A mesh hybrid adaptive service discovery protocol (MesHASeDiP): Protocol design and proof of correctness
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
The characteristics of wireless mesh networks (WMNs) have motivated us in the design of an efficient service discovery protocol that considers the capabilities of such networks. In this paper we propose a novel service discovery technique for WMNs. Our approach reduces the discovery overhead by integrating the discovery information in the routing layer. Based on an adaptively adjusted advertisement zone of service providers, we have combined proactive and reactive service discovery strategies to come up with an efficient hybrid adaptive service discovery protocol for WMNs. Our protocol optimizes the network overhead. We will show that our proposed protocol is scalable and that it outperforms existing service discovery protocols in terms of message overhead.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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