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Record W2072190413 · doi:10.1016/j.procs.2014.07.012

IDDR: Improved Density Controlled Divide-and-Rule Scheme for Energy Efficient Routing in Wireless Sensor Networks

2014· article· en· W2072190413 on OpenAlexaff
Farrukh Saleem, Y. Moeen, Muzammil Behzad, Md. Amir Hasnat, Zahoor Ali Khan, U. Qasim, Nadeem Javaid

Bibliographic record

VenueProcedia Computer Science · 2014
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of AlbertaDalhousie University
Fundersnot available
KeywordsComputer scienceEnergy consumptionWireless sensor networkEnergy (signal processing)Computer networkRouting protocolScheme (mathematics)Protocol (science)Routing (electronic design automation)ResidualSelection (genetic algorithm)Distributed computingAlgorithmElectrical engineeringArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

In Wireless Sensor Networks (WSNs) unbalanced energy consumption is a major problem. As a result, energy hole is created and network lifetime is reduced. In this paper, we propose IDDR to avoid the energy hole creation through uniform energy consumption. Proposed scheme reduces coverage and energy hole by dividing the network into small segments with static number of Cluster Heads (CHs) in each round. Selection of CH in each segment is based on maximum residual energy. Simulation results prove that proposed protocol outperforms the compared 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.

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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.206
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

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
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

Citations6
Published2014
Admission routes1
Has abstractyes

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