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Record W1981779732 · doi:10.1109/jsac.2015.2391892

On the Performance of Overlaid Wireless Sensor Transmission with RF Energy Harvesting

2015· article· en· W1981779732 on OpenAlexaff
Tianqing Wu, Hong‐Chuan Yang

Bibliographic record

VenueIEEE Journal on Selected Areas in Communications · 2015
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceEnergy harvestingWireless sensor networkRayleigh fadingNetwork packetTransmission (telecommunications)Transmission delayWirelessElectronic engineeringEnergy (signal processing)FadingComputer networkChannel (broadcasting)TelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

RF energy harvesting is a promising potential solution for providing convenient and perpetual energy supply to low-power wireless sensor networks. In this paper, we investigate the performance of overlaid wireless sensor transmission powered by RF energy harvesting from existing wireless system. Specifically, we derive the exact closed-form expression for the distribution function of harvested energy over a certain number of channel coherence time over Rayleigh fading channels with the consideration of hardware limitation, such as energy harvesting sensitivity and harvesting efficiency. We also obtain the exact distribution of the number of coherence time needed for fully charging the sensor. Based on these analytical results, we analyze the average packet delay and packet loss probability of sensor transmission subject to interference from existing system, for delay insensitive traffics and delay sensitive traffics, respectively. Finally, we investigate the optimal design of energy storage capacity of the sensor nodes to minimize the average packet transmission delay for delay insensitive traffics with two candidate transmission strategies.

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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.233
Teacher spread0.206 · 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

Citations69
Published2015
Admission routes1
Has abstractyes

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