Next-generation VANET routing: An AI-based time-evolving graph framework

Ali Hamlili
Smart Systems’ Laboratory, Department of Computer Networks, Ecole Nationale Supérieure d’Informatique et d’Analyse des Systèmes (ENSIAS), Mohammed V University in Rabat, Agdal, Rabat B.P. 713, Morocco

Article ID: 4261

Keywords: vehicular ad hoc networks, proactive routing, random geometric graphs, Markov chains, stochastic geometry, optimized link state routing protocol (OLSR), machine learning, intelligent transportation systems

Abstract

Vehicular Ad Hoc Networks (VANETs) exhibit highly dynamic topologies due to rapid vehicle mobility and frequent fluctuations in wireless connectivity. Consequently, efficient routing remains a significant challenge. Proactive routing protocols rely on periodic route updates. This creates a trade-off between routing accuracy and control overhead. Higher update frequencies improve accuracy and timeliness. However, they also generate substantial control overhead. Conversely, lower update frequencies reduce overhead at the cost of outdated topology information and increased routing errors. Achieving an optimal balance between these competing objectives is particularly difficult in non-stationary network environments, a well-documented challenge in VANETs. This paper introduces a unified framework combining stochastic modeling, graph analysis, and machine-learning optimization. Vehicle positions follow a Poisson process, while link dynamics are modeled with a two-state Markov chain. We derive closed‑form expressions that quantify network connectivity, expected link lifetime, and overall routing stability in dynamic vehicular environments. An artificial intelligence (AI)-driven adaptive controller is integrated into the proposed framework to dynamically adjust routing parameters in response to variations in VANET topology and non-stationary network conditions. Simulation results demonstrate a 35–50% reduction in routing overhead while preserving high packet delivery performance.

Published

2025-12-23

How to Cite

Hamlili, A. (2025). Next-generation VANET routing: An AI-based time-evolving graph framework. Computing and Artificial Intelligence, 3(4). https://doi.org/10.59400/cai4261

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Vol. 3 No. 4 (2025)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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