Empowering Consumer Electric Vehicle Mobile Charging Services With Secure Profit Optimization
Bibliographic record
Abstract
The rapid expansion of Intelligent Transportation System (ITS) services depends on the Electric Vehicle (EV) and Mobile Charging Station (MCS) consumer electronics industry, as well as the intelligent Consumer Internet of Things (CIoT) platform. The functioning environment of MCSs is inherently dynamic, influenced by inconstant user preferences, energy demands, and charging service availability. Adapting to these changes in near-real-time while ensuring cost efficiency and fairness poses a notable challenge. These consumer electronic devices share data with third parties, so privacy is a critical concern. This paper presents a secure, optimized approach for enhancing the performance and accuracy of charging/discharging scheduling of MCSs within the CIoT network while protecting consumers’ data. This study aims to develop an optimization mechanism that enables decentralized learning with minimal data transfer while preserving user privacy by embedding Federated Learning (FL) as a security layer in our system. Also, it aims to maximize the potential profit of these stations while optimizing their daily operational efficiency. We propose a fog-edge communication to enhance communication in the decentralized FL-based network. Evaluating the result demonstrated enhanced profit maximization for MCSs operating within the CIoT network to fulfill as many energy requests from EVs as feasible while reducing self-charging expenses.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".