Change Detection for Large Distributed Sensor Networks With Multitriggered Local Sensors
Bibliographic record
Abstract
The emergence of large sensor networks and the Internet of Things has reinvigorated interest into distributed quickest change detection. Important shortcomings of existing approaches are ease of design, flexibility in communication, and applicability to larger networks. The new approach proposed in this work features local sensors that can be triggered multiple times, i.e., can reset and continue monitoring after transmitting their decisions. With larger sensor networks as a focus, the system allows for multiple simultaneous transmissions to a fusion center within bandwidth limitations. The proposed system uses the cumulative-sum procedure at local sensors to binarize local decisions, which are then transmitted to the fusion center that also employs cumulative-sum quickest detection. Test overdesign due to sequential test overshoot is avoided, and global and local thresholds are chosen to meet a desired false alarm rate constraint using numerical computation of expected delay performance. The system compares favourably to several existing methods while offering greater flexibility in the amount of fusion center communication.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".