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Record W2103133146 · doi:10.1109/lcn.2003.1243206

New metrics for dominating set based energy efficient activity scheduling in ad hoc networks

2004· article· en· W2103133146 on OpenAlexafffund
Jamil Shaikh, J. Solano, Ivan Stojmenović, Jie Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y TecnologíaNational Science Foundation
KeywordsComputer networkComputer scienceFlooding (psychology)Wireless ad hoc networkGeographic routingScheduling (production processes)Distributed computingRouting protocolMobile ad hoc networkConnected dominating setRouting (electronic design automation)Dynamic Source RoutingWirelessTheoretical computer scienceGraphMathematicsTelecommunicationsNetwork packet

Abstract

fetched live from OpenAlex

In a multi-hop wireless network, each node is able to send a message to all of its neighbors that are located within its transmission radius. In a flooding task, a source sends the same message to all the network. Routing problem deals with finding a route between a source and a destination. In the activity-scheduling problem, each node decides between active or passive state. We present a scheme whose goal is to prolong network life while preserving connectivity. Each node is either active or has an active neighbor node. Routing and broadcasting are restricted to active nodes that create such dominating set. Activity status is periodically updated during a short transition period. The main contribution of this article is to propose new metrics for previously studied source-independent localized dominating sets, based on combinations of node degrees and remaining energy levels, for deciding activity status.

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)0.005
metaresearch head score (Gemma)0.023
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0020.006
Open science0.0020.002
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 head0.018
GPT teacher head0.255
Teacher spread0.237 · 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

Citations51
Published2004
Admission routes2
Has abstractyes

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Same topicMobile Ad Hoc NetworksFrench-language works237,207