Improving Neighbor Localization in Vehicular Ad Hoc Networks to Avoid Overhead from Periodic Messages
Bibliographic record
Abstract
Vehicular Ad Hoc Networks (VANETs) have been widely studied, and the deployment of such networks is likely to happen soon as the required technology and commercial opportunities are already available. Most of applications proposed for these networks require a localization mechanism with reasonable accuracy. In addition to applications, most protocols rely on the availability of a system that determines vehicles' positions. As many vehicles are (or are in the process of being) equipped with GPS, the accurate localization of vehicles in VANETs is achievable. However, one issue that has been ignored is the localization of neighboring vehicles. In VANETs, nodes usually move at high speeds; the information about the position of a neighbour vehicle is therefore fast outdated. The naive approach for handling this issue is to increase the frequency by which periodic messages containing a node's position are exchanged. This solution might lead an unfeasible overhead significantly dropping the bandwidth available for the exchange of services' data. In this paper, we study this problem further, and propose a solution where vehicles predict the position of a neighbor for the near future. Through extensive experiments, we show that a prediction model of low complexity was able to considerably increase the accuracy of neighbor localization. Our mechanism has achieved an accuracy of 50 centimeters with a frequency of exchange of beacons 75% smaller than the naive approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".