On BFSA Collision Resolution in LF, HF, and UHF RFID Networks

On BFSA Collision Resolution in LF, HF, and UHF RFID Networks

Varun Bhogal (School of Computing, University of North Florida, Jacksonville, FL, USA), Zornitza Genova Prodanoff (School of Computing, University of North Florida, Jacksonville, FL, USA), Sanjay P. Ahuja (School of Computing, University of North Florida, Jacksonville, FL, USA) and Kenneth Martin (School of Computing, University of North Florida, Jacksonville, FL, USA)
Copyright: © 2015 |Pages: 12
DOI: 10.4018/IJWNBT.2015040104

Abstract

RFID (radio frequency identification) technology has gained popularity in a number of applications. Decreased cost of hardware components along with wide adoption of international RFID standards have led to the rise of this technology. One of the major factors associated with the implementation of RFID infrastructure is the cost of tags. RFID tags operating in the low frequency spectrum are widely used because they are the least expensive, but have a small implementation range. This paper presents an analysis of RFID performance across low frequency (LF), high frequency (HF), and ultra-high frequency (UHF) environments. The authors' evaluation is theoretical, using a passive-tag BFSA based simulation model that assumes 10 to 1,500 tags per reader and is created with OPNET Modeler 17. Ceteris paribus, the authors' results indicate that total census delay is lowest for UHF tags, while network throughput performance of LF tags is highest for large scale implementations of hundreds of tags in reader's range. A statistical analysis has been conducted on the findings for the three different sets.
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2. Previous Analytical Results On Performance

2.1. Optimal Frame Size Analysis

For the purposes of this study, total census delay and network throughput were evaluated where an optimal fixed frame size (in number of slots) has been used for a given number of tags. For n number of tags, the optimal frame size can be evaluated as follows (Prodanoff, 2010):

,where Nopt represents the optimal frame size and ln(2) represents the natural logarithm of the integer 2. The optimal frame size is kept constant for the duration of a census.

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