Lightweight Dynamic Group Rekeying for Low-Power Wireless Networks in IIoT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, a novel pseudorandom key chaining (PRKC) algorithm is introduced and evaluated. This lightweight symmetric scheme enables a transmission-triggered time variation of group keys in low-power wireless networks during broadcasting or multicasting. The proposed algorithm uses pseudorandom (PR) sequences, generated at the physical (PHY) layer of radio transceivers during a communication session, to symmetrically refresh the encryption keys on both sides of a communication link. This solution is scalable and suitable for large networks of nodes with limited resources in an Industrial Internet-of-Things (IIoT) environment. The strength of generated keys was tested with the use of the National Institute of Standards and Technology Special Publication 800-22 (NIST SP 800-22) statistical suit. No binary patterns that may indicate a vulnerability were detected. The randomness of generated key sequences was further analyzed with the use of strange attractors approach which demonstrated that these sequences are robust against attacks, such as spoofing or intelligent brute forcing. To assess the real-time delay and computational overhead of the algorithm, it was implemented on a Raspberry Pi board. The results demonstrated that the PRKC algorithm runs over 60% faster and requires over 40% less CPU effort per round than the conventional hashing-based schemes. In addition, it does not require any communication overhead and transmission energy.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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