Using Markov Chains to Model Sensor Network Reliability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it