Sustainable Secure Communication in Consumer-Centric Electric Vehicle Charging in Industry 5.0 Environments
Bibliographic record
Abstract
Industry 5.0, a revolutionary paradigm focused on intelligent manufacturing, has profoundly impacted the automotive industry. It offers reliable data transmission and enhances the security of vehicle network systems, meeting evolving consumer needs. With a growing emphasis on energy conservation and environmental protection, electric vehicles have become a prominent segment of clean energy vehicles. Ensuring convenient and secure charging services is crucial for their widespread adoption. To address this, we propose an authentication protocol that uses digital signatures for secure communication in consumer-centric electric vehicle charging in Industry 5.0 environments. The protocol’s security was rigorously validated through a comprehensive analysis employing the real-or-random model. Furthermore, a systematic assessment was carried out to gauge the protocol’s computational performance, communication efficiency, and energy expenditure, yielding highly favorable outcomes. The optimization of the communication protocol was instrumental in enhancing data transmission efficiency and reliability, thereby contributing to an improved user experience during the charging process. Simultaneously, the reduction in energy costs underscores the exceptional sustainability of our protocol. Consequently, our protocol guarantees secure charging and exhibits enhanced adaptability and sustainability.
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 | 0.003 | 0.005 |
| 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 | 0.001 | 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".