Decentralized Edge Collaboration for Seamless Handover Authentication in Zero-Trust IoV
Why this work is in the frame
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Bibliographic record
Abstract
Given the frequently changing and potentially unreliable environment, the seamless handover authentication is essential to achieve zero-trust Internet of Vehicles (IoV) network with dramatically enhanced communication and transportation safety. The traditional centralized handover authentication schemes may suffer from the excessive latency and situation agnostic limitation, leading to potential interruption of critical services for fast moving vehicles. To overcome the above challenges, this paper proposes a novel decentralized edge collaboration-based handover authentication scheme with the assistance of blockchain for providing continuous protections in zero-trust IoV. A distributed learning process is designed by involving multiple authentication cooperators (ACs) to collect device/location-related features of vehicles at network edge and then to verify their identities. During the movement of vehicles, the access point (AP) could select new ACs by transferring the security information from existing ACs to the new members for seamless handover authentication. A situation-aware AC selection and update algorithm is proposed for maximizing handover authentication accuracy. Moreover, a hierarchical blockchain-assisted security information transfer and reputation management mechanism is designed for reliable collaboration and efficient management in zero-trust IoV. Compared with the existing schemes, our results characterize the outperformance of the proposed scheme in authentication accuracy and time cost of handover.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it