Channel-Quality Aware RFID Tag Identification Algorithm to Accommodate the Varying Channel Quality of IoT Environment
Source abstract
Abstract
Radio Frequency Identification (RFID) technique is broadly adopted as the automated identification system for the Internet of Things (IoT). Many RFID anti-collision algorithms were proposed to accelerate the tag identification process. However, they misjudged some unreadable slots which were due to collision instead of the bad channel condition, causing low bandwidth usage. This study proposes the Channel-quality Aware Query Tree algorithm (CAQT) to improve the identification performance in an error-prone channel environment. CAQT has three novel features: (1) it estimates the channel quality continuously and statistically in the rapidly changing channel quality environment; (2) it asks the tag for retransmission or to split the collide tags based on the channel quality; (3) the number of the groups which it splits tags is based on the estimated number of tags collide in current slot. The simulation results show that CAQT uses less than 31% slots compared with the conventional algorithms. The simulation results also demonstrate that CAQT provides enhanced performance when the channel quality is varying especially in outdoor environment, for example, ticket checking for railway or subway system.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: RFID technology advancements · Energy Harvesting in Wireless Networks · Indoor and Outdoor Localization Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei 11214, Taiwan
Mathematics and Applied Mathematics (Finance and Statistics), School of Information Engineering, Sanming University, Sanming 365004, Fujian, China
Department of Applied Informatics, Fo Guang University, Yilan County 26247, Taiwan
National Taiwan University of Science and Technology, Taipei 10607, Taiwan
References (36)
Show source-provided references
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