Fully secure pairwise and triple key distribution in wireless sensor networks using combinatorial designs
Bibliographic record
Abstract
We address pairwise and (for the first time) triple key establishment problems in wireless sensor networks (WSN). We use combinatorial designs to establish pairwise keys between nodes in a WSN. A BIBD(v; b; r; k; λ) (or t - (v; b; r; k; λ)) design can be mapped to a sensor network, where v represents the size of the key pool, b represents the maximum number of nodes that the network can support, k represents the size of the key chain. Any pair (or t-subset) of keys occurs together uniquely in exactly λ nodes. λ = 2 and λ = 3 are used to establish unique pairwise or triple keys. Our pairwise key distribution is the first one that is fully secure (none of the links among uncompromised nodes is affected) and applicable for mobile sensor networks (as key distribution is independent on the connectivity graph), while preserving low storage, computation and communication requirements. We also use combinatorial trades to establish pairwise keys. This is the first time that trades are being applied to key management. We describe a new construction of Strong Steiner Trades. We introduce a novel concept of triple key distribution, in which a common key is established between three nodes. This allows secure passive monitoring of forwarding progress in routing tasks. We present a polynomial-based approach and a combinatorial approach (using trades) for triple key distribution.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 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".