TD‐PSO: Task distribution approach based on particle swarm optimization for vehicular ad hoc network
Bibliographic record
Abstract
Abstract The rapid advancement of the artificial intelligence revolution during the past decade has significantly affected vehicular ad hoc networks (VANETs). Several applications have been introduced that must meet the requirements of a VANET, including automatic driving and preaccident alerts and broadcasting of video. The customization of vehicles for implementation of these applications is costly and might not be possible due to many constraints, particularly resource limitations. In order to achieve compliance with the VANET framework within the resource limitations, this article proposes limiting the time frame related with the individual parts of the process. To this end, this article recommends that the resource‐intensive ciphertext‐policy attribute‐based encryption (CP‐ABE) task be simplified by virtue of partitioning it into subtasks. This can be achieved by a machine‐learning technique (decision tree) in a manner that significantly influences the completion times of all subtasks. An approach based on particle swarm optimization (PSO), called task‐distribution PSO (TD‐PSO) , is proposed to perform the CP‐ABE task distribution on a VANET. The performance of this approach is evaluated by comparison with a genetic algorithm (GA), followed by comparison of these two solutions with the optimal solution proposed by the linear programming (LP) method. Results show that the TD‐PSO approach consumes less overhead than the GA. Moreover, comparison with the optimal solution proposed by LP shows that the near‐optimal solution obtained using TD‐PSO is more accurate than that obtained using the GA in most scenarios.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".