The VANET (Vehicle Ad Hoc Network) is gathering attention for autonomous vehicles and the MANET (Mobile Ad Hoc Network) is attracting interest as well. Therefore, efforts have been made to overcome the challenges of the VANET in which the topology changes in real time and instability exists due to the difference in speed and physical phase. Particularly in the IoT era, the total amount of network nodes in addition to vehicle nodes is expected to increase dramatically. Therefore, a clustering algorithm for a mesh network capable of autonomous configuration is suitable for reducing the load of the central control device and data redundancy on the network, which is expected to increase as the IoT era progresses. However, since clustering algorithms based on the existing research have been developed for the current traffic situation, inefficiency is inevitable in the future autonomous navigation period in which traveling path prediction can be accurately performed. Therefore, this paper discusses a clustering algorithm and a data propagation algorithm between clusters using path information. The main content of this paper is as follows. First, we propose a clustering algorithm using path information and considering the existing research results. In the autonomous navigation period, if the path is predictable, the probability that the nodes in the same cluster are in the same block for a longer time than the conventional one can converge to 100%. Therefore, the survival time of the cluster can be dramatically improved. Second, we developed a data propagation algorithm that can increase the information propagation rate of the entire network using path information. The cluster temporarily stores the data to be disseminated and then disseminates it when it encounters another cluster of neighbors. Therefore, data can be disseminated even for noncontiguous clusters. To summarize, this paper proposes clustering-based data dissemination algorithms and protocols using vehicle pathways for autonomous navigation and compares them with clustering-based data dissemination algorithms using existing directions.
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