Multi-Valued Model Checking IoT and Intelligent \nSystems with Trust and Commitment Protocols
Bibliographic record
Abstract
Abstract \nMulti-Valued Model Checking IoT and Intelligent Systems with Trust and \nCommitment Protocols \nGhalya Alwhishi, Ph.D. \nConcordia University, 2024 \nIn the era of connectivity, numerous domains utilize multi-sensor Internet of Things \n(IoT) and Intelligent Systems (IS) applications, which involve complex interactions among \nnumerous components in open environments. This complexity challenges the verification of \nthese systems’ reliability and efficiency. This study pioneers the verification of IoT applications \nand intelligent systems within multi-source data environments, employing multi-agent \ncommitment and trust protocols, particularly in uncertain and inconsistent settings. \nOur research introduces efficient frameworks to model and verify these systems, incorporating \ncommitment and trust protocols in settings characterized by uncertainty and \ninconsistency. We extend existing logics of commitment CTLcc and CTLc and the logic of \ntrust TCTL to multi-valued cases for reasoning about uncertainty and inconsistency. We \nintroduce 3v-CTLc and 3v-CTLcc, three-valued logics of commitment for reasoning about \nuncertainty, and 4v-CTLc and 4v-CTLcc, four-valued logics of commitment for reasoning \nabout inconsistency. In the context of trust, we introduce 3v-TCTL and 4v-TCTL, multivalued \nlogics for reasoning about uncertainty and inconsistency over systems with trust \nprotocols. \nTo address the complexity and time needed for developing direct algorithms, coupled \nwith the scarcity of multi-valued model checking tools, we developed reduction algorithms. \nThese algorithms transform the introduced multi-valued logics of commitment and trust \ninto their classical case or into CTL, facilitating interaction with efficient model checkers \nsuch as MCMAS+ and MCMASt, and NuSMV, respectively. To demonstrate the practicality and applicability of the tool in real settings, we presented \nand reported experimental results over multiple IoT and IS applications in healthcare, \nfinance, and smart buildings. Our findings indicate that the proposed approaches and the \nMV-Checker tool are highly efficient and scalable, providing accurate results under varying \nconditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".