Performance Analysis of Routing Protocols for Wireless Ad-Hoc Networks
Bibliographic record
Abstract
Wireless ad-hoc networks are decentralized wireless networks that do not rely on an infrastructure, such as base stations or access points. Routing protocols in ad-hoc networks specify communication between routers and enable them to select routes between a source and a destination. The choice of the routes is performed by routing algorithms. In this paper, we use OPNET Modeler version 16.0 A to simulate three routing protocols for wireless ad-hoc networks in several Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) scenarios. We analyze route discovery time, end-to-end delay, download response time, and routing traffic overhead in static, less dynamic, and highly dynamic mobility scenarios. Simulation results indicate that Ad-Hoc On-Demand Distance Vector (AODV) protocol is the most flexible when compared to Dynamic Source Routing (DSR) and Optimized Link State Routing (OLSR) protocols in the case of movement. OLSR is the only protocol that meets the end-to-end delay requirements of less than 20 ms.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".