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

Abstract

Most recent studies on Software Defined Vehicular Networking (SDVN) consider the vehicular network as a static graph and compute the flow table based on the static information. However, a static graph could only contain partial network data. Computation based on the static graph could be inefficient by missing the important temporal information since vehicular networks are temporal graphs in fact. Thus, in this paper, we propose a novel routing algorithm based on the Markov model and temporal graph. Unlike conventional routing algorithms, the proposed algorithm adopts the concept of a temporal graph where every edge has its specific temporal information. We apply the Markov model to predict the future routing of the network and use prediction data to get the optimal routing. This is achieved by running the temporal graph optimal path algorithm. The algorithm runs on the temporal graph constructed for SDVNs without generating additional routing overhead. In addition, 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.

A Network-Centric Analysis for the Internet of Vehicles and Simulation Tools

“…Traffic congestion is a common problem due to the dynamic environment, rush hours, and deadlocks [61], [75], [76]. VANETs have the potential to solve this problem using a routing technique to direct vehicles in a way that reduces congestion in traffic [61], [63], [71], [75], [76]. Xia et al propose a greedy traffic light and queue aware routing protocol (GTLQR) using network theory to reduce traffic congestion [75].…”

“…Perronnet et al also focus on routing at intersections, but also includes road reservation using a graph theory method to prevent deadlocks in traffic [76]. Other papers focus on an opportunistic routing protocol like the Dempster-Shafer evidence theory based method proposed by Kashani et al or propose temporal graph methods to solve the routing problem due to the high mobility of a VANET, such as in Zhao et al [61], [63]. Khan et al propose a different approach using oriented evolving graph and clustering to improve reliable routing requests in the network [71].…”

A Novel Prediction-Based Temporal Graph Routing Algorithm for Software-Defined Vehicular Networks

“…Specific to routing, the temporal information contains the starting time and duration of data transmission. This is critical for capturing the temporal-related properties of the vehicular network, as routing is time-dependent [13]. Adopting the temporal graph instead of the static graph to represent the entire VANET can be positive to capturing network characteristics and attributes.…”