ZONER: A ZONE-based Sensor Relocation Protocol for Mobile Sensor Networks
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".