Can Embedded Real-Time Linux System Effectively Support Multipath Transmission? An Experimental Study
Bibliographic record
Abstract
The rise of technologies such as 6G networks, edge computing, and the Industrial Internet has led to a dramatic increase in the amount of data that needs to be transmitted over heterogeneous integrated networks. The resources of embedded devices limit the ability of the Industrial Internet to transmit data. While the multipath transmission mechanism can mitigate data transmission issues of low reliability and low real-time performance from the network-level perspective. As the complexity of industry applications increases, however, the phenomenon that the high-quality data transmission is subject to the influence of the underlying layer is becoming increasingly apparent. The paper aims to explores the possibility of multipath transmission protocol running on a real-time kernel from the perspective of the operating system, as there is a lack of research and reports in this area. Based on RT-Preempt, a real-time system RT-Linux suitable for the “NXP i.MX6Q” ARM integrated board has been proposed, which replaces the native Linux kernel to optimize and enhance its real-time performance. As described in the experiment part, the original standard Linux system OR-Linux and the new RT-Linux are tested with single-threaded and multi-threaded load experiments, respectively. The results of the analysis show that this paper provides a way of validating the trial data and ensuring its accuracy using the lognormal distribution model, which is a statistical distribution used to model variables that are positive and skewed to the right. The RT-Linux scheme has better real-time performance and is more stable than the OR-Linux scheme after real-time processing, showing the viability of the scheme.
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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".