Performance evaluation of routing with ARC congested node over BTSnet for mobile ad hoc networks and inter-vehicular communications
Bibliographic record
Abstract
In this paper the new algorithm to improve the performance of data delivery percentage over Bangkok Train System Network (BTSnet) is presented. BTSnet can be implemented in regular traffic patterns by the public transportation authority to improve the performance of mobile ad hoc networks (MANET) for inter-vehicular communication systems (IVCS) in Bangkok. Since ad hoc networks consist of mobile nodes that are randomly moving and unpredictable, IVCS can provide users with a range of services. The implementation of IVCS is possible using MANET. Some places are congested and drop-packets occurred and it will be degrading performance. The proposed algorithm improves the performance by adjusting data transmission rate, rather than fixed rate, after the collection of current number of packet drop, data rate and packet delay time. Several simulations are performed by NS-2 simulations under the congested situation then our proposed algorithm results are compared to ones from traditional method. Results from each BTS-Train scenario will be simulated under the traffic situation in the geographical area of Bangkok. We found impressive results that support our algorithm. (6 pages)
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".