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Record W1979533605 · doi:10.1109/bwcca.2012.12

DominoMAC: A Wireless Sensor Networks Medium Access Protocol

2012· article· en· W1979533605 on OpenAlexaff
Fayez Gebali, Rami Sulaimani, Haytham Elmiligi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer networkComputer scienceWireless sensor networkMultiple Access with Collision Avoidance for WirelessAccess controlProtocol (science)Key distribution in wireless sensor networksMobile wireless sensor networkWireless distribution systemWireless LAN controllerMedia access controlWireless networkDistributed computingWirelessTelecommunications

Abstract

fetched live from OpenAlex

Wireless sensor networks have unique characteristics and restricted energy efficiency performance requirements that dictate special medium access control (MAC) protocols. In this work we show that design of any MAC should mainly consider the proximal nodes near the sink since they carry the most traffic while being the most crucial for survival of the network. We provide a comprehensive analysis of sensor network traffic and its impact on the design of MAC protocols. Network performance is modelled using analysis and verified using numerical simulations. The MAC protocol we propose here has several attractive features: viz. is collision-free, allows smooth degradation of network due to battery discharge, has short active period to prolong network operational time.

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)0.003
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
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 head0.023
GPT teacher head0.291
Teacher spread0.268 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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