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Record W1903041341 · doi:10.1002/wcm.2524

Adaptive low‐energy clustering in slotted beacon‐enabled IEEE 802.15.4 networks

2014· article· en· W1903041341 on OpenAlexaff
Hamidreza Tavakoli, Jelena Mišić, Majid Naderi, Vojislav B. Mišić

Bibliographic record

VenueWireless Communications and Mobile Computing · 2014
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceCluster analysisOverhead (engineering)Wireless sensor networkEfficient energy useComputer networkProbabilistic logicEnergy (signal processing)Distributed computingArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract We present, model, and evaluate a novel clustering algorithm running on top of IEEE 802.15.4 wireless sensor networks operating in slotted, beacon‐enabled mode. The adaptive low‐energy clustering algorithm provides randomized sleep and randomized rotation of the cluster‐head role so as to maximize the useful lifetime of the network by improving efficiency and balancing the lifetime of individual nodes. We model the adaptive low‐energy clustering algorithm through probabilistic analysis and show that its parameters can be tuned to extend the network lifetime and reduce the delay and energy overhead imposed by clustering. Copyright © 2014 John Wiley & Sons, Ltd.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0020.002
Research integrity0.0000.001
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 head0.012
GPT teacher head0.231
Teacher spread0.218 · 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
GenreEmpirical

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
Published2014
Admission routes1
Has abstractyes

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