Design and performance evaluation of QoS aware and location based service discovery protocol for vehicular networks
Bibliographic record
Abstract
Recently, we have witnessed a growing interest in the study of service discovery in vehicular networks that guarantee QoS to service requesters. To the best of our knowledge, none of the existing service discovery protocols in Vehicular Networks have been provided in the literature that guarantee QoS to service requesters nd to the Vehicular Network. For efficient service discovery in vehicular networks, it is very important to provide users with services that suit better to their requests while balancing the load on service providers. Moreover, for service discovery protocol integrated with routing protocols, it is important to guarantee load balancing on routing paths between service providers and service requesters. In this paper, we present a QoS aware location based service discovery protocol for Vehicular Networks. Our protocol guarantees load balancing on service providers, and routing paths between service providers and service requesters. It permits also to choose service providers and routing paths between service providers and service requesters that satisfy some performance attributes specified by service requesters. We present our QoS aware protocol, report on its performance evaluation, and discuss our experimental results we have obtained using realistic scenarios.1
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".