.png){kind=icon}
.png){kind=icon}
Return
An Efficient Protocol for RFID Multigroup Threshold-Based Classification Based on Sampling and Logical Bitmap
.png){kind=icon}
OA
AI
DOI：10.1109/TNET.2014.2367520.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Most existing research adopts a flat view of radio frequency identification (RFID) systems to perform various functions of collecting tag IDs, estimating the number of tags, detecting the missing tags, etc. However, in practice, tags are often attached to objects of different groups, which may represent different product types in a warehouse, different book categories in a library, etc. As we move from a flat view to an organized group view, there arise many interesting problems. One of them, called multigroup threshold-based classification, is the focus of this paper. It is to determine whether the number of objects in each group is above or below a prescribed threshold value. Solving this problem is important for inventory tracking applications. If the number of groups is very large, it will be inefficient to measure the groups one at a time. The best existing solution for multigroup threshold-based classification is based on generic group testing, whose design is however geared toward detecting a small number of populous groups. Its performance degrades quickly when the number of groups above the threshold becomes large. In this paper, we propose a new classification protocol based on tag sampling and logical bitmaps. It achieves high efficiency by measuring all groups in a mixed fashion. In the meantime, we show that the new method is able to perform threshold-based classification with an accuracy that can be preset to any desirable level, allowing tradeoff between time efficiency and accuracy.
Keywords:
Multigroup threshold-based classification
radio frequency identification (RFID)
time efficiency
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
3.6
Papers:
4.4K
Citations:
9.5K
View more organizations