BlockEV: Efficient and Secure Charging Station Selection for Electric Vehicles
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
The Intelligent Transportation System (ITS) has become essential for the economical and technological development of a country. The maturity of communication technologies (Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V)) and the amalgamation of smart grids, electric vehicles (EVs) and energy trading resulted in a storm of research opportunities for green ITS. In addition, the combination of vehicular communication technologies and ITS enable efficient selection of EV charging stations (CS) and scheduling EVs charging requirements in real-time. However, the untrusted centralized nature of energy markets and EV charging infrastructures result in several privacy and security threats to EV user's private information. These security and privacy threats include targeted advertisements, privacy leakage, selling data to third party, etc. In this work, we propose BlockEV, a blockchain-based efficient CS selection protocol for EVs to ensure the security and privacy of the EV users, availability of the reserved time slots at CSs, high Quality of Service (QoS) and enhanced EV user comfort. First, a blockchain-based framework is introduced to implement secure charging services and trusted reservation for EVs with the execution of smart contract. Second, we focus on the efficient CS selection and propose a mechanism for EVs to select the CS locally without sharing private information to CS, while fulfilling their service requirements. Evaluations show that the proposed BlockEV is scalable with significantly low blockchain transaction and storage overhead.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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