Recursion-based Analysis for Information Propagation in Vehicular Ad Hoc Networks

TL;DR

This paper introduces a recursive analytical framework for modeling information propagation in VANETs, accommodating general vehicle spacing distributions and providing insights into propagation metrics under various conditions.

Contribution

The work develops a novel recursive modeling approach for VANETs that handles arbitrary i.i.d. vehicle spacing distributions, extending beyond specific probability models.

Findings

Derived analytical expressions for propagation distance metrics.

Validated results through extensive simulations.

Demonstrated the method's efficiency with channel fading considerations.

Abstract

Effective inter-vehicle communication is fundamental to a decentralized traffic information system based on Vehicular Ad Hoc Networks (VANETs). To reflect the uncertainty of the information propagation, most of the existing work was conducted by assuming the inter-vehicle distance follows some specific probability models, e.g., the lognormal or exponential distribution, while reducing the analysis complexity. Aimed at providing more generic results, a recursive modeling framework is proposed for VANETs in this paper when the vehicle spacing can be captured by a general i.i.d. distribution. With the framework, the analytical expressions for a series of commonly discussed metrics are derived respectively, including the mean, variance, probability distribution of the propagation distance, and expectation for the number of vehicles included in a propagation process, when the transmission failures are mainly caused by MAC contentions. Moreover, a discussion is also made for demonstrating the efficiency of the recursive analysis method when the impact of channel fading is also considered. All the analytical results are verified by extensive simulations. We believe that this work is able to potentially reveal a more insightful understanding of information propagation in VANETs by allowing to evaluate the effect of any vehicle headway distributions.