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Record W4401387076 · doi:10.1109/jsen.2024.3437211

CPR: A Confined Percolation Routing for Distributed Underwater Acoustic Networks

2024· article· en· W4401387076 on OpenAlexaff
Yuan Liu, Lin Cai, F. Wang, Haiyan Wang, Junhao Hu

Bibliographic record

VenueIEEE Sensors Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsPercolation (cognitive psychology)UnderwaterRouting (electronic design automation)Computer scienceAcoustic sensorComputer networkAcousticsGeologyPhysicsOceanography

Abstract

fetched live from OpenAlex

A routing protocol is essential for multihop relay to deliver data and reduce energy consumption in underwater acoustic networks (UANs). However, the low connectivity and high transmission power requirement of underwater acoustic channels pose critical challenges to network connectivity and the lifetime. In this article, we present a confined percolation routing (CPR) protocol that enhances connectivity and energy efficiency in UANs by exploring multiple paths to meet reliability requirements and optimizing transmission power and retransmission counts to reduce energy consumption. The analysis models designed assess the impact of activated node combinations and link reliability on end-to-end (ETE) reliability and energy use. The proposed protocol has been tested in various scenarios, and the results show that the proposed protocol improved the average ETE reliability by 17%, 21%, 7%, 12%, and 44% compared with the benchmark GEDAR, EEGNBR, Dflooding, Multi-SPR, and LEACH protocols in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ {5} \times 5$ </tex-math></inline-formula> network, while consuming lower energy.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.493

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 communication0.0000.000
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 head0.020
GPT teacher head0.242
Teacher spread0.222 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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