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Record W570564009 · doi:10.1016/j.procs.2015.05.042

ARCUN: Analytical Approach towards Reliability with Cooperation for Underwater WSNs

2015· article· en· W570564009 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcedia Computer Science · 2015
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceComputer networkRouting protocolReliability (semiconductor)Wireless sensor networkNetwork packetThroughputTransmission (telecommunications)Channel (broadcasting)Routing (electronic design automation)Underwater acoustic communicationUnderwaterDistributed computingWirelessTelecommunicationsPower (physics)

Abstract

fetched live from OpenAlex

Cooperative routing is a hybrid approach utilizing routing techniques and cooperative communication to improve the communication quality of single-antenna sensor nodes. It exploits the broadcast nature of wireless medium and transmits cooperatively using nearby sensor nodes as relays. In this research, a cooperative transmission scheme is proposed for UnderWater Sensor Networks (UWSNs) to improve the network performance called ARCUN. The protocol is an energy-efficient and high-throughput routing scheme for UWSN. Potential relays are selected from a group of neighbor nodes that utilize signal-to-noise ratio and distance computation of the underwater channel. Optimal role of cooperation provides load balancing in the network and gives profound improvement in network stability period and packet delivery ratio.

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.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.786
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.043
GPT teacher head0.249
Teacher spread0.206 · 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