A taxonomy of protocol frameworks and gap analysis for adaptive publish/subscribe distributed realtime embedded systems
Bibliographic record
Abstract
The growing prevalence of distributed real-time embedded systems in applications such as emergency response, disaster recovery, and ambient assisted living necessitates the use of protocol frameworks to support quality of service requirements and respond to changing environment conditions at runtime. This paper presents a taxonomy that can be used to classify protocol frameworks. The taxonomy includes several features that are relevant for supporting adaptive DRE systems. A brief overview of existing work in the area of protocol frameworks and related network management is provided, and this work is evaluated and classified in terms of the taxonomy. Finally, the paper analyzes the current work on protocol frameworks within the context of adaptive publish/subscribe distributed real-time embedded systems and highlights the gaps found. Our results show that adaptive protocol frameworks are (1) still an area largely addressed by research without standardization and (2) deficient in requirements for adaptive publish/subscribe DRE systems.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".