An efficient neighborhood prediction protocol to estimate link availability in VANETs
Bibliographic record
Abstract
Vehicular Ad Hoc Networks (VANETs) are a new trend that offers many opportunities to the development of a wide range of interesting services. These services range from providing entertaining applications, such as videoconferencing, to enhancing safety conditions through automatic breaking or improving emergency response.VANETs are highly unstable environments due to their dynamic topology and the lack of previous deployed infrastructure. Topology dynamism is related to the usually short range of communication of such networks and to the high mobility of vehicles. This mobility characteristic of vehicles diminishes the suitability of solutions developed for general Mobile Ad Hoc Networks (MANETs) to VANETs.In this paper, we have designed and evaluated the Neighborhood Prediction Protocol (NPP). In essence, NPP tries to anticipate the availability of future links between vehicles through a mobility prediction model. Therefore, topology changes can be detected earlier and handled properly before it depreciate network performance. We show through extensive simulations that neighborhood prediction is feasible and does not incur into excessive overhead. NPP can be used for example for resource reservation, routing continuity or to improve handoff procedures.
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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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".