基于云边融合的车联网架构大数据安全集成算法

IoV architecture′s big data security integration algorithm based on cloud-edge fusion

为实现对过往车辆信息数据的深度挖掘,缓解特定时间内的交通拥堵压力,设计基于云边融合的车联网架构大数据安全集成算法。以车载云网格作为底层数据融合结构,在连接集中式微云决策引擎的同时,实现对车联网信息的发布与处理,搭建车联网架构下的云边融合环境。在此基础上,计算车辆信息的相似度数值,按照集成选择机制的应用需求,解决车联网数据的交叉和变异问题,完成基于云边融合的车联网架构大数据安全集成算法设计。对比实验结果表明,应用大数据安全集成算法后,车辆信息数据由一个集成节点迁移至另一个集成节点所需的等待时间降低至0.15 ms,而单位时间内的堆积总量却始终不超过7.5×10^(9) TB,不仅能够实现对过往车辆数据的深度挖掘,也可有效缓解由信息滞留造成的交通拥堵压力。

A big data security integration algorithm based on cloud-edge fusion for Internet of Vehicles(IoV)architecture is designed to realize the in-depth mining of passing vehicle information data and alleviate the traffic congestion in a specific time.With the vehicle-mounted cloud grid as the underlying data fusion structure,the centralized micro cloud decision engine is connected to realize the release and processing of the IoV information,and build the cloud-edge fusion environment under the framework of IoV.On this basis,the similarity value of vehicle information is calculated.Furthermore,according to the application requirements of the integrated selection mechanism,the data intersection and variation of the IoV is figured out to complete the design of the big data security integration algorithm based on cloud-edge fusion.The contrast experimental results show that,after application of the big data security integration algorithm,the waiting time for vehicle information data to migrate from one integration node to another one is reduced to 0.15 ms,while the total amount of accumulation per unit time is always less than 7.5×10^(9) TB.It can not only realize the in-depth mining of the passing vehicle data,but also effectively alleviate the traffic congestion caused by information retention.