A Fuzzy Method for Fault Tolerance in Mobile Sensor Network
Bibliographic record
Abstract
Faults occurring to sensor nodes are common due to the limitation sensors and the harsh environment sensor networks. The fault sensors lowest effect in network efficiency or in others word sensor network has fault tolerance. In this paper is studied the fault tolerance problem from the coverage point of view for sensor networks. In the proposed methods missing regions with faulty sensors recoup by its neighbors and using minimum redundant sensors. After, a sensor node becomes fault, coverage loss caused covered by its neighbors moving to failure node. Major problem elected neighbor node for movement. The priority of neighbor nodes for movement and coverage missing regions determines overlapping sensing range and its distance from faulty node. In this paper priority of neighbors determine by two methods, in first method priority determines by an equation. It is included overlapping and distance but in second method priority of neighbors obtained by a fuzzy system with inputs overlapping and distance, and its output priority of neighbors. The target of proposed methods is decrement redundant sensors for replacement faulty sensors in network sensor. The propose methods compared with themselves and previous methods which used only from redundant sensors.
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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 | 0.000 |
| 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".