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Record W2081881677 · doi:10.1109/icc.2013.6654732

An energy efficient MAC protocol for fully-connected wireless networks

2013· article· en· W2081881677 on OpenAlexaff
Kamal Rahimi Malekshan, Weihua Zhuang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Waterloo
FundersCisco Systems
KeywordsComputer scienceComputer networkEnergy consumptionThroughputWirelessWireless Application ProtocolTransmission (telecommunications)Node (physics)Protocol (science)Efficient energy useWireless networkReal-time computingTelecommunicationsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Power consumption is an important consideration in deploying wireless networks, especially for battery-powered mobile devices such as smartphones. Radio interface is the main source of energy consumption in mobile devices. Medium access control (MAC) directly controls the radio interface operation; therefore, an efficient MAC protocol can significantly decrease the energy consumption. This paper presents a new energy efficient MAC scheme for fully connected wireless networks. Time is divided into beacon intervals and a temporary coordinator node manages transmission in each beacon interval. The proposed scheme reduces energy consumption by putting radio interfaces in the sleep state periodically and reducing transmission collision. Simulation results show that the proposed scheme has lower power consumption, higher throughput and lower delay than existing power saving MAC protocols.

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)0.004
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.278
Teacher spread0.263 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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