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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".