Revisiting RFID Missing Tag Identification

TL;DR

This work revisits the missing tag identification problem in RFID networks, providing a rigorous, algorithm-agnostic lower bound on the achievable time and quantifying how existing solutions compare. It introduces a novel collision-partition tree (CPT) data structure that leverages a subset of tag pseudo-IDs and Manchester coding to parse the tree efficiently. The CPT algorithm achieves an expected time of $\Theta\left(\frac{\log\log N}{\log N}N+\frac{(1-\alpha)^2(1-\delta)^2}{ \epsilon^2}\right)$, reducing the overhead by up to a factor of $\log N$ over the previous best. While CPT approaches the theoretical limit, the paper identifies a remaining gap to the bound and motivates future work to close it, with significant implications for scalable RFID missing-tag diagnostics. Overall, the work advances both theory and practice by providing tight bounds and a scalable, tree-based solution for fast, reliable missing tag identification.

Abstract

We revisit the problem of missing tag identification in RFID networks by making three contributions. Firstly, we quantitatively compare and gauge the existing propositions spanning over a decade on missing tag identification. We show that the expected execution time of the best solution in the literature is $Θ\left(N+\frac{(1-α)^2(1-δ)^2}{ ε^2}\right)$, where $δ$ and $ε$ are parameters quantifying the required identification accuracy, $N$ denotes the number of tags in the system, among which $αN$ tags are missing. Secondly, we analytically establish the expected execution time lower-bound for any missing tag identification algorithm as $Θ\left(\frac{N}{\log N}+\frac{(1-δ)^2(1-α)^2}{ε^2 \log \frac{(1-δ)(1-α)}ε}\right)$, thus giving the theoretical performance limit. Thirdly, we develop a novel missing tag identification algorithm by leveraging a tree structure with the expected execution time of $Θ\left(\frac{\log\log N}{\log N}N+\frac{(1-α)^2(1-δ)^2}{ ε^2}\right)$, reducing the time overhead by a factor of up to $\log N$ over the best algorithm in the literature. The key technicality in our design is a novel data structure termed as collision-partition tree (CPT), built on a subset of bits in tag pseudo-IDs, leading to more balanced tree structure and reducing the time complexity in parsing the entire tree.

Figures (6)

• Figure 1: Illustration of Manchester coding.
• Figure 2: Illustration of the reader-tag dialogue. Query, QueryRep and QueryAdj are used to initialize a new frame, among which QueryRep and QueryAdj are broadcast by the reader in tag identification. ACK, CW, RN16 and EPC are the acknowledge command, the continuous ware, the 16-bit random number and the electronic product code, respectively.
• Figure 3: (a) Pseudo-IDs of tags; (b) the constructed CPT
• Figure 4: Illustration of Step 2: parsing the sub-tree rooted in node $1$ in the CPT in Fig. \ref{['UI']}
• Figure 5: Performance comparison with different parameter settings from the first group of experiments.

Theorems & Definitions (9)

• Lemma 1
• Lemma 2
• Theorem 1: Algorithm-independent performance bound
• Remark
• Theorem 2
• Lemma 3
• Example 1
• Theorem 3
• Lemma 4