An Efficient Fault Tolerant Location Based Service Discovery Protocol for Vehicular Networks
Bibliographic record
Abstract
Recent studies on service discovery protocols in vehicular networks have shown promising results. However, very little work has been reported on how to integrate the fault tolerance feature into these mechanisms. This feature is vital for the success of the deployment of service discovery protocols in vehicular networks. Drivers on their cars should not have their requests dissatisfied or their connections dropped because a service provider or some components between the service provider and the service requester have failed. In this paper, we present a fault tolerant location based service discovery protocol for Vehicular Networks. The main advantages of our protocol is its ability to tolerate service providers failure, communication links failure and roadside routers failure. We present our fault tolerant protocol, and report on its performance evaluation. Our simulation experiments show that our proposed fault tolerant LocVSDP algorithm improves the success rate significantly over the basic LocVSDP. In fact, the success rate is improved in the roadside routers failures scenario and in the links failures scenarios by 50 percent and 30 percent respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".