JUMP mode---a dynamic window-based scheduling framework for Bluetooth scatternets
Bibliographic record
Abstract
The emerging Bluetooth technology enables devices to be wirelessly connected in an ad hoc fashion. Using Bluetooth, devices are organized into small piconets, which in turn may be inter-connected to form larger networks called scatternets. In a scatternet, some of the devices participate in more than one piconet. These nodes are called PMP (Participant in Multiple Piconets) nodes. Since a Bluetooth unit only hence one transceiver, it may only be active in one piconet at any given instant and hance a PMP node must schedule its time between piconets on a time-division basis. The availability of PMP nodes represents an important performance constraint when building scatternets and has to be effectively coordinated between the different piconets. To allow flexible and efficient scatternet operation and to overcome the shortcomings of the current Bludetooth modes, we proposed a new mode---JUMP mode. This mode includes a set of communication rules that enable efficient scatternet operation by offering a great deal of flexibility for a node to adapt its activity in different piconets to the traffic conditions. Using JUMP mode a PMP node divides the time into timewindows and then signals about which piconet to be present in for each of these time windows. The time windows are of pseudo random length to eliminate systematic collisions and thereby avoid starvation and live-lock problems without any need for scatternet-wide may coordination. Besides enabling scatternet operation, JUMP mode also enhance other aspects of Bluetooth , such as low-power operation
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".