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Record W2063973888 · doi:10.5555/644108.644237

Dynamic construction of Bluetooth scatternets of fixed degree and low diameter

2003· article· en· W2063973888 on OpenAlex

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
KeywordsScatternetBluetoothComputer scienceComputer networkWireless ad hoc networkScalabilityRouting (electronic design automation)Distributed computingPiconetSimple (philosophy)Topology (electrical circuits)Network topologyWirelessTelecommunicationsMathematicsOperating system

Abstract

fetched live from OpenAlex

Bluetooth is a promising recent radio technology for ad hoc networking. Bluetooth networks are based on connecting together piconets, to form a scatternet. The structure of the scatternet, and the way the scatternet is built and maintained, are not part of the Bluetooth specifications, but have a tremendous impact on the performance of the network. We present an efficient distributed algorithm for Bluetooth scatternet construction. The resulting scatternet is scalable and our construction is dynamic in the sense that nodes can join and leave the network at their convenience. For fixed constant degree of nodes, the resulting diameter is polylogarithmic in the size of the network, and the connectivity of the masters is high. We also give a routing protocol adapted to the specific scatternet topology returned by our algorithm. This protocol does not require complicated path-discovery methods, but is based on a simple virtual labeling of the devices participating in the scatternet.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.222
Teacher spread0.207 · 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