Towards Governance of Localized VANET: An Adjustable Degree Distribution Model

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.

💡 Research Summary

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The paper tackles a fundamental governance challenge in vehicular ad‑hoc networks (VANETs): how to reconcile the need for robustness against targeted attacks with the demand for low‑latency data dissemination. Existing localized VANET models inherit a single attachment rule (either pure random or pure preferential attachment) and consequently produce a fixed degree distribution that cannot be tuned to the prevailing traffic scenario. To overcome this limitation, the authors propose a schedulable degree distribution model that blends random attachment and preferential attachment through a controllable weight parameter p.

When a new vehicle (node) joins the network, it first selects a local world of M neighboring nodes within its communication range. From this set it establishes m connections, but the choice of each neighbor follows the hybrid probability

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