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Record W2114787869 · doi:10.1109/wicom.2008.974

An Efficient MAC Protocol Based on Hybrid Superframe for Wireless Sensor Networks

2008· article· en· W2114787869 on OpenAlexaff
Ma Ge, Dongyu Qiu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsConcordia University
Fundersnot available
KeywordsSuperframeComputer networkComputer scienceNetwork packetWireless sensor networkEnergy consumptionProtocol (science)ThroughputIEEE 802.15ScheduleNetwork allocation vectorTime division multiple accessWirelessIEEE 802.11TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Designing low energy consumption, high efficiency media access control (MAC) protocols are one of the most important directions in wireless sensor networks (WSN). In this paper, we proposed a new contention reserve MAC protocol, named CRMAC, under the inspiration of IEEE 802.15.4's superframe structure. CRMAC is a MAC protocol suitable for intra-cluster WSN that combines the advantages of contention and schedule-based MAC protocols. We introduce the mechanism and superframe structure of CRMAC in detail and verified the performance of this protocol through simulations. Our results show that CRMAC performs better than IEEE 802.15.4 in energy consumption, system delay and network throughput. CRMAC is especially suitable for short packet transmission under low load networks, which is the main situation in WSN.

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: none
Teacher disagreement score0.819
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 studies0.0010.000
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 head0.016
GPT teacher head0.255
Teacher spread0.239 · 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

Citations4
Published2008
Admission routes1
Has abstractyes

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