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Record W2064574113 · doi:10.1145/1143549.1143720

IEEE 802.15.3 intra-piconet route optimization with application awareness and multi-rate carriers

2006· article· en· W2064574113 on OpenAlexaff
Zhanping Yin, Victor C. M. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of British Columbia
FundersAssociation for Applied Sport Psychology
KeywordsPiconetDEVSComputer scienceComputer networkPHYWirelessPhysical layerReal-time computingDistributed computingBluetoothSimulationTelecommunicationsModeling and simulation

Abstract

fetched live from OpenAlex

In IEEE 802.15.3 wireless personal area networks, all devices (DEVs) within a piconet communicate in a peer-to-peer manner. In this paper, we enhance the performance of intra-piconet communications by taking advantage of the multi-rate physical layer (PHY) and diverse application traffic characteristics. As higher PHY data rates are typically achievable over shorter transmission distances, a low rate link between two DEVs can potentially be replaced by a higher rate multihop connection through intermediate DEVs in the same piconet. A novel application-aware shortest path (AASP) algorithm is proposed for centralized intra-piconet route optimization, which finds the route that requires the minimum overall channel time allocation (CTA) based on the traffic parameters. Performance evaluations show that the effective CTA rates vary dramatically for different applications, and can be greatly increased by simply delaying the acknowledgments. Especially for low rate links and between unreachable DEV pairs, the AASP algorithm yields very high optimization ratios, which increase with frame payload size and the density of DEVs in the piconet.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.738
Threshold uncertainty score0.415

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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2006
Admission routes1
Has abstractyes

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