Towards Governance of Localized VANET: An Adjustable Degree Distribution Model

TL;DR

This work tackles the governance challenge of localized VANET topologies by introducing a schedulable degree distribution model that blends uniform and preferential attachment via a tunable parameter $p$. Using mean-field analysis, it derives how the degree distribution transitions continuously between exponential (robust, attack-resistant) and power-law (low-latency, hub-enabled) regimes, providing explicit forms for each regime. Simulations on a real road network validate the theory, showing how increasing $p$ reduces degree heterogeneity and improves resistance to targeted attacks at the cost of longer transmission paths. The proposed framework offers a practical, adaptable paradigm for dynamic VANET topology management, with future work pointing toward autonomous, DRL-driven self-scheduling.

Abstract

Vehicular Ad-hoc Networks (VANETs) serve as a critical enabler for intelligent transportation systems. However, their practical deployment faces a core governance dilemma: the network topology requires a dynamic trade-off between robustness against targeted attacks and ensuring low-latency information transmission. Most existing models generate fixed degree distributions, lacking the ability to adapt autonomously to the demands of diverse traffic scenarios. To address this challenge, this paper innovatively proposes a schedulable degree distribution model for localized VANETs. The core of this model lies in introducing a hybrid connection mechanism. When establishing connections, newly joining nodes do not follow a single rule but instead collaboratively perform random attachment and preferential attachment. Through theoretical derivation and simulation validation, this study demonstrates that by adjusting the cooperative weighting between these two mechanisms, the overall network degree distribution can achieve a continuous and controllable transition between a uniform distribution and a power-law distribution. The former effectively disperses attack risks and enhances robustness, while the latter facilitates the formation of hub nodes, shortening transmission paths to reduce latency. Experimental results based on the real-world road network of Beijing indicate that this model can precisely regulate node connection heterogeneity, attack resistance, and average transmission path length through the reshaping of the underlying topology. This provides a forward-looking and practical governance paradigm for constructing next-generation VANETs capable of dynamically adapting to complex environments.

Figures (8)

• Figure 1: Process diagram for model evolution steps.
• Figure 2: Net of an area to the east of the Forbidden City in Beijing.
• Figure 3: Topological links and corresponding degree distributions under different $p$, with upper, middle, and lower subfigures corresponding to $p = 0.0$, $0.5$, and $1.0$, respectively.
• Figure 4: Variance of all node degrees.
• Figure 5: Variance of RSU node degrees.