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Record W2116634365 · doi:10.1109/ccece.2010.5575135

Reliability of ZigBee networks under broadband electromagnetic noise interference

2010· article· en· W2116634365 on OpenAlexaff
Lee Seung Woo, Ken Ferens, Witold Kinsner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsElectromagnetic interferenceBroadbandWirelessElectromagnetic compatibilityComputer scienceNoise (video)Robustness (evolution)Interference (communication)Electronic engineeringWireless broadbandNeuRFonReliability (semiconductor)Electromagnetic noiseElectromagnetic environmentTelecommunicationsComputer networkWireless networkElectrical engineeringEngineeringWi-Fi arrayPhysics

Abstract

fetched live from OpenAlex

The goal of this paper is to determine the robustness of the ZigBee wireless networking technology under the influence of interference caused by broadband electromagnetic noise from the operating environment of sensing, monitoring, and control systems. Broadband electromagnetic noise was of interest because such electrical noise (i) does exist and is prevalent due to emissions from electrical and electronic components nearby the communication system, and (ii) can cause interference across multiple (wireless) channels. Therefore, such noise will need to be characterized and modeled to assess its effects upon the performance of a wireless system as well as for devising noise control methods. In addition to answering the research question of how will ZigBee survive under broadband electromagnetic noise, this work also provides a method to predict the minimum number of ZigBee nodes required for reliable operation within a given space. These two questions have neither been answered in the ZigBee specification nor in any existing research publications.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.521

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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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