A Secure Cooperative Approach for Nonline-of-Sight Location Verification in VANET
Bibliographic record
Abstract
In vehicular ad hoc networks (VANETs), network services and applications (e.g., safety messages) require an exchange of vehicle and event location information. The data are exchanged among vehicles within each vehicle's respective radio communication range through direct communication. In reality, direct communication is susceptible to interference and blocked by physical obstacles, which prevent the proper exchange of information about localization information. Obstacles can create a state of nonline of sight (NLOS) between two vehicles, which restricts direct communication even when corresponding vehicles exist within each other's physical communication range, thus preventing them from exchanging proper data and affecting the localization services' integrity and reliability. Dealing with such obstacles is a challenge in VANETs as moving obstacles such as trucks are parts of the network and have the same characteristics of a VANET node (e.g., high-speed mobility and change of driving behavior). In this paper, we present a location verification protocol among cooperative neighboring vehicles to overcome an NLOS condition and secure the integrity of localization services for VANETs. The simulation results showed improvement in neighborhood awareness under NLOS conditions. A solution such as that we propose will help to maintain localization service integrity and reliability.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".