A framework for ontology provisioning in Virtualized Wireless Sensor Networks
Bibliographic record
Abstract
Virtualization in Wireless Sensor Networks (WSN) allows an efficient resource usage through the sharing of the same WSN physical infrastructure by multiple applications. Semantic applications are gaining more and more momentum. However, provisioning them in Virtualized WSNs (vWSNs) remains a big challenge; the data collected by the virtual sensors needs to be annotated in-network and for this annotation, an ontology needs to be provisioned, i.e. developed, deployed and managed. This paper proposes a framework for ontology provisioning in vWSNs. The framework comprises of an ontology provisioning center, an ontology enabled vWSN and an ontology provisioning protocol that enables the interactions between the provisioning center and the ontology enabled vWSN. To the best of our knowledge this is the first effort to provide such support in vWSNs. We have built a prototype to evaluate the performance of the framework and also present simulation results of the ontology provisioning protocol.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".