Coverage preserving aggregation protocols for dense sensor networks
Bibliographic record
Abstract
Sensor networks are often deployed more densely than would be minimally required. In such cases, node scheduling protocols can be used to determine which nodes are active, and which nodes sleep so as to conserve energy and prolong network lifetime. A drawback of node scheduling approaches, however, is delay due to node or communication failure(s), and subsequent wake-up of replacement node(s), during which monitoring coverage of some sub-region may be lost. This paper proposes an alternative approach for use in contexts in which the objective is to periodically collect sensing data that completely covers a region of interest. In the proposed approach, nodes dynamically determine during each round of data collection whether they should transmit their data, or whether their area is covered by neighbouring nodes that have already transmitted. Both unicast and broadcast-based data collection protocols are designed, and their performance compared using simulation to that of data collection protocols relying on node scheduling. Our results suggest that the coverage-preserving broadcast-based protocol can greatly improve reliability at the potential cost of increased traffic volume owing to non-minimal selection of transmitting nodes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".