Key Predistribution for Homogeneous Wireless Sensor Networks with Group Deployment of Nodes.
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Bibliographic record
Abstract
Recent literature contains proposals for key predistribution schemes for sensor networks in which nodes are deployed in separate groups. In this paper we consider the implications of group deployment for the connectivity and resilience of a key predistribution scheme. After showing that there is a lack of flexibility in the parameters of a scheme due to Liu, Ning and Du, limiting its applicability in networks with small numbers of groups, we propose a more general scheme, based on the structure of a resolvable transversal design. We demonstrate that this scheme permits effective trade-offs between resilience, connectivity and storage requirements within a group-deployed environment as compared with other schemes in the literature, and show that group deployment can be used to increase network connectivity, without increasing storage requirements or sacrificing resilience. Keywords: Group-based deployment, key predistribution, wireless sensor networks 1
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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