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Record W4236846606 · doi:10.32920/ryerson.14652228

Bluehoc-based simulation study of user data throughput in Bluetooth-enabled devices

2021· preprint· en· W4236846606 on OpenAlexaff
Syed Rahat

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicBluetooth and Wireless Communication Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBluetoothComputer scienceThroughputKey (lock)WirelessRobustness (evolution)Computer networkReal-time computingTelecommunicationsComputer security

Abstract

fetched live from OpenAlex

Bluetooth technology aims at allowing short-range communication between portable and/or fixed devices. It uses short-range radio links to replace cables between Bluetooth-enabled devices. In this way, it is similar in purpose to the Infrared Data Association (IrDA), however, Bluetooth is a radio frequency (RF) technology utilizing the unlicensed 2.5 GHz industrial, scientific, and medical (ISM) band. The key features of Bluetooth technology are robustness, low power and low cost with its primary market for data and voice transfer between communication devices and PCs. In this project, a simulation study is done with three major goals in mind: (i) to gather expertise on and evaluate a Bluetooth simulation tool called Bluehoc for further use, (ii) to gather measurements of some Bluetooth characteristics such as throughput in post connection state and (ii) to describe a model that can be used to get maximum throughput for voice and data applications. We also review some of the key aspects in Bluetooth simulation and present models of the Bluetooth devices to get maximum throughput. We show that user data transfer rate (throughput) between Bluetooth master and slaves is effected by distance, number of slaves and slave's start time.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0090.014
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.109
GPT teacher head0.346
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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