Towards better understanding of the behaviour of Bluetooth networks distributed algorithms
Why this work is in the frame
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Bibliographic record
Abstract
The use of frequency hopping spread spectrum in Bluetooth significantly differentiates its networks from classical radio networks. In order to observe such differences, we studied basic algorithms, in particular neighbour discovery and message exchange algorithms. Some of the major differences are found in the procedures of device discovery and link establishment, which are studied in this paper. We focus on their impact on Bluetooth networks' distributed algorithms. We show through detailed simulation experiments that minor modifications to the Bluetooth specifications or their implementation may significantly affect the performance of well-known neighbour discovery algorithms. We then study the impact of the procedures of link establishment with the purpose of finding time-efficient implementations of communication rounds for Bluetooth networks. We study OrderedExchange and RandomExchange as both algorithms implement communication rounds in Bluetooth, but use the PAGE and PAGE SCAN states differently. Theoretical analysis shows that RandomExchange has a better time complexity, while simulation experiments show that OrderedExchange significantly outperforms RandomExchange in networks with a practical size (110 nodes and less). We use the previous results to improve the time efficiency of Bluetooth scatternet formation algorithms through the introduction of the time-efficient algorithm OrderedExchangeCMIS. We believe that the study of some other basic algorithms (such as broadcasting, spanningtree and election) will lead to a better understanding of Bluetooth networks, and as a consequence, to more efficient algorithms that fully leverage the strength of this type of network.
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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 | 0.000 |
| 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