A performance evaluation of a context-aware path recommendation protocol for Vehicular Ad-hoc Networks
Bibliographic record
Abstract
Many protocols and mechanisms have been proposed aiming to find an alternative path towards each targeted destination in downtown and urban areas. These protocols recommend the fastest path (i.e., least congested path) without considering the services or conditions of the recommended road segments. In this work, we propose a real-time, distributed, and context-aware path recommendation protocol. The proposed protocol considers the existence of special services at alternative road segments and guarantees a congestion-free level for each road segment that is located at a critical service (e.g., hospital, school, etc). Moreover, it considers the conditions of each traveled road segment (e.g., pot-holes, weather conditions, obstacles, etc), while recommending the path towards any targeted destination. We discuss and report on the performance of our protocol compared to other path recommendation and traffic congestion avoidance techniques, using an extensive set of scenarios and experiments implemented in NS-2.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".