An Efficient Route Optimization Using Ticket-Id Based Routing Management System (T-ID BRM)
Bibliographic record
Abstract
Abstract MANET – Mobile Ad Hoc Network is the connection of several remote mobile nodes. These networks are dynamic and independent to move anywhere. It does not contain any central controller, and hence it is stated as a structure less network. MANET is one of the most emerging technologies getting popular in recent days. It's significant features, and real-time issues grab the research community's attention towards it. The unstable data transmission will leverage the overall network performance, and designing an energy-efficient routing protocol is challenging. The main reason for this problem is the lack of a stable multipath routing system and resource constraints. Various research works have launched several efficient routing mechanisms, but the need for advancement still exists. In this paper, we propose a Ticket –ID Based Routing management system for enabling reliable routing for the entire network. The proposed system work under the TID principle, which executes according to the node properties and routing maintenance system. This system works under the supervision of TID- Routing manger. The TID routing manager is responsible for managing the ticketing pool and allocating unique ticket-ID based on the collected node factors such as energy, node location, speed, etc. The proposed routing system facilitates the shortest path for reliable communication. An experimental work using NS- simulator is done with the proposed method. The proposed work's efficiency is determined using a comparison work between T-ID BRM with TABRP, OGFSO, and PDMR. The observation states that the proposed Ticket-ID system achieves 94% better than other existing methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".