A Near-Optimal Category Information Sampling in RFID Systems

TL;DR

The paper addresses Category Information Sampling (CIS) in RFID systems under tag-missing conditions by deriving a formal lower bound on execution time and proposing a near-optimal two-stage protocol, OPT-C, that approaches this bound. OPT-C1 rapidly selects slightly more than the target per-category sample via parallel Bernoulli trials, while OPT-C2 refines to exactly $c_k$ tags and assigns unique reporting orders with minimal additional cost. The authors provide rigorous analyses of the two stages, prove overall near-optimality (within a factor of about 1.88 of the lower bound), and validate practicality through extensive simulations and real-world experiments on COTS devices, including a 37% average time reduction versus competing protocols. The work offers a scalable, hash-based sampling framework with efficient tag selection (SelGen) and demonstrates its applicability to real RFID deployments, enabling faster, more reliable category-level data collection in real-time applications.

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.

Figures (11)

• Figure 1: Ensuring High Success Probability with a Small Reliability Number. Note: (a) The success probability demonstrates rapid growth as the reliability number $c_k$ increases, even when a substantial portion ($70\%$) of tags in $P_k$ are missing ($\alpha_k=0.7$). (b) This graph illustrates the reliability number $c_k$ that can secure a high success probability of 0.99, as $\alpha_k$ varies from $0.05$ to $0.9$.
• Figure 2: An example of the Category Information Sampling Problem in an RFID system with missing tags. Suppose $c_1 =1$, $c_2 = 1$, $c_3=2$. Then the Category Information Sampling Problem requires the reader to randomly select one tag from $P_1$ and read it, randomly select one tag from $P_2$ and read it, and randomly select two tags from $P_3$ and read them. Note that the three tags: $t_2,t_5,t_6$ are missing, but the reader does not know this fact.
• Figure 3: A running example of OPT-C2.
• Figure 4: Real-world experimental scenario with densely deployed COTS tags in a small area.
• Figure 5: The entropy of $200$ COTS tag IDs.

Theorems & Definitions (9)

• Definition 2.1
• Theorem 2.2
• Theorem 3.1
• Theorem 3.2
• Theorem 3.3
• Theorem 3.4
• Theorem 3.5
• Theorem 3.6
• Theorem 3.7