A machine learning approach for package size estimation using UHF RFID interrogation signature

Availability of data The datasets generated and analysed during the current study are available in the author’s GitHub repository (https://github.com/plopezmp/package-size-estimation-using-UHF-RFID-signature.

Detalles Bibliográficos
Autores: Vales Alonso, Javier, López Matencio Pérez, Pablo Antonio
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad Politécnica de Cartagena(UPCT)
Repositorio:Repositorio Digital UPCT
OAI Identifier:oai:repositorio.upct.es:10317/13882
Acceso en línea:http://hdl.handle.net/10317/13882
https://link.springer.com/article/10.1007/s10489-024-05412-2
Access Level:acceso abierto
Palabra clave:RFID gate
Logistics
Machine learning
Supervised learning
Package size estimation
Ingeniería Telemática
33 Ciencias Tecnológicas
3325.05 Radiocomunicaciones
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oai_identifier_str oai:repositorio.upct.es:10317/13882
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repository_id_str
spelling A machine learning approach for package size estimation using UHF RFID interrogation signatureVales Alonso, JavierLópez Matencio Pérez, Pablo AntonioRFID gateLogisticsMachine learningSupervised learningPackage size estimationIngeniería Telemática33 Ciencias Tecnológicas3325.05 RadiocomunicacionesAvailability of data The datasets generated and analysed during the current study are available in the author’s GitHub repository (https://github.com/plopezmp/package-size-estimation-using-UHF-RFID-signature.This paper introduces a new approach for performing package classification and sizing using Radio-Frequency Identification (RFID) systems. This technique is applicable when packages are labeled with or contain multiple RFID-tagged items. During the interrogation of the tags, received signal strength (RSS) statistics and other information, such as the frame count or the reading time, are collected by the reader and used to predict the package type from a set of candidate classes using an Artificial Neural Network (ANN). The primary challenge lies in acquiring sufficient training data for a target scenario to ensure reliable predictions. To address this, a two-phase training process based on transfer learning is adopted. Initially, a base model is developed using synthetic data generated from a detailed RFID simulator, designed to suit diverse scenarios, establish detailed link budgets, and comprehensively simulate the communication protocols. This model is then refined using a small dataset collected experimentally in the actual scenario. This method was validated in a real testbed with four different package types. The base model was trained using 1000 synthetic samples per package type (4000 in total), whereas the refined model was trained with a dataset consisting of only 25 real interrogation traces (samples) per package type (100 in total). The experimental samples were obtained using a software-defined radio unit, the Ettus B210 Universal Software Radio Peripheral (USRP) platform. This experiment achieved an accuracy of over 92\%. In summary, this approach introduces a new feature to existing RFID setups, demonstrating potential for advanced package handling and cost optimization in the logistics sector.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Partial funding through grant AriSe, (Ref. PID2020-116329GB / AEI / 10.13039/501100011033).Kluwer AcademicAgencia Estatal de Investigación202420242024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10317/13882https://link.springer.com/article/10.1007/s10489-024-05412-2reponame:Repositorio Digital UPCTinstname:Universidad Politécnica de Cartagena(UPCT)Ingléshttps://github.com/plopezmp/package-size-estimation-using-UHF-RFID-signaturePID2020-116329GBAtribución-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:repositorio.upct.es:10317/138822026-05-15T06:39:02Z
dc.title.none.fl_str_mv A machine learning approach for package size estimation using UHF RFID interrogation signature
title A machine learning approach for package size estimation using UHF RFID interrogation signature
spellingShingle A machine learning approach for package size estimation using UHF RFID interrogation signature
Vales Alonso, Javier
RFID gate
Logistics
Machine learning
Supervised learning
Package size estimation
Ingeniería Telemática
33 Ciencias Tecnológicas
3325.05 Radiocomunicaciones
title_short A machine learning approach for package size estimation using UHF RFID interrogation signature
title_full A machine learning approach for package size estimation using UHF RFID interrogation signature
title_fullStr A machine learning approach for package size estimation using UHF RFID interrogation signature
title_full_unstemmed A machine learning approach for package size estimation using UHF RFID interrogation signature
title_sort A machine learning approach for package size estimation using UHF RFID interrogation signature
dc.creator.none.fl_str_mv Vales Alonso, Javier
López Matencio Pérez, Pablo Antonio
author Vales Alonso, Javier
author_facet Vales Alonso, Javier
López Matencio Pérez, Pablo Antonio
author_role author
author2 López Matencio Pérez, Pablo Antonio
author2_role author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación
dc.subject.none.fl_str_mv RFID gate
Logistics
Machine learning
Supervised learning
Package size estimation
Ingeniería Telemática
33 Ciencias Tecnológicas
3325.05 Radiocomunicaciones
topic RFID gate
Logistics
Machine learning
Supervised learning
Package size estimation
Ingeniería Telemática
33 Ciencias Tecnológicas
3325.05 Radiocomunicaciones
description Availability of data The datasets generated and analysed during the current study are available in the author’s GitHub repository (https://github.com/plopezmp/package-size-estimation-using-UHF-RFID-signature.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10317/13882
https://link.springer.com/article/10.1007/s10489-024-05412-2
url http://hdl.handle.net/10317/13882
https://link.springer.com/article/10.1007/s10489-024-05412-2
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://github.com/plopezmp/package-size-estimation-using-UHF-RFID-signature
PID2020-116329GB
dc.rights.none.fl_str_mv Atribución-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Kluwer Academic
publisher.none.fl_str_mv Kluwer Academic
dc.source.none.fl_str_mv reponame:Repositorio Digital UPCT
instname:Universidad Politécnica de Cartagena(UPCT)
instname_str Universidad Politécnica de Cartagena(UPCT)
reponame_str Repositorio Digital UPCT
collection Repositorio Digital UPCT
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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