An Adaptive Clustering Approach for Dynamic Service Provisioning in Vehicular Cloud Networks
Bibliographic record
Abstract
The integration of Internet of Things (IoT) and connected car technologies enables resource sharing and collaboration within the vehicular ecosystem. Through IoT connectivity, vehicles can dynamically share and optimize the utilization of resources. However, Vehicular Cloud Networks (VCNs) face unique challenges due to their dynamic topology changes caused by high vehicle mobility and unpredictability. Clustering is a key mechanism used in vehicular networks to address such issues. Clustering groups nearby vehicles into clusters to improve routing efficiency, reduce broadcast storms, and enable distributed coordination. When applied to VCNs, clustering can efficiently match resource requests to resource-rich vehicle candidates. This paper proposes an adaptive clustering approach that forms clusters based on vehicle mobility parameters and cloud resource profiles. A similarity score is used to group vehicles with similar attributes, and a connectivity-based method selects stable cluster heads. An adaptive joining policy dynamically adjusts clustering thresholds in response to changes in network traffic. Simulation results show that the proposed approach achieves more stable clusters and lower overhead compared to a baseline clustering algorithm. It also balances cluster count and service quality under varying network conditions through adaptive threshold adjustment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".