Connectivity-based Fog Structure Management for Software-defined Vehicular Networks

Vehicle Fog computing combines intelligent and connected vehicles to form a mobile cloud. Several works have modelled link stability for data delivery in light of solving the issues originating from unstable vehicle connectivity. However, results have shown that some mobility patterns potentially misguide the uncertainty-based estimation process. We thus propose a region-based connectivity ranking strategy. A fog management approach dynamically defines and supervises regions delimited by vehicles; such regions are mapped over an urban centre. In addition, the model develops a software-defined vehicular network (SDVN) controller to select data from the vehicular heterogeneous network environment through V2X and C-V2X. Our model admits four parameters to describe vehicular connectivity, which evaluates vehicles’ potential for communication and performs dynamic vehicular clustering. The 5G and DSRC heterogeneous networks support a more precise connectivity model for vehicular classification. Simulated analyses allow observing vehicular mobility and connectivity data in real-time scenarios where the management efficiency of vehicular fog regions is assessed in SDVN context.