Sustainable Secure Communication in Consumer-Centric Electric Vehicle Charging in Industry 5.0 Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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