面向车联网大规模网络动态演化过程的通达性机理研究

项目名称： 面向车联网大规模网络动态演化过程的通达性机理研究

项目编号： No.61472284

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 程久军

作者单位： 同济大学

项目金额： 81万元

中文摘要： 车联网大规模网络客观存在高维数据的复杂关系、互连互通耦合度低、实时性和稳定性难以保证等缺陷，即通达性问题，成为阻碍车联网大规模网络应用和产业发展的瓶颈。现有研究方法主要通过协议转换和路由算法实现应用集成，缺乏开放网络环境下实现网络互连互通并保持网络稳定的理论基础和方法，本课题突破现有小尺度闭环系统的研究思路，从车联网大规模网络环境出发，提出数据解析→网络连通→状态稳定的理论体系。具体包括：首次引入深度学习解析车联网高维数据的复杂关系，在此基础上，提出解决互连互通问题的网络进程演化理论模型；借助李雅普诺夫稳定性理论，给出网络稳定性条件，结合种群竞争，提出网络稳定状态转换方法；给出车联网大规模网络通达性机理的优化机制。解决上述科学问题，建立一套能支持车联网大规模网络通达性的理论体系和方法，为网络层提供理论支撑，为应用层提供实时数据保障，同时也为未来车联网服务智能化发展奠定理论和应用基础。

中文关键词： 车联网；网络进程演化理论模型；动态演化机制；深度学习；种群竞争

英文摘要： There objectively exist some defects on the complex relationship of high-dimensional data, low coupling on interconnection, real-time and stability can't be difficultly guaranteed in the large-scale network of Vehicle to Vehicle(V2V), that is accessibility problem, it has become an obstacle to large-scale applications and industrial development. The current researches mainly realized application integration by protocol conversion and routing algorithm, it lacks of theoretical principle and methods of network interconnection and keeping network be stability in an open-loop conditions. This project breaks through the current research idea in the small-scaled closed-loop system, and considering the large-scale network of V2V, we proposed the theoretical system on data analysis→network interconnection→state stability. The research content are as following, we introduced deep learning to analyze the complex relationship of high-dimensional data of V2V for the first time, on this basis, the theory model of network process evolution which solves network interconnection problems; With the help of Lyapunov stability theory, network stability conditions will be given, and combined with population competition, network steady state conversion methods are proposed; The optimization mechanism on accessibility mechanism of a large-scale network of V2V are given. After solved the above scientific problems, the theoretical system and methods which can support accessibility in the large-scale network of V2V will be established, and it can provide the theoretical support for network layer, and can provide real-time data for application layer, and can also provide the theoretical and applied basis for the development of intelligent services on V2V for the future.

英文关键词： Vehicle to Vehicle;Network Process Evolutionary Theoreticl Model;Dynamic Evolutionary Mechansim;Deep Learning;Population Competition