Congestion control in vehicular ad hoc networks using meta-heuristic techniques
Bibliographic record
Abstract
In VANET, various limitations such as high mobility, high rate of topology changes, limitation of bandwidth, etc, play a significant role for reducing performance in these networks. Qualities of Service policies have been used to improve the performance of VANET. One of the significant parameters in Quality of Service is Congestion Control. The congestion control is used to ensure safe and reliable communication architecture. Three types of strategies are available for congestion control which consists of transmission power control, packet transmission frequently control and packet duration. Heuristic techniques can be used to define heuristic rules and finding feasible and good enough solution to some problems in reasonable time. According to heuristic's benefits, we are motivated to use these techniques in congestion control to generate efficient VANET. This work is aimed to improve congestion control with heuristic techniques to reduce the traffic communication channels while considering reliability requirements of applications in VANETs. The simulation results have demonstrated that meta-heuristic techniques features significantly better performance in terms of packet loss, throughput and delay compared with other congestion control algorithm within VANETs.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".