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Record W2974303744 · doi:10.1109/jiot.2019.2942037

Energy Utilization-Aware Operation Control Algorithm in Energy Harvesting Base Stations

2019· article· en· W2974303744 on OpenAlexaff
Haneul Ko, Sangheon Pack, Victor C. M. Leung

Bibliographic record

VenueIEEE Internet of Things Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of British Columbia
FundersNational Research Foundation of KoreaKorea UniversityNational Research Foundation
KeywordsEnergy consumptionComputer scienceBase stationEnergy (signal processing)Wireless sensor networkTransmission (telecommunications)Flexibility (engineering)Real-time computingComputer networkTelecommunicationsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Radio frequency (RF) energy transfer has received high attention as a promising technology for wireless sensor networks (WSNs) due to its flexibility of energy supply. However, unplanned RF energy transmissions may lead to increased energy consumption in the main grid. To address this problem, we first develop the energy queuing models for base station (BS) and sensor node (SN). Based on them, we propose an energy utilization-aware operation control algorithm (EU-OCA) to minimize the energy outage probabilities of SNs while maintaining the energy consumption of the main grid below a certain level. In EU-OCA, a controller determines jointly the active/sleep modes and the transmission powers of renewable energy-based BSs with the consideration of the statistical information on the energy arrival of BSs and the energy consumption of SNs. Evaluation results demonstrate that EU-OCA can achieve longer lifetime compared to other BS operation control algorithms while maintaining the energy consumption of the main grid below a target level.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.914
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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.

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

Citations17
Published2019
Admission routes1
Has abstractyes

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