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Record W3095418416 · doi:10.1109/icsme46990.2020.00093

Exploring Bluetooth Communication Protocols in Internet-of-Things Software Development

2020· article· en· W3095418416 on OpenAlex
Tri Minh Triet Pham, Jinqiu Yang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBluetooth and Wireless Communication Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsFirmwareBluetoothComputer scienceInternet of ThingsSoftwareThe InternetSoftware developmentSoftware engineeringWorld Wide WebWirelessOperating system

Abstract

fetched live from OpenAlex

Internet of Things (IoT) development heavily depends on the connectivity of real-world objects. Bluetooth technology is widely applied to such connectivity in many IoT domains, such as smart home systems. Developing an integrated IoT system involves various stakeholders, e.g., mobile app developers and firmware developers. Discrepancies on the connectivity of the devices, i.e., how to communicate, may occur between different stakeholders in IoT development. Discrepancies occur when one group of developers misunderstand the communication protocols or incorrectly implemented them in the code. Such discrepancies may lead to unmet requirements and runtime connection errors. To help reduce such discrepancies, we perform a study to understand the current practices of designing Bluetooth communication protocols (BCPs) (i.e., by firmware developers) and how software developers manage the diverse BCPs in the code. Such understanding is a first step to provide tool support that can help developers better manage BCPs and detect (fault-indicating) discrepancies, aiding the maintenance effort of mobile applications.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.892
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.000
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 head0.193
GPT teacher head0.285
Teacher spread0.092 · 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