Author manuscript, published in "IFIP WSAN 2008, Canada (2008)" HERO: Hierarchical kEy management pRotocol for heterOgeneous wireless sensor networks
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. Early researches focused on the security of homogenous sensor networks. However, recent works have demonstrated that the presence of heterogeneous sensor nodes gives better performance than homogenous ones in terms of energy consumptions, storage overhead, and network connectivity. In this paper, we propose a hierarchical key management scheme named HERO which is based on random key pre-distribution. HERO aims to construct a secure tree instead of complete connected graph as in existing schemes. Thanks to this realistic assumption, our key management scheme reduces considerably the number of pre-loaded keys assigned to each node while maintaining high security level at the same time. The preliminary simulation results using TOSSIM demonstrates that our scheme outperforms existing ones with respect to storage overhead. 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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