A Temporal-Information-Based Adaptive Routing Algorithm for Software Defined Vehicular Networks

In Software Defined Vehicular Networks (SDVNs), most existing studies of routing consider the vehicular network as a static graph and compute the flow table based on static information. However, a static graph could only contain partial network data. Routing computation based on the static graph could be inefficient because vehicular networks are temporal graphs. Thus, in this paper, we propose a novel routing algorithm based on the Markov model and the temporal graph. Unlike conventional routing algorithms, the proposed algorithm adopts the concept of the temporal graph where every edge has its specific temporal information. We apply the Markov model to predict the future routing of the network and adopt prediction data to get the optimal routing by running the temporal graph optimal path algorithm. A benefit of our proposal is, the proposed algorithm searched on the temporal graph of SDVNs can avoid generating additional routing overhead. Besides, based on the information of the vehicular network which is collected from the data plane, the controller can enhance the Markov model as time flows. By applying the above mechanisms, the flow table (route) could be calculated more precisely to enable efficient vehicular communication. The simulation experiments demonstrate the superiority of the proposed algorithm over its counterparts in high-density vehicular networks.