Efficient Hybrid Data Dissemination for Edge-Assisted Automated Driving

Abstract

Automatic driving services have large volume, location-aware, and time-changing contents, which are suitable to be cached by the edge. However, the traffic on the edge will be extremely high especially in the area with high vehicle density, if the vehicles directly access the contents from the edge as they demand. A hybrid data dissemination model with both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) disseminations has been proposed to reduce the traffic on the edge, in which the edge (infrastructure) selectively injects data to the vehicles and leverages the vehicle network to disseminate the data. In this article, we study the hybrid data dissemination problem, i.e., to optimally determine when and which vehicle the data are injected into, and whether the vehicle acquires the demanded data directly from the edge or from nearby neighbors, with the aim of minimizing the traffic cost on the edge and meeting the deadlines of acquiring the data. Existing approach prioritizes the selection of V2I disseminations at first and then explores the V2V disseminations which have no conflict with the V2I disseminations. This approach cannot fully take advantage of V2V to reduce the traffic cost on the edge. We propose a new data dissemination algorithm, named the offline algorithm for hybrid data dissemination (OFDD), which seeks the most beneficial V2V broadcasts with priority, and then choose feasible V2I disseminations. Based on OFDD, we develop both the snapshot and prediction-based online algorithms. We follow with extensive simulations to validate the proposed algorithms. The results show that our algorithms significantly outperform the state-of-the-art approaches in terms of data acquisition rate and traffic cost.