Lightweight Dynamic Group Rekeying for Low-Power Wireless Networks in IIoT
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".