An edge computing-based monitoring framework for situation-aware embedded real-time systems
Bibliographic record
Abstract
An embedded real-time system (ERTS) needs to provide continuous service in various dynamic situations. Such service requirements in different situations create the need for the ERTS to monitor its environment at run time, gather knowledge of its situations, and guarantee its timing and operational behavior through self-adaptation. Recent advances in sensor technologies have introduced cameras, lidar, and radar as powerful monitoring tools. However, processing and storing raw sensor streams require significant storage and computational ability. An ERTS is embedded in nature, and therefore, it has limited storage and processing capacity. This paper considers that the ERTS contains an analytics endpoint (edge node). We present an edge computing-based monitoring framework that characterizes environmental situations at run time by identifying events and their properties. We enable the framework to store and process from a significantly reduced dataset by creating a knowledge base. The framework also allows the ERTS to identify resource, performance, and safety constraints in the edge node for each situation. The framework assists the ERTS in adapting to the situations (if the constraints are satisfied) by determining adaptive tasks that need to be triggered with respect to the environmental events. The experimental analysis shows that the framework present in the edge node assists in situation characterization in terms of the identified events and admission of adaptive tasks. The monitoring framework also allows improvement regarding the probability of failure and average response time. We use the earliest deadline first (EDF) scheduling algorithm with and without considering the edge node and perform a comparative schedulability analysis. We demonstrate that overall demand due to the admission of adaptive tasks and situation-driven analytics exceeds available supply, which can be addressed using the proposed edge computing-based framework.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".