Cooperative location verification for vehicular ad-hoc networks
Bibliographic record
Abstract
Vehicular ad hoc networks (VANETs) have attracted much attention over the last few years. Localization and position information of vehicles is very significant in VANETs; this is a result of the special nature of VANETs. In this paper, we propose a location verification approach to prevent position-spoofing attacks on VANETs. Cooperative Location Verification (CLV), which is our approach, basically used two vehicles, a Verifier and a Cooperator, to complete the verification of a vehicle (Prover). The Verifier and Cooperator sent a challenge to the respective Prover; and, the Prover was required to reply with its location information immediately which was based on radio frequency. The Verifier then verified the claimed location according to the Time-of-Flight of the signals in those two challenge-response procedures. In the simulation, the results show that our approach is better than both Secure Location Verification (SLV) and Greedy Forwarding Algorithm (GFA).
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".