基于双重梯度的车辆传感器网络定向扩散梯度场

摘要

In order to speed up forming Directed Diffusion (DD) gradient field and improve the connectivity of logic networks, a Directed Diffusion Gradient Field based on Double Gradient (DDGF-DG) for Vehicular Sensor Networks (VSN) was proposed. With the help of the estimated gradient value of roadside-node, the large scale vehicular sensor network was divided into a certain number of sub-reigns taking roadside-nodes as the local cores, the local directed diffusion gradient field around every roadside-node was set up in distributed way, and at last, the global directed diffusion gradient field composed of the local directed diffusion gradient fields was formed with the help of double gradient value, so the autonomous management in every sub-reign was realized. Theoretical analysis and simulation results show that DDGF-DG and its dynamic adjustment would reduce the time consumption and improve the real-time connectivity.%为了提高定向扩散梯度场建立的快速性和逻辑网络的实时连通性,提出一种基于双重梯度的车辆传感器网络(VSN)定向扩散梯度场(DDGF-DG).通过网络中各路边节点估算的梯度值将网络划分为若干以路边节点为局部核心的区域,各局部核心分布式启动局部定向扩散梯度场的建立,利用双重梯度值将各局部定向扩散梯度场连接成全局定向扩散梯度场,实现巨大规模车辆传感器网络的分区治理.理论分析和仿真结果表明,基于双重梯度的定向扩散梯度场及其动态调整有利于减小梯度场建立与维护的时间开销和提高网络的实时连通性.