Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing.

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Vehicular fog computing (VFC) performance is hindered by challenges in obtaining vehicle collaborative network data.
• Existing models lack the ability to fully capture the social and communication dynamics within vehicular networks.

Purpose of the Study:

• To propose an optimization framework for vehicular fog computing.
• To develop a cooperative vehicular network model addressing data acquisition challenges.
• To analyze the underlying structure and dynamics of vehicular networks using hypergraph theory.

Main Methods:

• Application of hypergraph theory to model the network structure, incorporating vehicle social characteristics and communication.
• Analysis using Poisson stochastic process and mean field theory, assuming vehicles join according to a Poisson process.
• Simulation of cooperative network evolution using MATLAB.

Main Results:

• The study demonstrates that the vehicle super-degree in vehicular fog computing exhibits scale-free characteristics.
• The developed model accurately predicts vehicle dynamics and analyzes cooperation scenarios.
• Simulation results validate the effectiveness of the proposed framework and model.

Conclusions:

• The proposed model enhances the analysis of vehicle cooperation and improves vehicular fog computing performance.
• The framework's universality allows transformation into various complex network models by adjusting parameters.
• Findings offer significant reference value for research on vehicular ad-hoc networks (VANETs) and cooperative network theory.