Lightweight Pseudo Random Number Generator for Embedded Systems
Bibliographic record
Abstract
A cryptography algorithm for data transfer encryption provides confidentiality, requires considerable computing power, and is not commonly implemented in embedded systems with limited computing power, such as Programmable Logic Controller (PLC).PLC is the core component for automation and control in industrial automation.For decades, PLC has prioritized speed over security; program execution in PLC must be as efficient as possible.The cryptography algorithm uses a seed, the initialization vector, to encrypt the data with the cryptography key to strengthen the encryption.Pseudo Random Number Generator (PRNG) can be used as the initialization vector.This paper proposes the XORasm PRNG algorithm, the lightweight XORshift-based algorithm with a modified seed from the system's clock.The applied methodology generates and visualizes PRNG, tests the randomness, and implements the PRNG on compact PLC.XORasm is evaluated statistically with runs-test in simulation by comparing the algorithm to one of the simulated compact PLC's PRNG implementations.The findings from this research are that p-values demonstrate that XORasm is statistically and significantly more random than the current implementation, and there is evidence that XORasm's generated data distribution is practically random at a 99.95% confidence level, suitable for implementation in embedded systems as a lightweight PRNG.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".