文献类型：期刊文献

英文题名：Blockchain-Based Dangerous Driving Map Data Cognitive Model in 5G-V2X for Smart City Security

作者：Chen, Kai[1]; Xu, Cheng[1]; Liu, Hongzhe[1,2]; Wang, Pengfei[3]; Chen, Ziyi[1,3]

第一作者：Chen, Kai

机构：[1] Beijing Key Laboratory of Information Service Engineering, College of Robotics, Beijing Union University, Beijing, China; [2] Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China; [3] Communication and Information Center, Ministry of Emergency Management of the People's Republic of China, Beijing, China

第一机构：北京联合大学北京市信息服务工程重点实验室|北京联合大学机器人学院

通讯机构：[1]Beijing Key Laboratory of Information Service Engineering, College of Robotics, Beijing Union University, Beijing, China|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;[1141739]北京联合大学机器人学院;

年份：2022

卷号：2022

外文期刊名：Security and Communication Networks

收录：EI(收录号：20221812063883)

语种：英文

外文关键词：5G mobile communication systems - Blockchain - Maps - Vehicles - Smart city - Network security - Vehicle to vehicle communications - Intelligent vehicle highway systems - Vehicle to Everything

摘要：The development of 5G network communication has brought technological innovation to smart city communication, making the realization of V2X (vehicle to everything) technology possible. Vehicles wirelessly communicate with other vehicles, sensors, pedestrians, and roadside units, raising data security issues while driving. In order to ensure driving safety, the risk map cognitive model is established with the help of blockchain technology. In this model, the key map data and personal privacy information are encrypted and uploaded to form a blockchain, and the smart contract technology is used for automatic script processing. Then, according to different risk scenarios, cognitive learning is carried out for different risk levels, the cognitive results and corresponding operations are fed back to the intelligent vehicle, and these operations ensure the safe operation of the vehicle according to the intelligent vehicle. Finally, the feasibility of the model was verified by comparing different dangerous scenarios. The experimental results show that this risk cognition model can cognize the data of the intelligent vehicle according to different danger scenarios, and the model can transmit acceleration, deceleration, braking, and other behaviors to the intelligent vehicle to ensure smart city driving safety. ? 2022 Kai Chen et al.