Modeling And Performance Analysis Of Vehicular Network With Stochastic Geometry Theory

Significant advances of wireless communications and the pervasive use of mobile electronics have turned vehicular networks from a futuristic promise to an attainable technology to meet the imminent demands for reduced accidents and improved road safety and efficiency.The uncertainty in the locations of vehicles on streets induced by vehicles passing and queueing make the spatial modeling of vehicles a difficult task.In vehicular network,the deployment efficiency of RSU has drawn the attention of academics and industry because of the high cost of the RSU deployment.Thus,this paper focus on the spatial modeling of vehicle location based on stochastic geometry,the deployment of RSU and the fleet management in the intelligent transportation scenario.The specific research contents are as follows:The performance of the V2V is mainly affected by the communication links,which in turn are governed by the topology characteristics of VANETs.To analyze the performance of V2V communication for VANETs,accurate spa-tial modeling is of great importance.This paper concentrates on spatial point process modeling for random vehicle locations in two cities.We showed that the LGCP model accurately characterizes diverse spatial point patterns of ran-dom vehicle location.This is verified by the minimum contrast method.Then we study the node degree as an important metric for the communication perfor-mance of the networks.Roadside units are the key components to collect and disseminate infor-mation from or to vehicles in V2I communication.The efficiency of RSU de-ployment is a significant problem in V2I communication.This paper analyzes the connectivity of vehicular network based on the spatial model of vehicles.Furthermore,the paper minimizes the number of the deployed RSUs while guar-antee the connectivity of the RSUs and vehicles.The deployment problem are divided into two sub-problems.The fist step is to select some locations in the map as the potential locations to deploy RSU with the depth first traversal algo-rithm and K-means algorithm.The second step is to select a part of the potential locations to deploy RSU.The vehicle connectivity to RSU is guaranteed and the deployment overhead is minimized.Based on the V2V communication and V2I communication technology,the traditional transportation is evolving to intelligent transportation system.AVs may dramatically change future intelligent transportation in smart cities.To provide best user experience of such service,two primary factors including waiting time and supply of AVs are taken into consideration.For accuracy and convenient processing,NC is first used in the queueing problem in our paper.To manage the supply and demand of the service in each geographical area,bipartite graph matching is adopted to accomplish optimal resource allocation.It is further shown that the optimization of operation of autonomous vehicle fleet can be successfully achieved,to outperform what human-driving vehicles can possibly do.