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Record W2132435544 · doi:10.1109/wcnc.2009.4917878

Packet Transmission Scheduling Algorithm for Wireless Sensor Networks with Energy Limited Mobile Sink

2009· article· en· W2132435544 on OpenAlexaff
Ali Sharifkhani, Norman C. Beaulieu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceWireless sensor networkNetwork packetSink (geography)Computer networkEnergy consumptionTransmission delayReal-time computingScheduling (production processes)Sensor nodeWirelessKey distribution in wireless sensor networksAlgorithmWireless networkEngineeringTelecommunicationsMathematical optimizationMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

A transmission scheduling algorithm is proposed for wireless sensor networks with energy limited mobile sinks. The algorithm is primarily based on the well known tradeoff between the expected number of data packets that are successfully received by the sink and the transmission power consumed in the system. The likelihood of having new packets generated at the sensor node during the transmission as well as the possibility of erroneous reception of acknowledge packets at the sensor node are accounted for in the algorithm. The behavior of the system is studied in terms of power consumption and packet reception for different packet generation rates as well as different costs of transmission power at the sink.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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