Fixed node assisted collection tree protocol for mobile wireless sensor networks
Why this work is in the frame
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Bibliographic record
Abstract
The collection tree protocol (CTP) is widely used in static wireless sensor network applications. With the increasing deployment of mobile WSNs, the performance of this scheme in mobile scenarios becomes extremely important. The motivation in using collection tree protocols over clustering and mobile ad hoc routing protocols is there simplicity resulting in reduced control packets but higher packet losses. We introduce the fixed node assisted-CTP (FNA-CTP) algorithm which aims to enhance the performance of CTP in mobile scenarios by tuning the parameters of CTP for the mobile and fixed nodes in order to reduce the messaging overhead required to maintain the network in mobile scenarios. We provide a detailed evaluation of the performance of FNA-CTP in different mobile sensor network scenarios through a set of simulations which indicates the superior performance of FNA-CTP and discuss various design issues associated with this scheme.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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