  In many RFID-enabled applications, objects are classified into different
categories, and the information associated with each object's category (called
category information) is written into the attached tag, allowing the reader to
access it later. The category information sampling in such RFID systems, which
is to randomly choose (sample) a few tags from each category and collect their
category information, is fundamental for providing real-time monitoring and
analysis in RFID. However, to the best of our knowledge, two technical
challenges, i.e., how to guarantee a minimized execution time and reduce
collection failure caused by missing tags, remain unsolved for this problem. In
this paper, we address these two limitations by considering how to use the
shortest possible time to sample a different number of random tags from each
category and collect their category information sequentially in small batches.
In particular, we first obtain a lower bound on the execution time of any
protocol that can solve this problem. Then, we present a near-OPTimal Category
information sampling protocol (OPT-C) that solves the problem with an execution
time close to the lower bound. Finally, extensive simulation results
demonstrate the superiority of OPT-C over existing protocols, while real-world
experiments validate the practicality of OPT-C.
