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Record W2766007449 · doi:10.1109/sarnof.2017.8080385

Evaluation of ultra-wideband radio for industrial wireless control

2017· article· en· W2766007449 on OpenAlexaff
Daniel M. King, Bradford G. Nickerson, Wei Song

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsComputer networkWirelessWireless sensor networkPhysical layerTransceiverComputer scienceProtocol stackNetwork packetReliability (semiconductor)Ultra-widebandWireless networkElectronic engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper investigates the use of ultra-wideband (UWB) radio for communication in industrial wireless sensor networks (WSNs). OpenWSN is an open-source IPv6 mesh network protocol stack based on IEEE 802.15.4 time slotted channel hopping (TSCH). We adapted OpenWSN to operate an UWB physical layer based on the DecaWave DW1000 UWB transceiver. Experiments were conducted in an industrial steam heating plant environment and an office/laboratory environment to measure the bit error ratio (BER) performance of UWB. The performance of an OpenWSN network using the UWB physical layer was experimentally compared against WirelessHART, an existing industrial wireless automation standard. We found that UWB is a feasible medium for industrial wireless communication in both environments. Experimental results indicate that OpenWSN over UWB achieved better reliability in guaranteed time slots than WirelessHART in an office environment, while WirelessHART lost fewer packets than OpenWSN over UWB.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.099
GPT teacher head0.334
Teacher spread0.235 · 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
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

Citations7
Published2017
Admission routes1
Has abstractyes

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