A Key Distribution and Management Scheme for Clustered Ad Hoc Sensor Networks
Bibliographic record
Abstract
Presently, the most practical approach for bootstrapping initial secret keys in sensor networks is to load keys into sensor nodes before they are deployed [54,57].These initial keys are used to establish pairwise keys for node-to-node communications.After that, the pairwise keys are used to distribute cluster keys for broadcasting messages.However, some of them are vulnerable to impersonation attacks.We describe a novel key distribution and management scheme for clustered ad hoc sensor networks.The scheme uses the Boneh-Franklin's ID-based encryption (IBE) scheme and Yi's ID-based signature scheme to achieve mutual authentication between nodes [50].The signature scheme is used to distribute a cluster key which can be updated.We also derive a master key from the signature which can also be updated when needed.Our contribution is that we resolved the impersonation problems that exist in current key distribution schemes for ad hoc sensor networks.A timestamp is incorporated in the signing procedure, avoiding message replay attacks.Finally, This scheme can be extended to hierarchical ad hoc sensor networks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".