A cooperative multi-hop location verification for Non Line Of Sight (NLOS) in VANET
Bibliographic record
Abstract
Localization and exchanged position information plays a big role in Vehicular Ad-Hoc Networks (VANET). Several network services and applications, such as safety messages, require location information. The data are exchanged between vehicles within their radio communication range. In real life implementation, direct communication can be interfered and blocked by obstacles on the road preventing two vehicles from directly communicating with each other and creating a state of Non Line of Sight (NLOS) between them. Thus, preventing them from exchanging proper data which will have an effect on the localization service integrity and reliability. Moreover, securing location information under NLOS is yet a challenge. In this paper we propose a cooperative location verification protocol between neighboring vehicles for a NLOS conditions in order to secure the localization services for VANET. Simulation results showed improvement in neighborhood awareness under NLOS condition which will help maintaining localization service integrity and reliability.
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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 | 0.000 |
| 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) | 0.000 | 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".