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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 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score0.854

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 communication0.0010.000
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 head0.021
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 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

Citations4
Published2019
Admission routes1
Has abstractyes

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