effectiveness of our proposed mobility prediction-based scheme in reducing overall task completion latency in VFC, thereby unlocking the full potential of vehicular fog computing in the IoV realm.