Distributed Policy-Based Management for Wireless Sensor Networks
Bibliographic record
Abstract
Due to hardware resource limitations in Wireless Sensor Network (WSN), devices on WSN may hold up to 20 policies at any given time [1]. This number may not be sufficient at all times and has a huge impact on restricting the management capabilities and tasks that can be performed on the device as well as the whole WSN. The design choice of an existing policy-based WSN platform causes the policy engine to execute policies serially [2]; therefore, when multiple policies are triggered by an event, the order of the execution is not persistent [2]. This phenomena causes instability and unpredictability in the system because it may cause different policies’ orders to be executed every time the same event is triggered. The architecture of many existing or proposed policy-based WSN platforms relies on a local policy repository on the node to access any required policy [1] [2] [3]. This architecture choice raises many issues, mainly exposing the users to serious difficulties since they have to store policies on the targeted node only, creating serious administrative difficulties. The goal of this research is to create a new framework for distributed policy-based management for WSNs to overcome the existing policy-based WSN platform limitations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".