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.

翻译：在许多基于RFID的应用中，物体被划分为不同类别，每个物体所属类别的相关信息（称为类别信息）被写入其附着的标签中，以便读写器后续访问。此类RFID系统中的类别信息采样——即从每个类别中随机选择（采样）少量标签并收集其类别信息——是实现RFID实时监控与分析的基础。然而，据我们所知，该问题仍存在两大技术挑战未得到解决：如何保证最短的执行时间，以及如何减少因标签缺失导致的收集失败。本文通过研究如何以最短时间从每个类别中依次分批采样不同数量的随机标签并收集其类别信息，来解决这两个局限性。具体而言，我们首先推导了能够解决此问题的任何协议所需执行时间的下界。随后，我们提出了一种近乎最优的类别信息采样协议（OPT-C），该协议能以接近该下界的执行时间解决问题。最后，大量仿真结果证明了OPT-C相较于现有协议的优越性，而实际场景实验验证了OPT-C的实用性。