Cluster-Based Vehicular Data Collection for Efficient LTE Machine-Type Communication

Machine-Type Communication (MTC) poses an ongoing research topic in the development of cellular communication systems. In this context, the efficient collection of extended Floating Car Data (xFCD) via Long Term Evolution (LTE) is a major challenge. In this paper, we present cluster-based xFCD collection schemes in order to form clusters with a long lifetime. As a result, the proposed clustering algorithms reduce the occurring cellular communication traffic. For the performance evaluation of the presented algorithm, a novel system model is used. By means of the system model, the user mobility can be modeled realistically and a precise quantification of the utilization of the LTE network for xFCD transmission is possible. The results show that the LTE network utilization can be significantly reduced by the proposed clustering algorithms.