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Record W2169421143 · doi:10.1109/wimob.2006.1696383

PBRCE: Energy Efficient MAC Protocol for Wireless Ad Hoc Networks

2006· article· en· W2169421143 on OpenAlexaff
Tiantong You, Hossam S. Hassanein, Chi‐Hsiang Yeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceMultiple Access with Collision Avoidance for WirelessComputer networkRetransmissionDistributed coordination functionEfficient energy useThroughputNetwork packetHidden node problemWireless ad hoc networkMedia access controlTransmission (telecommunications)Network allocation vectorAccess controlWirelessIEEE 802.11Wireless networkOptimized Link State Routing ProtocolTelecommunicationsWi-Fi arrayRouting protocolEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The currently most popular medium access control (MAC) protocol, namely IEEE 802.11 distributed coordination function (DCF), is not energy efficient. In this paper we present the principles of achieving energy-efficiency in MAC protocol design for WLANs. Along these principles, we propose a novel dual-channel MAC protocol, called power-control binary-countdown-carrier-sense/request-to-send (RTS)/clear-to-send (CTS)/ensure-to-send (ETS) (PBRCE). PBRCE aims not only at energy-efficiency, but also at higher network throughput. The enhanced network performance is achieved by easing the "exposed terminal" and "hidden terminal" problems. The energy efficiency is achieved by reducing the collision rate-thus saving energy through avoiding retransmission of the same packets-and by controlling the signal transmission power and going to sleep mode to avoid the unnecessary passive listening

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)0.002
metaresearch head score (Gemma)0.003
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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.004

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 head0.013
GPT teacher head0.268
Teacher spread0.255 · 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
GenreMethods

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
Published2006
Admission routes1
Has abstractyes

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