A Green Cluster-based Traffic Information Acquisition Method in Vehicular Networks

The Smart Vehicle (SV) with multiple onboard sensors is one of the indispensable tools to collect dynamic traffic information in the Intelligent Transport System (ITS). In this paper, we aims at jointly relieving the cellular network load and the transmission energy consumption during the SVs' information uploading. A Green Cluster-based Traffic Information Acquisition Method named GCTIAM is proposed, where an edge clustering management architecture is designed to control the traffic information upload cycles of each SV. On this basis, We build an optimal clustering mechanism to further improve the energy utilization while relieve the cellular network load. The mechanism optimizes the cluster iteration radius and divides SVs into different competitive areas according to their movement characteristic. An adaptive multi-parameter iteration among the same competitive area will be executed to decide the data transmission mode and build optimal cluster structures by considering dynamic parameters such as link duration time, uploading data volume etc.. The simulation results show that the proposed scheme can reduce the transmission energy consumption by 81% while cut down the cellular access rate up to 70%.