Effective public key infrastructure for vehicle-to-grid network
Bibliographic record
Abstract
A growth of electric vehicle (EV) technologies likely leads a fundamental shift not only in transportation sector but also in the existing electric power grid infrastructure. In Smart grid infrastructure, vehicle-to-grid (V2G) network can be formed such that participating EVs can be used to store energy and supply this energy back to the power grid when required. To realize proper deployment of V2G network, charging infrastructure having various entities such as charging facility, clearinghouse, and energy provider has to be constructed. So use of Public key infrastructure (PKI) is indispensable for provisioning security solution in V2G network. The ISO/IEC 15118 standard is ascribed that incorporates X.509 PKI solution for V2G network. However, as traditional X.509 based PKI for V2G network has several shortcomings, we have proposed an effectual PKI for a V2G network that is built on based on elliptic curve cryptography and self-certified public key technique having implicit certificate to reduce certificate size and certificate verification time. We show that the proposed solution outperforms the existing solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".