Edge Communication Efficiency with GNN in the Internet of Vehicles
Bibliographic record
Abstract
Vehicular edge plays a central role in ensuring an effective allocation of resources to provide services and applications. Resource allocation and communication in dynamic vehicular environments face numerous challenges in efficiently managing resources and data sharing, specifically managing the intricate balance of connectivity, storage, energy, computing, and cost of resources. These challenges are also affected by mobility, resulting in the demand for precision in communication range, density, and resource availability. Efficient resource allocation is a critical objective within vehicular networks, and to achieve this, intelligence, prediction, optimization, and incentive modelling are often employed. However, challenges persist, such as sporadic connectivity, transmission delays, and the inherent uncertainty of highly dynamic environments. In response to these challenges, this paper introduces the use of graph neural networks (GNNs) to learn hidden spatial and functional patterns in complex vehicular networks. Combining with clustering-based methodologies. This approach enables the intelligent organization of network nodes, reducing transmission delays and enhancing connectivity in dynamic environments. The resulting framework supports predictions and estimates based on evolving communication and mobility patterns. They are further improving the efficiency of connectivity and communications in vehicular edge networks. Using graph neural networks (GNN) and clustering techniques to address connectivity challenges, reduce transmission latency, and manage the inherent unpredictability of rapidly changing vehicular settings, this study is poised to enhance the delivery of services and applications in vehicular networks. It also lays the foundation for prospective research into resource management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".