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Record W3180505701 · doi:10.3390/mi12070819

A Survey on Battery-Less RFID-Based Wireless Sensors

2021· review· en· W3180505701 on OpenAlexafffund
Nabil Khalid, Rashid Mirzavand, Ashwin K. Iyer

Bibliographic record

VenueMicromachines · 2021
Typereview
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsWireless sensor networkWirelessBattery (electricity)Key distribution in wireless sensor networksExploitNetwork topologyComputer scienceRadio-frequency identificationKey (lock)Wi-Fi arrayWireless networkChipMobile wireless sensor networkComponent (thermodynamics)Range (aeronautics)Embedded systemComputer networkEngineeringTelecommunicationsPower (physics)

Abstract

fetched live from OpenAlex

We present a survey on battery-less Radio Frequency Identification (RFID-based wireless sensors that have emerged in the past several years. We discuss the evolution of RFID turning into wireless sensors. Moreover, we talk about different components of these battery-less RFID-based wireless sensors, five main topologies that transform a simple RFID chip into a battery-less wireless sensor, and state-of-the-art implementations of these topologies. In battery-less wireless sensors, the read range is of key importance. Hence, we discuss how each component of the sensor plays its role in determining the read range and how each topology exploits these components to optimize read range, complexity, and/or cost. Additionally, we discuss potential future directions that can help provide improvements in RFID-based wireless sensor technology.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.004

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 head0.042
GPT teacher head0.282
Teacher spread0.240 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations38
Published2021
Admission routes2
Has abstractyes

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