A Fault Inference Mechanism in Sensor Networks Using Markov Chain
Bibliographic record
Abstract
The reliability of communication and sensor devices has been recognized as one of the crucial issues in Wireless Sensor Networks (WSNs). In distributed environments, micro-sensors are subject to high-frequency faults. To provide high stability and availability of large scale sensor networks, we propose a fault inference mechanism based on reverse multicast tree to evaluate sensor nodes' fault probabilities. This mechanism is formulated as maximization-likelihood estimation problem. Due to the characteristics (energy awareness, constraint bandwidth and so on) of wireless sensor networks; it is infeasible for each sensor to announce its working state to a centralized node. Therefore, maximum likelihood estimates of fault parameters depend on unobserved latent variables. Hence, our proposed inference mechanism is abstracted as Nondeterministic Finite Automata (NFA). It adopts iterative computation under Markov Chain to infer the fault probabilities of nodes in reverse multicast tree. Through our theoretical analysis and simulation experiments, we were able to achieve an accuracy of fault inference mechanism that satisfies the necessity of fault detection.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".