Using Markov Chains to Model Sensor Network Reliability
Bibliographic record
Abstract
In the recent decades computing systems have become ubiquitous in our daily life. Due to wear and tear, limited component lifetime, and extraneous factors, among other reasons, all of the systems that we design and implement are subject to failure. One of the main areas in the field of fault tolerance, system evaluation, is concerned with the analysis of systems and faults as well as their operational environments. In the context of system evaluation, this paper is concerned with failure modeling and fault prediction. We propose a model for evaluating network systems in the context of failure and repair. Although the focus here is on sensor networks, it can surely be extended to other situations. A systems engineer can use the proposed model to estimate the longevity of a system and plan appropriate maintenance during the system design or maintenance phases. The approach makes use of Markov chains to model failure states of the system based on historical data. The effectiveness of this model is demonstrated through preliminary experiments and a case study, which also confirm intuitions about the effects of network topology on the network's reliability.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".