A C-V2X Compatible Massive Data Download Scheme Based on Heterogeneous Vehicular Network
Xiuwen Yin, Jianqi Liu, Xiaochun Cheng, Xiaoming Xiong
Abstract
Due to the high mobility and limited signal bandwidth, downloading massive data to gathered dense vehicles in traditional IoVs is still rather restricted. Introducing cellular communications into vehicular networks is the intuitive solution for this issue. However, the cellular-communication-based vehicular network has several unavoidable shortcomings such as occupying cellular resources and generating communication charges. To address this issue, we combine heterogeneous network, edge caching and collaborative distribution techniques for downloading massive data in IoVs. Considering the easy deployment and popularity of C-V2X systems in engineering, the proposed scheme is compatible with C-V2X in deployment. Specifically, a comprehensive scheme for intensively downloading massive data to dense vehicles is proposed based on a heterogeneous network, in which the data distribution mode, data scheduling within the cache node, and the matched collaborative distribution algorithms are presented. A new data distribution mode without request-response delay is proposed for distributing data to multiple vehicles with a negligible interval between distributions. The matched algorithms for efficiently realizing the load balance among collaborative vehicles are designed. The simulation results show our scheme can effectively download massive data to dense target vehicles without dependence on cellular communication resources.