An Efficient Early-breaking Estimation and Tree-splitting Missing RFID Tag Identification Protocol

TL;DR

The paper introduces ETMTI, an efficient RFID protocol that quickly identifies missing tags in large-scale systems by estimating unknown tags and using a tree-splitting approach, reducing time and errors.

Contribution

It proposes a novel early-breaking estimation and deactivation method combined with a tree-splitting strategy for rapid missing tag detection in RFID systems.

Findings

ETMTI reduces identification time compared to benchmarks.

ETMTI achieves a lower false negative rate.

Theoretical analysis confirms optimal parameters and performance.

Abstract

Recent statistics have demonstrated that missing items have become the main cause of loss for retailers in inventory management. To quickly identify missing tags, traditional protocols adopt Aloha-based strategies which take a long time, especially when the number of tags is large. Among them, few works considered the effect of unexpected unknown tags on the missing tag identification process. With the presence of unknown tags, some missing tags may be falsely identified as present. Thus, the system's reliability is hardly guaranteed. In this work, we propose an efficient early-breaking estimation and tree-splitting-based missing tag identification (ETMTI) protocol for large-scale RFID systems. In ETMTI, a new early-breaking estimation and deactivation method is developed to effectively estimate the number of unknown tags and deactivate them within a short time. Next, a new tree-splitting-based missing tag identification method is proposed to quickly identify missing tags with a B-ary splitting tree. Besides, a bit-tracking response strategy is designed to further reduce the time cost. The optimal parameters, time cost, and false negative rate of ETMTI are analyzed theoretically. Simulation results are presented to demonstrate that the proposed ETMTI protocol takes a smaller time and has a lower false negative rate than the best-performing benchmarks.