An efficient fault tolerant distributed path recommendation protocol for next generation of vehicular networks
Bibliographic record
Abstract
Several research studies have introduced an efficient and intelligent path recommendation protocols for vehicular networks. Communications among traveling vehicles and located roadside units (RSUs) have been utilized to investigate the traffic distribution over the road network. This helps construct the optimal path towards each targeted destination located on the road network. However, none of the previous proposed protocols in this field have specifically considered potential faults among the nodes of the vehicular networks or potential link failures. In this paper, we present a fault tolerant distributed-based path recommendation (TD-PR) protocol. Our protocol detects and tolerates faults occur among nodes and/or communication links. We present TD-PR protocol in this paper and report on its performance evaluation. Our simulation experiments show that TD-PR improves the success rate significantly over our previously proposed path recommendation protocol (ICOD). The success ratio is improved in roadside failure and link failure scenarios. In general TD-PR has better performance in terms of decreasing the traveling time and traveling distance compared to ICOD in these scenarios.
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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 | 0.003 |
| 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 | 0.000 |
| 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".