Decentralized Quality of Service Based System for Energy Trading Among Electric Vehicles
Bibliographic record
Abstract
This paper incorporates a new perspective into P2P energy trading coordination schemes for EVs by considering Quality of Service (QoS) management. QoS could be utilized as a control metric to facilitate resilient and reliable transactions according to user preferences. To that end, this paper proposes a novel decentralized QoS-based system for P2P energy trading among EV energy providers and consumers. The system utilizes smart contracts to carry out the matching between EVs and monitor the delivery of a QoS-based P2P contract without the presence of a third party. Two QoS-based mechanisms are proposed to match trading EVs in this system. The proposed mechanisms are designed to match single-consumer to multiple-providers and multiple-consumers to multiple-providers based on consumers’ and providers’ QoS requirements and offers, respectively. A fuzzy-based approach with minimum and intelligible input is introduced to determine the weight values of each QoS attribute. Further, a penalty mechanism is developed to discourage dishonest requests/offers and ensure that trading parties stick to their contractual obligations. Numerical simulations are conducted to validate the effectiveness of the proposed QoS-based mechanisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".