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Record W2084603600 · doi:10.1049/iet-cta.2013.0859

Asynchronous consensus‐based time synchronisation in wireless sensor networks using unreliable communication links

2014· article· en· W2084603600 on OpenAlexafffund
Salman Ahmed, Feng Xiao, Tongwen Chen

Bibliographic record

VenueIET Control Theory and Applications · 2014
Typearticle
Languageen
FieldComputer Science
TopicNetwork Time Synchronization Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAsynchronous communicationComputer scienceWireless sensor networkWirelessTime synchronizationDistributed computingConsensusComputer networkKey distribution in wireless sensor networksReal-time computingWireless networkMulti-agent systemTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Time synchronisation is required for a variety of distributed control applications in wireless sensor networks (WSNs). In this study, a consensus‐based time synchronisation protocol is presented. The proposed protocol considers asynchronous framework where the sensor nodes can have different time‐periods, starting times and input update times. The communication links are assumed to be unreliable. The clocks in a WSN are modelled by a time‐varying system with time‐delay terms. The objective is to design local control inputs to achieve time synchronisation by processing the information received from available neighbour nodes. By employing tools from non‐negative matrix and graph theories, the convergence analysis is presented. Numerical examples are given to demonstrate the effectiveness of the proposed protocol.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.218
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2014
Admission routes2
Has abstractyes

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