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Record W2001106000 · doi:10.1109/iwcmc.2013.6583592

Reliable and energy efficient cooperative relaying scheme (REECR) in wireless sensor networks

2013· article· en· W2001106000 on OpenAlexaff
Wenying Zheng, Kazi Atiqur Rahman, Kemal Tepe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Windsor
FundersMedical Research Council
KeywordsRetransmissionComputer networkComputer scienceNetwork packetFadingRelayWireless sensor networkAutomatic repeat requestEnergy consumptionCooperative diversityNode (physics)Hybrid automatic repeat requestTransmission (telecommunications)Efficient energy useTransmitterReliability (semiconductor)Power controlChannel (broadcasting)Power (physics)EngineeringTelecommunicationsTelecommunications linkElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, a reliable and energy efficient cooperative relaying (REECR) scheme was proposed for multi-hop communications in wireless sensor networks (WSNs). The scheme investigates node cooperation in conjunction with power control to conserve transmission energy and improve communication reliability in the presence of fading. By using a set of relay nodes that are located between transmitter and receiver to assist packet delivery, the scheme is capable of reaping both spatial diversity gains and path-loss savings. Local channel information is utilized in the relay selection process and the power control to reduce the energy consumption of data packet transmission. Moreover, a cooperative automatic repeat request (ARQ) is incorporated, whereby an intermediate node is chosen to help packet retransmission. Our simulations and analyses show that the proposed scheme improves energy efficiency, packet delivery ratio and extends nodes' battery lifetime.

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: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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
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
Published2013
Admission routes1
Has abstractyes

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