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Robust RFID Tag Identification.
Benedetti, David; Maselli, Gaia.
Afiliación
  • Benedetti D; Zirak, 12084 Mondovì, Italy.
  • Maselli G; Computer Science Department, Sapienza University of Rome, 00161 Rome, Italy.
Sensors (Basel) ; 22(21)2022 Nov 01.
Article en En | MEDLINE | ID: mdl-36366102
ABSTRACT
Fast and reliable identification of Radio Frequency Indentification (RFID) tags by means of anticollision (MAC) protocols has been a problem of substantial interest for more than a decade. However, improvements in identification rate have been slow, as most solutions rely on sequential approaches that try to avoid collisions, which have limited margin for performance improvement. Recently, there has been growing interest in concurrent techniques that exploit the structure of collisions to recover tag IDs. While these techniques promise substantial improvements in speed, a key question that remains unaddressed is how to deal with noise or interference that might introduce errors in the recovery process at the reader. Our goal in this paper is to consider a noisy wireless channel and add robustness to concurrent RFID identification techniques. We propose a new protocol, called CIRF (Concurrent Identification of RFids), which uses multiple antennas to add robustness to noise and leverages block sparsity-based optimization to recover EPC IDs of transmitting tags. We include fail-safe methods to handle errors that persist after the optimization stage. Extensive simulations show that CIRF achieves substantial resilience improvement in a range of very low to medium Signal-to-Noise (SNR) situations, being able to always correctly recover 99% of tags.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dispositivo de Identificación por Radiofrecuencia Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Italia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dispositivo de Identificación por Radiofrecuencia Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Italia
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