Multilayer flavoured dynamic source routing in mobile <i>ad-hoc</i> networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it