Forming MS-Free and Outdegree-Limited Bluetooth Scatternets in Pessimistic Environments
Bibliographic record
Abstract
This paper introduces two distributed Bluetooth scatternet formation (BSF) algorithms, called BSFWAVVY(MSF) and BSFWAVVY(ODL). The first algorithm forms scatternets that does not contain master-slave (MS) bridges (MS-free scatternets), whereas the second algorithm forms scatternets in which each piconet has at most k slaves (outdegree-limited scatternets). The motivation is that MS-freeness and outdegree limitation are the two properties that significantly improve the quality of the scatternets. However, and contrary to the existing BSF algorithms, our algorithms consider these properties under pessimistic environments modeled as arbitrary networks (i.e., no assumptions are made on the underlying network topology). We give two lower bounds that prove the asymptotic optimality of our algorithms with respect to time complexity and message complexity. We also show that the problem of forming MS-free and outdegreelimited scatternets at the same time is NP-COMPLETE. We introduce a time-efficient implementation of BSFWAVVY(MSF) and BSFWAVVY(ODL) that exploits unique characteristics of Bluetooth networks. Simulation experiments show that our algorithms have short execution time relative to major BSF algorithms and it outperforms other major algorithms with respect to various performance metrics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".