ZONER: A ZONE-based Sensor Relocation Protocol for Mobile Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
In mobile sensor networks, self-deployment and relocation are two different research issues, both of which involve autonomous sensor movement. They share in most cases a common goal, that is, to improve overall network sensing coverage. Under this circumstance, some self-deployment algorithms may be applied to solving relocation problem without modification. However, considering efficiency, they will not be a good option in the scenario with high sensor failure rate. Existing sensor relocation protocols are not quite practical because they rely on strong assumptions and/or have weakness in maintaining network topology. In this paper, we propose a distributed zone-based sensor relocation protocol, ZONER, for mobile sensor networks on the basis of a restricted flooding technique, i.e., ZFlooding. Requiring zero-knowledge about sensor field, the ZONER is able to effectively discover previously-deployed redundant sensors without being concerned with obstacles or network ununiformity, and it relocates them in a shifting way to replace failed non-redundant ones without changing network topology. At the end of the paper, we prove the correctness of the ZONER and point out our future work
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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