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 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".