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Record W2131858722 · doi:10.1109/glocom.2009.5425620

Feedback Based Real-Time MAC (RT-MAC) Protocol for Wireless Sensor Networks

2009· article· en· W2131858722 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 networkComputer scienceNetwork packetWireless sensor networkMultiple Access with Collision Avoidance for WirelessReal-time computingMedia access controlChannel (broadcasting)Node (physics)Sink (geography)WirelessRouting protocolEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a MAC layer protocol, called RTMAC, for real-time data streaming in wireless sensor networks. RT-MAC eliminates need to contend for a wireless medium by potential transmitting nodes in a range by introducing feedback control packet, called Clear Channel (CC). RT-MAC maximizes spatial channel reuse by avoiding false blocking problem of RTS/CTS exchange based wireless MAC protocols. RT-MAC reduces contention duration for control packets to facilitate faster traveling of data packets; thus, it reduces end-to-end delay of data packet transmission. RT-MAC facilitates periodic delivery of data packets as well as fast reporting of an alarming event. This paper provides the upper bounds of end-to-end delay of data packet transmission with periodic sleep/listen schedule for RT-MAC protocol. In this paper, extensive simulation results are presented. RT-MAC supports single stream communication between a randomly selected source and sink node pair as well as multi-stream non-interfering communication among different source and sink node pairs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.178
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.268
Teacher spread0.253 · 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 teacher head, not a consensus.

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

Citations11
Published2009
Admission routes1
Has abstractyes

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