BlockDEV: Blockchain-Based Decentralized Charging Service Provider Selection for Electric Vehicles
Bibliographic record
Abstract
In future, the substantial rise in number of electric vehicles (EVs) will increase the charging-power demand significantly, placing considerable strain on existing charging stations. The adoption of renewable energy resources (RESs) is seen as a potential solution to address scalability of existing charging network, by leading the households to trade and capitalize on their surplus power. However, the centralized nature of EV charging infrastructure pose several security and privacy threats for EV owners and prosumers. Distributed ledger technologies offer a potential solution to the current privacy and security challenges. Thus, we propose a blockchain-based EV architecture that integrate prosumers into existing EV infrastructure. To begin with, we propose BlockDEV: a decentralized charging provider selection mechanism, which allows EVs to select a charging provider without sharing any personal information. A smart contract design is then proposed, allowing households with RESs to trade their surplus energy using dynamic pricing based on energy supply and demand, and, allowing EVs to make a decentralized charging slot reservation. Assessments reveal that BlockDEV provides 98% more dynamic pricing updates that accurately represent genuine energy shifts in the network. Furthermore, it incurs 50% fewer blockchain transactions compared to the baselines, and outperforms them on the effectiveness of the reputation system, and the preservation of location privacy.
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 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 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".