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The clustering method in the vehicular ad-hoc network provides an opportunity for a cluster head to improve the network connections, but it still remains a problem in the border cluster. The vehicles, in view of their position in the slices of two clusters, receive a weak signal from the cluster head. The intersection area causes a throughput decrease for cluster members. To provide the network connection in the border cluster, we, in turn, proposed an adaptive border node clustering by utilizing the combination of K-Medoids algorithm, modified Genetic Algorithm and modified Tabu Search. We ameliorated the recent model in Enhanced Model of Weighted K-Medoids Clustering Algorithm by adding the fusion process to the best gene pieces into one individual temporary. The effects of this fusion were on the production of a temporary cluster that can make the main cluster environment more stable. This temporary cluster was adapted to the position of the two closest main cluster heads. Our model was found able to increase throughput and to keep the stability of cluster members at any velocities. The increase in throughput represents an improvement parameter in the network quality of service (QoS). We achieved the overall throughput at 93.97% (throughput vs cluster member) compared with the previous methods and the cluster member stable at around 25 vehicles (cluster member vs transmission range) in varied conditions.

ABNC: Adaptive Border Node Clustering Using Genes Fusion Based on Genetic Algorithm to Support the Stability of Cluster in VANET