A taxonomy of protocol frameworks and gap analysis for adaptive publish/subscribe distributed realtime embedded systems
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
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.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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