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Record W1964578722 · doi:10.1016/j.procs.2013.06.047

Divide-and-Rule Scheme for Energy Efficient Routing in Wireless Sensor Networks

2013· article· en· W1964578722 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcedia Computer Science · 2013
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceWireless Routing ProtocolRouting protocolComputer networkWireless sensor networkCluster analysisDynamic Source RoutingZone Routing ProtocolDistributed computingProbabilistic logicEnhanced Interior Gateway Routing ProtocolRouting (electronic design automation)Link-state routing protocolArtificial intelligence

Abstract

fetched live from OpenAlex

From energy conservation perspective in Wireless Sensor Networks (WSNs), clustering of sensor nodes is a challenging task. Clustering technique in routing protocols play a key role to prolong the stability period and lifetime of the network. In this paper, we propose and evaluate a new routing protocol for WSNs. Our protocol; Divide-and-Rule (DR) is based upon static clustering and dynamic Cluster Head (CH) selection technique. This technique selects fixed number of CHs in each round instead of probabilistic selection of CH. Simulation results show that DR protocol outperform its counterpart routing protocols.

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.

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 score0.594
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
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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