Dominating set based bluetooth scatternet formation with localized maintenance
Bibliographic record
Abstract
This paper addresses the problem of scatternet formation and maintenance for multi-hop Bluetooth based personal area and ad hoc networks with minimal communication overhead. Each node is assumed to know its position and position of all its neighbours. The proposed formation algorithms have three phases. In the first phase the unit graph is constructed (each node establishes connection with all its neighbors that are located within its transmission radius, which is equal for all nodes), and, if planar structure is desirable, localized sparse subgraph (such as relative neighbourhood or Gabriel graph) is extracted. In the second phase, the degree of each node is limited to 7 by applying Yao subgraph construct simultaneously on all nodes with excessive degree, followed by either elimination of directed edges or the application of reverse Yao construct. In the last phase, master-slave relations are created by applying higher degree priority (with dominating set membership as the primary key). The creation and maintenance requires minimal overhead in addition to maintaining accurate location information for one-hop neighbours. The proposed schemes have localized maintenance property (scatternet maintenance due to movement or activity change of a single node is limited to the locality of that node), which is not the case with the existing clustering based Bluetooth scatternet formation schemes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".