Security of 5G-V2X: Technologies, Standardization and Research Directions

Cellular-Vehicle to Everything (C-V2X) aims at resolving issues pertaining to the traditional usability of Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) networking. Specifically, C-V2X lowers the number of entities involved in vehicular communications and allows the inclusion of cellular-security solutions to be applied to V2X. For this, the evolvement of LTE-V2X is revolutionary, but it fails to handle the demands of high throughput, ultra-high reliability, and ultra-low latency alongside its security mechanisms. To counter this, 5G-V2X is considered as an integral solution, which not only resolves the issues related to LTE-V2X but also provides a function-based network setup. Several reports have been given for the security of 5G, but none of them primarily focuses on the security of 5G-V2X. This article provides a detailed overview of 5G-V2X with a security-based comparison to LTE-V2X. A novel Security Reflex Function (SRF)-based architecture is proposed and several research challenges are presented related to the security of 5G-V2X. Furthermore, the article lays out requirements of Ultra-Dense and Ultra-Secure (UD-US) transmissions necessary for 5G-V2X.Comment: 9 pages, 6 figures, Preprin

Big data driven vehicle battery management method: A novel cyber-physical system perspective

The establishment of an accurate battery model is of great significance to improve the reliability of electric vehicles (EVs). However, the battery is a complex electrochemical system with hardly observable and simulatable internal chemical reactions, and it is challenging to estimate the state of battery accurately. This paper proposes a novel flexible and reliable battery management method based on the battery big data platform and Cyber-Physical System (CPS) technology. First of all, to integrate the battery big data resources in the cloud, a Cyber-physical battery management framework is defined and served as the basic data platform for battery modeling issues. And to improve the quality of the collected battery data in the database, this work reports the first attempt to develop an adaptive data cleaning method for the cloud battery management issue. Furthermore, a deep learning algorithm-based feature extraction model, as well as a feature-oriented battery modeling method, is developed to mitigate the under-fitting problem and improve the accuracy of the cloud-based battery model. The actual operation data of electric buses is used to validate the proposed methodologies. The maximum data restoring error can be limited within 1.3% in the experiments, which indicates that the proposed data cleaning method is able to improve the cloud battery data quality effectively. Meanwhile, the maximum SoC estimation error in the proposed feature-oriented battery modeling method is within 2.47%, which highlights the effectiveness of the proposed method.</p