“CANthings”(Context Aware Network for the Design of Connected Things) service modeling based on Timed CPN
Bibliographic record
Abstract
In the past few years, the advances in context-aware systems and sensor technologies, has elevated the Internet of Things (IoT) development greatly and rather quickly. Services of IoT systems must be reasonably designed to provide not only the user's requirements and requests, but also perceive the environmental context and customized services to get user's satisfaction. Systematic modeling methodologies are essential to control the correctness of the services and the systems behaviors among dynamic changing contexts. The presented solution will be a novel IoT framework, “CANthings” (Context-Aware Networks for the Design of Connected Things) to identify IoT needs.This paper mainly promotes and analyzes an IoT system modeling methodology based on Timed Colored Petri Net (TCPN) to check the effectiveness of the provided services in the CANthings framework. Our goal is to present a standard solution that can be used in high-technical research and industrial projects.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".