A Secure and Efficient Wireless Charging Scheme for Electric Vehicles in Vehicular Energy Networks
Bibliographic record
Abstract
To address the limited driving range of electric vehicles (EVs) and promote EVs’ penetration, vehicular energy networks (VENs) have emerged and opened possibility to charge EVs in motion via dynamic wireless power transfer (DWPT) technology. However, security and efficiency concerns arise due to the untrusted operating environment and EVs’ selfish charging/discharging behaviors. Existing trust models rely on the personal recommendations from neighboring EVs to identify malicious entities in VENs, which may cause potential privacy breaches and data misuse for recommenders. Besides, it is challenging to optimally schedule EVs’ energy behaviors by considering complex interactions among three energy entities (i.e., energy nodes, charging EVs, and discharging EVs). To this end, by leveraging blockchain technology and game theory, this paper proposes a secure and efficient wireless charging scheme to address these issues in VENs. Firstly, a blockchain-based fine-grained access control mechanism with traceability and auditability is presented to enable EV users to fully control and audit their personal rating data usage during trust management by logging data activities and issuing access tokens into decentralized ledgers. In this manner, the privacy of recommenders can be preserved by fully controlling the access and usage of personal rating data. Furthermore, by introducing cooperative wireless energy transfer mode, a hierarchical game-based energy scheduling algorithm is developed to optimize the strategies of three energy parties tier by tier, while considering their cooperation and competition. Finally, extensive simulations are conducted, which demonstrate that the proposed scheme can effectively improve users’ utility and security of energy transmission for EVs, compared with existing representative approaches.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".