Bloom Filter Supporting Distributed Policy-Based Management in Wireless Sensor Networks
Bibliographic record
Abstract
Wireless Sensor Networks (WSN) are particularly special due to many characteristics, such as a working environment that makes maintenance and support a challenge; and hardware resources, particularly memory, processing and battery power, that make it capable of handling only limited software. Consequently, the administration of WSN is becoming a challenge. To overcome these limitations we proposed Distributed Policy-Based Management (DBPM) framework. Our proposed framework is expected to conceal the complexity of administrating policies operations from the users by simplifying the deployment processes. Bloom Filter is an elegant data structure that answers the membership inquiry with no false negative and manageable false positive. In this paper we propose utilizing Bloom Filter in Distributed Policy-Based Management (DPBM) environment in WSN to confirm the existence of any policy within the WSN, which will reduce the traffic within the network as well as preserve the sensor node energy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".