LAUSR

The current data distribution method of vehicular network cannot satisfy the strict spatiotemporal constraints on the transmission of massive service data. Neither can the 5th generation mobile network (5G) meet the massive data demand of vehicular network services. To solve the problems, this paper designs an edge-assisted service data distribution method for vehicular network services. Specifically, the service data distribution was predicted by time series analysis through edge computing, based on the storage capacity of base stations. Then, a spatiotemporal constrained data sharing algorithm was proposed, which sets up a distribution tree to evaluate the importance of each vehicle in data transmission, and heuristically chooses the most important vehicles as seeds to speed up the vehicle-to-vehicle data sharing. Finally, the simulation experiment verifies that our method can greatly reduce the load of 5G network without breaking the spatiotemporal constraints of data transmission in vehicular network.