A Novel Clustering Scheme for Heterogeneous Vehicular Networks

Abstract

Effective clustering is vital to mitigate routing scalability and reliability issues in heterogeneous vehicular networks. In this paper, we propose an adaptive clustering scheme to maximize the cluster stability in vehicular networks. The scheme uses the predicted driving behavior of vehicles over a time horizon to maximize the clusters’ lifetime. To this end, we first define the stability degree of vehicles by exploiting the unique aspects of vehicular environments. We then formulate the clustering problem as an optimization problem, which is used within a rolling horizon framework in the cluster formation process. Our scheme is based on a heterogeneous vehicular network architecture, which allows the coexistence of dedicated short-range communication and cellular network for vehicular communications. The simulation results demonstrate that our scheme significantly outperforms alternative clustering algorithms in terms of the overall clusters’ lifetime under different traffic conditions. Our scheme can also be utilized to provide a well-grounded comprehension of the optimally of the existing and future distributed clustering algorithms.