A Near-Optimal Category Information Sampling in RFID Systems

TL;DR

This paper introduces a near-optimal protocol for category information sampling in RFID systems, minimizing execution time and failure rates, with proven theoretical bounds and validated through simulations and real-world tests.

Contribution

It proposes a novel sampling protocol that approaches the theoretical lower bound on execution time for RFID category information collection.

Findings

OPT-C achieves near-optimal execution time.

OPT-C outperforms existing protocols in simulations.

Real-world experiments confirm practicality of OPT-C.

Abstract

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.