Time-efficient algorithms for the outdegree limited bluetooth scatternet formation problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present in this paper three Bluetooth Scatternet Formation (BSF) algorithms which forms deterministically connected and outdegree limited scatternets. Our algorithms improve the execution time of algorithm BlueMIS, which consists of two consecutive phases called BlueMIS I and BlueMIS II. The first phase, BlueMIS I, is a BSF algorithm that forms connected and outdegree limited scatternets in a time-efficient manner. Our algorithms improve BlueMIS by improving BlueMIS I. First, we introduce a time-efficient implementation of communication rounds, called OrderedExchange, that is more suitable for Bluetooth networks, where in a communication round each node sends and receives a message to and from all its neighbors. Using OrderedExchange, we introduce algorithms ComputeMIS I and ComputeMIS II which form the same scatternet as those formed by BlueMIS I but with less execution time. We also introduce algorithm Eliminate. Instead of letting each node u forms a maximal independent set of all its neighbors and then considers them as slaves, as in the case of BlueMIS I, Eliminate let each node u forms a maximal independent set of all its neighbors that have smaller identifiers. Eliminate forms scatternets that are similiar to those formed by BlueMIS I, and more efficient with respect to some performance metrics. Eliminate improves the execution time of ComputeMIS I and ComputeMIS II by about 60%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it