Formulation of BLE Throughput Based on Node and Link Parameters
Bibliographic record
Abstract
We present a novel scheme to formulate the throughput and the average number of successfully transmitted packets in a multinode Bluetooth low-energy (BLE) platform during each connection event. In this scheme, the wireless link is considered to be error prone which leads to the existence of uncorrelated bit error. Our proposed scheme considers factors that cause a connection event to close. The effect of master’s scheduling algorithm and BLE parameters on this scheme are also discussed to establish a comprehensive study on finding the throughput of a peripheral node in a BLE network. It is clear that an analytical model to formulate the throughput of a node offers a beneficial guideline for understanding the BLE throughput and ultimately the types of applications for which BLE technology would be a suitable option. We performed extensive experiments on the BLE v4.2 platform to investigate the throughput of a BLE node in a multipacket data transmission during a connection event. The results of our experimental study show some of the deficiencies that exist in the existing BLE devices such as the limitation on the maximum allowable packets during a connection event or the design of the scheduling process used by the central node.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".