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Record W2138711660 · doi:10.1109/eit.2009.5189639

A novel real-time MAC layer protocol for wireless sensor network applications

2009· article· en· W2138711660 on OpenAlexaff
Brajendra K. Singh, Kemal Tepe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer scienceComputer networkWireless sensor networkMultiple Access with Collision Avoidance for WirelessProtocol (science)WirelessWireless Application ProtocolMedia access controlLayer (electronics)Wireless distribution systemReal-time computingWireless networkKey distribution in wireless sensor networksTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a comparative study of existing real-time MAC layer protocols for wireless sensor networks. Then, a new real-Time MAC protocol is presented that is based on a general purpose MAC protocol, called S-MAC. While medium access strategy in S-MAC is based on contention and back-off schemes, protocol proposed in this paper uses feedback approach as a medium access strategy. As a result of this, it increases consistency in data transmission pattern, which enables it to guarantee end-to-end delay deadlines for soft realtime applications. Proposed protocol works in continuous ON mode of operation at MAC layer and is intended to be used for randomly deployed single stream wireless sensor applications. Finally, a comparative performance analysis of proposed realtime protocol is done with other real-time and general purpose MAC protocols for wireless sensor networks.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
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 head0.290
Teacher spread0.267 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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