Your browser doesn't support javascript.
loading
RFID Adaptive Parallel Response Collision Tree Algorithm Based on Lock-Bit.
Luo, Xuan; Jia, Xiaolin; Gu, Yajun.
Afiliação
  • Luo X; School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China.
  • Jia X; Mobile Internet of Things and Radio Frequency Identification Technology Key Laboratory of Mianyang (MIOT&RFID), Mianyang 621010, China.
  • Gu Y; School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China.
Sensors (Basel) ; 24(2)2024 Jan 09.
Article em En | MEDLINE | ID: mdl-38257482
ABSTRACT
This paper proposes the Lock-Position-Based RFID Adaptive Parallel Collision Tree (LAPCT) algorithm to address the issues of excessive time slots required in the identification process of collision tree algorithms for multiple tags and the high communication complexity between the reader and multiple tags. The LAPCT algorithm adopts a single-query multiple-response mechanism and dynamically divides the response sub-cycle numbers in the identification cycle based on an adaptive strategy. It uses Manchester encoding to lock collision positions and generate a common query prefix, effectively reducing the number of reader queries. This reduction in queries decreases the total number of required time slots and transmitted bits during the reader-tag communication process, thereby improving the efficiency of multiple tag recognition. Theoretical and simulation experiments demonstrate that compared to similar algorithms, the LAPCT algorithm achieves a maximum reduction of 37% in total time slots required, a maximum improvement of 30% in recognition efficiency, and a maximum reduction of 90% in communication complexity. Furthermore, with an increase in the number of tags, the performance advantages of the LAPCT algorithm become more pronounced, making it suitable for large-scale tag scenarios.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China