Secure and Efficient Urban Parking Solution: A Blockchain-Integrated Smart Parking Management System
Bibliographic record
Abstract
In increasing limited space and congestion, the parking vehicle management is important in the smart cities. The existing method often fails in smart parking solutions such as security, and energy efficiency. The suggested Smart Parking Management System enhances user experience, security, and parking efficiency in order to address this issue. This proposed system begins with registering and authenticating the user via mobile app and user accounts are secured. To analyse the real-time availability of spaces, the sensors in the parking zone employed to found the whether the spaces or occupied or available. Before sending the collected sensor data to the cloud for safe blockchain storage, it first pre-processed to remove irrelevant information. Afterthought, booking the parking area the smart contracts is taken care of payments for ensuring the smooth transaction process. Flowingly, parking assignments using dynamic space allocation algorithms using Reinforcement methods based on the past data and user behavior. The user data protection and unauthorised used activity is detected using federated learning method and multi-factor security methods. Users get notifications when spots become available, and the system continuously learns to enhance the upcoming parking experiences. The advanced smart parking management system gradually improves the user satisfaction. Overall this innovative method surpasses the existing methods in the light of effectiveness, security, and a sustainable urban environment.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".