Complex Network Theoretical Analysis on Information Dissemination over Vehicular Networks

TL;DR

This paper applies complex network theory to analyze and improve information dissemination in vehicular networks, proposing models and algorithms for network optimization based on real-world GPS data.

Contribution

It introduces a novel application of complex network analysis to vehicular communication, including models, clustering, and optimization algorithms for enhanced network performance.

Findings

Characterized vehicular networks as complex networks using GPS data

Developed models for vehicle-to-infrastructure and vehicle-to-vehicle communication

Proposed algorithms for station selection and traffic optimization

Abstract

How to enhance the communication efficiency and quality on vehicular networks is one critical important issue. While with the larger and larger scale of vehicular networks in dense cities, the real-world datasets show that the vehicular networks essentially belong to the complex network model. Meanwhile, the extensive research on complex networks has shown that the complex network theory can both provide an accurate network illustration model and further make great contributions to the network design, optimization and management. In this paper, we start with analyzing characteristics of a taxi GPS dataset and then establishing the vehicular-to-infrastructure, vehicle-to-vehicle and the hybrid communication model, respectively. Moreover, we propose a clustering algorithm for station selection, a traffic allocation optimization model and an information source selection model based on the communication performances and complex network theory.