An Efficient Data Extraction Mechanism for Mining Association Rules from Wireless Sensor Networks
Bibliographic record
Abstract
With the advances of wireless sensor networks and their ability to generate a large amount of data, data mining techniques to extract useful knowledge regarding the underlying network have recently received a great deal of attention. However, the stream nature of the data, the limited resources, and the distributed nature of sensor networks bring new challenges for the mining techniques that need to be address. In this paper, we introduce a new formulation for the association rules, a well known data mining technique, that is able to generate the time relations between sensor devices in a particular sensor network. This new formulation will allow traditional data mining algorithms proposed to solve the classical association rules mining problem to be applied on sensor based class of applications that generate and use sensor data. The generated rules will give a clear picture about the correlations between sensors in the network and can be used to make decisions about the network performance, or it can be used to predict the sources of future events. In order to prepare for the data needed in the mining process and to maximize the network lifetime, a distributed extraction methodology is introduced, in this distributed methodology sensors perform optimization based on local computation to decide whether it will participate in sending data or not. Experimental results have shown that the distributed extraction solution is able to reduce the number of exchanged messages and the data size by 50% compared to a direct transmission of the data.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".