Data Dissemination With Trajectory Privacy Protection for 6G-Oriented Vehicular Networks

Abstract

Data dissemination of vehicles is critical for vehicular networks because of the extensive impact of traffic information. The existing works for data dissemination in vehicular networks mainly use data scheduling algorithms to transmit data among vehicles. However, it is challenging to meet the ultrareliable and low-latency requirements of data transmission among vehicular networks due to the intrinsic movement characteristic of vehicles. To promote the data dissemination of vehicular networks, a data dissemination algorithm with trajectory privacy protection is proposed in this article, which leverages the cooperative distribution of key tasks and distance deviation. Specifically, the key tasks are disseminated to vehicles first, and the trajectory privacy protection scheme is further developed to guarantee the security of data transmission by the exploration of distance deviation and pseudonym entropy. Simulation results indicate that the proposed adaptive data dissemination algorithm is approximately 60%, 69%, and 50% better than the state-of-the-art scheduling algorithms in terms of connectivity degree, transmission delay, and average distance deviation for the vehicular network.