Coverage-Based Sensor Association Rules for Wireless Vehicular Ad Hoc and Sensor Networks
Bibliographic record
Abstract
Recently, Knowledge Discovery Process has proven to be a promising tool for extracting behavioral patterns regarding sensor nodes from wireless vehicular ad hoc and sensor networks. In this paper, we propose a new type of behavioral patterns, which we refer to as Coverage-based Rules, to discovers the correlation among the set of locations monitored by the network. Coverage- base Rules is an extension for a recent proposed behavioral patterns named as Sensor Association Rules. However, in contrast to Sensor Association Rules, Coverage-based Rules have been designed specifically for sensor networks that guarantee a k- coverage property for the area under monitoring. The major application of Coverage-based Rules is to predict the location of future events. This feature might prove to be quite useful in vehicular ad hoc and sensor network based applications. To report about the efficiency of our proposed scheme, an extensive set of simulation experiments have been conducted to compare the performance of the network during the data preparation process for Coverage-based and Sensor Association Rules schemes.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".