Machine Learning-Driven Routing in the Internet of Vehicles: A Critical Survey of Learning Paradigms, Gaps, and Open Problems
DOI:
https://doi.org/10.46610/JAHNMC.2026.v03i03.002Keywords:
Deep Q-network, Federated learning, Graph neural network, Internet of Vehicles, Machine learning, Reinforcement learning, Routing protocol, VANETAbstract
The Internet of Vehicles (IoV) has evolved rapidly from vehicular ad hoc networking into a large-scale, heterogeneous system integrating vehicles, roadside infrastructure, edge servers, and cloud platforms in support of intelligent transportation. A persistent bottleneck in this evolution is routing: selecting reliable multi-hop paths across a topology that reconfigures on the order of seconds due to high vehicle mobility, variable traffic density, and unpredictable link quality. Traditional topology-based, position-based, and cluster-based routing protocols encode fixed heuristics that react to connectivity loss only after it occurs, a poor structural match for the pace of change in IoV. This has driven more than a decade of research applying machine learning (ML), tabular reinforcement learning, deep reinforcement learning, graph neural networks, federated learning (FL), and metaheuristic-assisted clustering to make routing decisions predictive rather than reactive. This study presents a comprehensive survey of ML-based routing protocols for IoV, drawing on more than seventy studies published largely between 2020 and 2025. The study first characterizes the IoV architecture and the specific challenges that high mobility imposes on routing. The literature is organized into ten methodological categories: reinforcement learning, deep reinforcement learning, graph neural networks, hybrid DRL + GNN architectures, FL, software-defined networking integration, blockchain-assisted trust management, mobility and traffic prediction, digital-twin-assisted edge computing, bio-inspired clustering, and multi-agent reinforcement learning, offering a critical analysis of the strengths, weaknesses, and evaluation practices of representative protocols in each category rather than a simple enumeration. A comparative analysis distills the trade-offs across paradigms along latency, packet delivery ratio, overhead, scalability, and deployability. It identifies open research issues, including inconsistent convergence-time reporting, limited cross-scenario generalization, training–inference asymmetry on resource-constrained onboard units, and the near absence of standardized benchmarks. Future research directions are outlined toward hybrid prediction–decision architectures, privacy-preserving FL at scale, and trustworthy, explainable routing decisions. The survey is intended as both a reference map of the field and a foundation for designing the next generation of learning-based IoV routing protocols.
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