A Bit-Tracking Knowledge-Based Query Tree for RFID Tag Identification in IoT Systems.

Yuan-Cheng Lai, Shan-Yung Chen, Zelalem Legese Hailemariam, Chih-Chung Lin

Open access · goldAbstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

1.1field-weighted citation impact, top 20% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 10 citations in OpenAlex.

A knowledge tracing approach with dual graph convolutional networks and positive/negative feature enhancement network.PloS one · 2025
Article

4 · The record

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5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Yuan-Cheng LaiDepartment of Information Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan.ORCID 0000-0003-3695-5784

Shan-Yung ChenDepartment of Information Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan.

Zelalem Legese HailemariamDepartment of Information Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan.ORCID 0000-0001-5914-2693

Chih-Chung LinDepartment of Computer Information and Network Engineering, Lunghwa University of Science and Technology, Taoyuan 333, Taiwan.

National Taiwan University of Science and Technology · TWLunghwa University of Science and Technology · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In an IoT (Internet of Things) system where each IoT device has one/many RFID tags, there might be many RFID tags. However, when multiple tags respond to the reader's interrogation at the same time, their signals collide. Due to the collision, the reader must request the colliding tags to retransmit their IDs, resulting in higher communication overhead and longer identification time. Therefore, this paper presents a Bit-tracking Knowledge-based Query Tree (BKQT), which uses two techniques:

Indexed as

Internet of ThingsRadio Frequency Identification DeviceComputer SimulationKnowledge Basesanti-collision protocolbit trackingknowledge-basedquery treeRFID

Identifiers

PMID35591013

DOI10.3390/s22093323

PMCPMC9101040

OpenAlexW4224937150

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.