Cluster-based route convergence method based on network state graph model

To address the challenges of time-varying node connectivity and frequent link failures in tactical communication networks under conditions of strong adversarial and high-mobility operations, which result in frequent route convergence and short effective transmission times, a cluster-based route convergence method based on network state graph model (OSPF-CSG) was proposed. Firstly, link states were obtained based on the statistical characteristics of Hello messages, and node states were characterized using aggregated link state features to construct the network state graph model. Secondly, the neighbor state machine’s state transition triggers were improved, and two new OSPF message types were developed to design an active/passive association mechanism for complete adjacency relationships. Finally, on this basis, a cluster head bootstrap algorithm, node clustering algorithm, and inter-cluster connection algorithm were proposed to achieve cluster-based routing convergence. Simulation results show that, in eight different network topologies and under different link damage conditions, OSPF-CSG achieves an average reduction of 82% in the total number of route convergences, a reduction in routing overhead by 70% on average, and an improvement in packet delivery rate by 60% on average compared to the traditional OSPF protocol’s route convergence algorithm.