A new in-network data reduction mechanism to gather data for mining wireless sensor networks
Bibliographic record
Abstract
Recently, association rules for sensors have received a great deal of attention due to their importance in capturing the temporal relations between sensor nodes in wireless sensor networks (WSNs). Because of this capability, these rules can be used to improve the Quality of Service (QoS) of wireless sensor networks by participating in the resource management process. To mine sensor association rules, behavioral data that describes the sensors' activities over time must be extracted and accumulated at the central node (the Sink) for further analysis. Given the limited resources of sensor nodes, a well designed data gathering algorithm is required for gathering the behavioral data efficiently. In this paper, an in-network data reduction technique is proposed to reduce the amount of data that needs to be routed to the Sink by exploiting the redundancy between sensors' activities. The in-network reduction technique will be implemented on top of a data gathering tree that we refer to as the Minimum Nodes Data Gathering Tree (MNDGT), which consists of the nodes that will participate in formulating the sensor rules and those that are necessary for maintaining the minimum distance to the Sink. To report on the performance of the reduction mechanism, a comparison analysis with other two gathering schemas is introduced. Indeed, the results show that the in-network reduction technique is able to reduce the number of messages by a factor ranging from 10% to 70% compared to the other techniques.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".