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Record W2940945556 · doi:10.5555/3324320.3324391

Competition: RedNodeBus, Stretching out the Preamble

2019· article· en· W2940945556 on OpenAlexaff
Antonio Escobar-Molero, Javier Garcia-Jimenez, Jirka Klaue, Fernando Moreno-Cruz, Saez Borja, Francisco J. Cruz, Unai Ruiz, Angel Corona

Bibliographic record

VenueInternational Conference on Embedded Wireless Systems and Networks · 2019
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsLatency (audio)Computer scienceNetwork packetComputer networkPreambleFlooding (psychology)Wireless sensor networkReliability (semiconductor)WirelessEnergy consumptionNetwork topologyLow latency (capital markets)Synchronization (alternating current)Real-time computingTelecommunicationsChannel (broadcasting)EngineeringPower (physics)

Abstract

fetched live from OpenAlex

A real-time wireless bus based on flooding and the capture effect is proposed to achieve highly reliable broadcast communication in a Wireless Sensor Network (WSN) working in harsh environments, in a multi-source-to-multisink topology, where multiple hops are required (Fig.). Different sources access the medium without colliding using network-wide predefined time slots and frequency channels. Frequency-, spatialand time-diversities are exploited using redundant retransmissions. Packet deliveries are latency-bounded, and messages are discarded after a predetermined time-to-live; in order to achieve an optimal trade-off between reliability, energy consumption and latency. Furthermore, long packet preambles are used to ease the synchronization requirements and favor the capture effect.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0350.016

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 head0.021
GPT teacher head0.251
Teacher spread0.230 · 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 designBench or experimental
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

Citations4
Published2019
Admission routes1
Has abstractyes

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Same venueInternational Conference on Embedded Wireless Systems and NetworksSame topicEnergy Efficient Wireless Sensor NetworksFrench-language works237,207