Multilayer flavoured dynamic source routing in mobile <i>ad-hoc</i> networks
Bibliographic record
Abstract
Dynamic source routing (DSR, introduced in 1996) is one of the most frequently used routing protocols for mobile ad-hoc networks (MANETs). Numerous MANET protocols were created based on DSR's algorithm. In addition to inheriting the overall performance specifications of DSR, these MANET protocols are designed to perform optimised for specific functionality. To name a few, these functionalities include: hierarchical routing, security-aware routing and multipath routing. Such flavoured DSR schemes (X-DSR) are often compared against the original DSR protocol through simulation results. The purpose of this survey is to first introduce DSR in detail, discuss most of the DSR flavours, point out their specific features, and to present a complete survey of the analyses given in the current literature against the original DSR protocol. Following this in-depth discussion, we introduce an X-DSR-aware management architecture, which utilises a multilayer scheme that imports parameters from different layers (network, data-link and physical) and performs current network condition matching compared to the closest pre-defined network condition groups. The output of such a match is the selection of the most optimal routing protocol, which satisfies most of the criteria of the pre-defined condition group.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".