Wireless Sensor Networks Fault Detection and Identification
Bibliographic record
Abstract
We have developed and experimentally tested a set of models for the detection and identification of sensor faults that commonly occur in wireless sensor networks. Considered faults include outlier, spike, variance, high-frequency noise, offset, gain, and drift faults. These faults affect the system operations and can endanger operators, final users, and the general public. The fault detection models are divided into two classes: data-centric models, which only analyze a single data stream, and system-centric models, which consider the overall system. For data-centric models, we use the magnitude, the gradient, and the variance of raw sensor data to model faults. For system-centric models, we introduce variogram-based techniques that allow faults to be detected by comparing readings from multiple sensors that measure related phenomena. For data-centric and system-centric sensor fault detection, we show how a few model parameters affect the sensitivity of wireless sensor network fault models. We present simulation and experimental results that illustrate the fault detection and identification models. The system is intended for health monitoring applications of the NASA Stennis Space Center (SSC) test stands and widely distributed support systems, including pressurized gas lines, propellant delivery systems, and water coolant lines. The testbed consists of Coremicro® reconfigurable embedded smart sensor nodes [29] capable of wireless communication, a network-capable application processor, a wireless base station, the software that supports sensor and actuator health monitoring, a database server, and a smartphone running a health monitoring Android application.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".