An efficient group key establishment in location-aided mobile ad hoc networks
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Bibliographic record
Abstract
Mobile Ad hoc Networks (MANETs) create additional challenges for implementing the group key establishment due to resource constraints on nodes and dynamic changes on the topology. To facilitate the deployment of group key agreements in MANETs, a range of distributed algorithms have been proposed. However, for a given level of security, these algorithms incur linearly increasing communication and computational costs. In this paper, we present two scalable maximum matching algorithms (M2) to deploy binary tree-based group key agreements in MANETs. Furthermore, the proposed technique is lightweight since it uses the Elliptic Curve Diffie-Hellman key exchange in place of the regular Diffie-Hellman and also does not require third-party's support. The performance analysis shows that our distributed M2 algorithms reduce key establishment's round number from O(n) to O(log2n) and our novel group key establishment decreases communication cost and computational overhead significantly.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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