In-Network Data Reduction and Coverage-Based Mechanisms for Generating Association Rules in Wireless Sensor Networks
Bibliographic record
Abstract
Sensor association rules, which are a kind of behavioral pattern that aims to capture the temporal relations between sensor nodes, has proven to be a promising tool for improving wireless sensor network (WSN) performance and its quality of service (QoS) by participating in the resource management process and by compensating for the undesired effects of wireless communication. To prepare the data needed for generating sensor association rules, each sensor node should monitor its activity over time and inform the sink about the time in which events are detected. However, without an efficient extraction mechanism, this process is costly, giving the limited resources of sensor nodes. In this paper, we propose an in-network data reduction mechanism to reduce the amount of data (about sensors' behaviors) by removing some of the data's redundancies. In addition, we propose a relaxed version of sensor association rules that emphasizes the correlation between a set of locations (areas) rather than individual sensor nodes. We refer to the new rules proposed by coverage-based sensor association rules.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".