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An Iterative k-means++ and Ant Colony Clustering Scheme for Vehicular Networks

In this paper, a clustering scheme for vehicle user equipment (UEs) based on an iterative k-means++ and ant- colony clustering scheme for a vehicular network is investigated. In this context, we exploit traffic demand generated from the Simulation of Urban Mobility (SUMO). Furthermore, we extend the clustering problem and formulate it as an optimization problem which is solved using ant colony optimization (ACO). We compare the performance of our proposed clustering schemes in terms of cluster stability to existing clustering approaches in the literature. Results obtained demonstrate the effectiveness of our proposed scheme in maintaining a stable cluster.