Multi-Layer Task Offloading Scheme in Fog Computing-Based VANETs With Optimized Completion Delay

Due to the characteristics of high mobility, low latency and short connection, task offloading in vehicular ad-hoc networks (VANETs) is facing many issues. In this paper, we propose a multi-layer task offloading scheme in fog computing-based VANETs, whose task completion delay can be optimized to an approximate minimum by a dynamical task offloading process. In our proposed scheme, a vehicle movement model is constructed to predict the position of each vehicle in a short time. Then, the link reliability evaluation metrics are constructed based on the vehicular willingness, the degree of vehicle connection and the expected count of task transmission. Furthermore, an evaluation model of processing delay and resource quantity is established to further estimate the selection of offloaded vehicles according to node degree, resource degree, computation capacity and transmission capacity. Finally, the multi-layer task offloading scheme is proposed, where we build an optimization model to minimize the task transmission and computation (completion) delays. Related experiments show our multi-layer task offloading scheme is efficient by optimizing task completion delay in fog computing-based VANETs.