Multi-valued Model Checking A Smart Glucose Monitoring System with Trust
Bibliographic record
Abstract
IoT in the e-Healthcare domain has recently seen much interest. The IoT applications in this domain involve extensive interactions between many components within open environments, making verifying these applications a significant challenge. This paper introduces a new framework for modelling and verifying IoT applications in the healthcare domain using a practical system verification technique called multi-valued model checking. We focus on applications that involve interactions based on trust protocols under inconsistency. Specifically, we introduce a new logic of trust called (4v-TCTL) to reason about the inconsistency between designers over IoT systems. We use a Smart Glucose Monitoring System as a case study. We model our system and assign six trust properties to be checked against this system. We introduce a new reduction algorithm for reducing our four-valued model checking problem to the two-valued version to reuse an existing model checker called MCMASt. We verified our system using our approach and reported the experimental results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".