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Record W2950858926 · doi:10.23977/isspj.2019.41001

Research on WSN Topology and Protocol for Transmission Lines Monitoring

2019· article· en· W2950858926 on OpenAlexvenueno aff
Baoyi Wang, Sen Jing, Shaomin Zhang

Bibliographic record

VenueInformation Systems and Signal Processing Journal · 2019
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsnot available
Fundersnot available
KeywordsComputer networkRouting protocolNode (physics)Computer scienceZone Routing ProtocolWireless sensor networkEnergy consumptionTransmission (telecommunications)Path vector protocolWireless Routing ProtocolRouting (electronic design automation)Real-time computingEngineeringTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

The battery of the wireless sensor node carries less energy, and its energy-saving requirements are relatively high when the transmission line is deployed. Based on the hierarchical routing protocol, a kind of new communication routing protocol is proposed. Reduce the number of long chains generated by adjacent contacts by introducing distance thresholds. On the selection of the cluster head node, the residual energy of the candidate cluster head node and the distance factor between the node and the base station are fully considered, which improves the data transmission efficiency and node lifetime. Simulation results show that compared with the existing LEACH communication routing protocol, the proposed method reduces the energy consumption of the node and prolongs the survival time of the node, which is suitable for the application of the transmission line monitoring system.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.058
GPT teacher head0.362
Teacher spread0.304 · 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
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

Citations1
Published2019
Admission routes1
Has abstractyes

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