Agent-based Fault Detection Mechanism in Wireless Sensor Networks
Bibliographic record
Abstract
Recently, agent-based approaches are considered as an appropriate solution to address the issue of the overwhelming data traffic in wireless sensor networks (WSNs). Theoretically, these approaches would eliminate the redundancy and achieve substantial energy gain. However, in practice, the reliability of sensor devices has been recognized as one of the crucial issues in wireless sensor networks. Using agents at the sensor devices may provide more efficient energy consumption. But, micro-sensors are subject to high-frequency faults in distributed environments. Towards this end, we propose agent-based system architecture with fault-detection inference engine based on reverse multicast tree to evaluate sensor nodes' fault probabilities. Due to the characteristics of wireless sensor networks (energy awareness, constraint bandwidth and so on); it is infeasible to require each sensor to announce its working state to a centralized terminal node. Therefore, we formulate the agent's inference engine as nondeterministic finite accepter and adopt iterative computation to infer the fault probability of nodes in reverse multicast tree.
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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) | 0.000 | 0.000 |
| 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 | 0.002 | 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".