Vehicular radio connectivity in urban environment

Abstract

-In this paper we present a study of mobile connectivity for vehicular network in urban environment. The considered area is a real map divided into equally sized cells, each characterized by a set of information including the type of structure in the cell, cell altitude and cell attraction for vehicles. These characteristics are taken into account in two models: vehicular mobility model and electromagnetic wave propagation model. We study the impact of nodes density and obstacles present in the environment on network connectivity based on radio links. Graphs representing radio connections between vehicles are periodically generated. We examine four metrics that are nodes degree, connections duration, dominated nodes rate and cluster formation. I. INTRODUCTIONIn recent years, several researches have been conducted on inter-vehicle communication systems. According to that one infrastructure is used or not, two communication types may be distinguished: Vehicle-to-Infrastructure (V2I) communications where vehicles are connected to stations located on roadsides for information acquisition, information transmission and internet access; and Vehicle-to-Vehicle (V2V) communications, in which vehicles exchange information without relying on any given infrastructure, they collaborate to form at each time a dynamic distributed system called VANET (Vehicular Ad hoc NETwork).In ad hoc networks, the possibility of establishing communications and the quality of exchanged messages depend on the radio connectivity between nodes. One factor that affects considerably this connectivity is nodes motion, particularly when nodes move at high speeds as it is the case for vehicular networks. Several models have been proposed to simulate nodes mobility in MANET [1]. These models ignore the key characteristics of vehicular traffic, mainly the constraints on nodes movement and the interactions between neighboring nodes; they are thus not suitable for VANET. Aiming to reflect as closely as possible the real behavior of vehicular traffic, new models have been proposed [2]. Depending on the level of detail considered, these models may be classified into two categories: macroscopic models that take into account parameters such as road topology, roads attributes, traffic signs and traffic characteristics (density, flow etc.) and microscopic models that focus on the individual behavior of each vehicle and try to model features such as speed, acceleration, braking and interactions between vehicles.