CHRONOLOGICAL TREE — A COMPRESSED STRUCTURE FOR MINING BEHAVIORAL PATTERNS FROM WIRELESS SENSOR NETWORKS
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) have proven their success in a variety of applications for monitoring physical and critical environments. However, the streaming nature, limited resources, and the unreliability of wireless communication are among the factors that affect the Quality of Service (QoS) of WSNs. In this paper, we propose a data mining technique to extract behavioral patterns about the sensor nodes during their operation. The behavioral patterns, which we refer to as Chronological Patterns, can be thought of as tutorials that teach about the set of sensors that report on events within a defined time interval and the order in which the events were detected. Chronological Patterns can serve as a helpful tool for predicting behaviors in order to enhance the performance of the WSN and thus improve the overall QoS. The proposed technique consists of: a formal definition of the Chronological Patterns and a new representation structure, which we refer to as Chlorotical Tree (CT), that facilities the mining of these patterns. To report about the performance of the CT, several experiments have been conducted to evaluate the CT using different density factors.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".