Secure and Privacy-Preserving Physical-Layer-Assisted Scheme for EV Dynamic Charging System
Bibliographic record
Abstract
Dynamic charging system will enable moving electrical vehicles (EVs) to charge their batteries through magnetic induction by charging pads (CPs) placed on a portion of the roadbed. To realize such a system, the EVs need to communicate with the various parts of the system that include a bank, a charging service provider (CSP), road side units (RSUs), and CPs. In this paper, we propose a secure and privacy-preserving physical-layer-assisted scheme for dynamic charging systems to secure payment and authentication and also preserve the drivers' location privacy. We develop an efficient hierarchical authentication scheme that considers the scalability nature of the system. Efficient cryptosystems are used to authenticate the EVs to the bank, CSP, and RSUs, but our evaluations indicate that the contact time of fast moving EVs and the CPs is too short to exchange multiple messages and execute time consuming operations. Therefore, we develop an efficient physical-layer-based authorization scheme that utilizes autocorrelation demodulation and hypothesis testing to enable the CPs to identify and charge the authorized EVs. Through extensive analysis, simulation, and practical experiments, we demonstrate that the proposed scheme is secure against the considered attacks and can achieve fast authentication and high authorization rate. Moreover, the proposed scheme can achieve full anonymity where no entity or even colluding entities can know the drivers' locations.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".