Processing capability and QoE driven optimized computation offloading scheme in vehicular fog based F-RAN

The Fog Computing was proposed to extend the computing task to the network edge in lots of Internet of Things (IoT) scenario, such as Internet of Vehicle (IoV). However, the unbalanced data processing requirement caused by the uneven distribution of vehicles in time and space limits the service capability of IoV. To enhance the flexibility and data processing capability, we propose a hybrid fog architecture which composed by fog computing radio access network (F-RAN) and Vehicular Fog Computing (VFC), which is called VF-based F-RAN. In addition, we propose a heuristic algorithm enhanced by deep learning to optimize the computation offloading in this hybrid architecture. The simulation result reveals that the proposed hybrid fog architecture with the heuristic algorithm can effectively improve the data processing efficiency and balance the Quality of Experience (QoE).