MobileMAN: integration and experimentation of legacy mobile multihop ad hoc networks
Bibliographic record
Abstract
Although research on mobile ad hoc networks has been ongoing for some time, there are relatively few experiences with real ad hoc networks in laboratory testbeds, and users never use multihop ad hoc networks. This seems due to a gap between what end users might find useful, and what research is currently addressing. Indeed, a large portion of research activities concentrate on the development of novel solutions to optimize lower-layer protocols in often unrealistic settings, while little attention is devoted to the quality of service (QoS) these networks may provide to end users in realistic applicative scenarios. The MobileMAN project tried to contribute to reduce this gap by promoting a research plan aimed at combining theoretical research with the integration of developed solutions in prototypes to be used for validating them in realistic small- and medium-scale scenarios (few hops and 10‐20 nodes). The aim is to design and integrate a full protocol stack and experimentally quantify the QoS the system is able to provide to the users. This research approach points out that, also in this limited setting, several problems still exist to construct efficient multihop ad hoc networks. In the next article [1] we discuss how cross layering can be exploited to fix some performance problems identified in our analyses.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".