A Connectivity-aware Method for Infrastructure-less Vehicular Cloud Service Discovery
Bibliographic record
Abstract
In the context of the Internet of Things (IoT), where interconnected devices require efficient communication and resource usage, service discovery poses a significant challenge in infrastructure-less Vehicular Cloud Networks (VCNs) due to their dynamic topology and intermittent connectivity. This paper proposes a distributed two-level directory approach to optimize service provisioning in such networks. In this approach, vehicles are first organized into clusters that are managed by Cluster Heads (CHs), responsible for maintaining first-level distributed directories. To strike a balance between efficiency and resource allocation, a novel scoring system is introduced to group clusters, forming second-level directories for broader visibility. Simulations evaluate the proposed approach against alternative methods, considering wait time to register, discovery delay, and hit percentage ratio under varying network conditions. The results demonstrate improved performance, thereby validating the advantages of hierarchical directories for service discovery in infrastructure-less VCNs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".