A new two-phase scatternet formation algorithm for bluetooth wireless personal area networks
Bibliographic record
Abstract
A Bluetooth multi-hop personal area network can be formed by interconnecting one or more piconets into a scatternet. A Bluetooth scatternet is an ad hoc network in which the devices move randomly and organize themselves. The scatternet is attractive because it can extend the Bluetooth radio range and improve the network capacity. The current Bluetooth specification [1] only defines the scatternet but does not address how the scatternet is formed. To reduce the high load on the master nodes and bridge nodes, a twophase scatternet formation (TPSF) [8] algorithm has been proposed in which a control scatternet is created for the control traffic in the first phase and an on-demand scatternet is created for the data traffic in the second phase. In TPSF, route information for the ondemand scatternet on each node is discovered only when the node initially accesses the network. The original TPSF does not consider the support of mobility. In this thesis, we propose a new scheme which is called TPSF+ for the on-demand scatternet formation in the second phase of TPSF. In TPSF+, route information is discovered when a communication session is required between the two nodes. Consequently, the on-demand scatternet can be formed with much higher success ratio when the slaves randomly move around the master after accessing the network. We also propose to use PM_ADDR (Parked Member Address) instead of BD_ADDR (Bluetooth Device Address) during route discovery in order to reduce the time of the route discovery process. Furthermore, to reduce the hop distance of the on-demand scatternet, we limit the number of hops in each piconet in the control scatternet. Based on the simulation results, we show that our scheme can improve network performance greatly in terms of aggregate throughput and end-to-end delay even with the consideration of packet collisions. With the slaves randomly moving around the master, TPSF+ achieves much better performance in terms of a higher successful path connection ratio.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".