Performance analysis of a distributed comparison-based self-diagnosis protocol for wireless ad-hoc networks
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers the problem of self-diagnosis of wireless ad-hoc networks using the comparison approach. In this approach, a MANET consists of a collection of n independent heterogeneous mobile hosts interconnected via wireless links, and it is assumed that at most σ of these mobile hosts are faulty. In order to diagnose the state of the wireless ad hoc network, tasks are assigned to pairs of mobiles and the outcomes of these tasks are compared. The agreements and disagreements among mobiles are the basis for identifying the faulty mobiles. The comparison approach is believed to be one of the most practical diagnosis approaches. We describe a new distributed self-diagnosis protocol for wireless ad hoc networks that is able to identify hard and soft faults in a finite amount of time. We then analyze the time and communication complexities of wireless ad hoc networks and show that it performs better, from a communication complexity viewpoint, than the existing protocols.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it