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RFID technologies are making their way into numerous applications, including inventory management, supply chain, product tracking, transportation, logistics, etc. One important application is to automatically detect anomalies in RFID systems, such as missing tags, unknown tags, or cloned tags due to theft, management error, or targeted attacks. Existing solutions are all designed to detect a certain type of RFID anomalies, but lack a general functionality for detecting different types of anomalies. This paper attempts to propose a general framework for anomaly detection in RFID systems, thereby reducing the complexity for readers and tags to implement different anomaly-detection protocols. We introduce a new concept of differential Bloom filter (DBF), which turns physical-layer signal data into a segmented Bloom filter that encodes the IDs of abnormal tags. As a case study, we propose a protocol that builds DBF for identifying all missing tags in an efficient way. We implement a prototype for missing-tag identification using USRP and WISP tags to verify the effectiveness our protocol, and use large-scale simulations for performance evaluation. The results show that our solution can significantly improve time efficiency, when comparing with the best existing work.
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This content will become publicly available on December 18, 2025
Enhancing RFID Observations Using Masked Autoencoders for Improved Indoor Localization
This paper presents the MAE model that uses a Masked AutoEncoder (MAE) to enhance the observations from commercial passive Radio-Frequency Identification (RFID) devices. It is crucial to address the common issue of RFID readers failing to collect observations from all their hop channels and antennas due to environmental effects and device limitations. The proposed method examines the inner rationale among observations from various channels and antennas to reconstruct the missing observations, which can significantly improve the performance of downstream applications. The experiment results show that when we collect more than 70% observation in all antennas at all channels, our MAE model can restore 90% of the missing phase with an error of less than 0.1 radians, which is even less than the error caused by thermal noise in an RFID system. Our MAE model's accuracy in restoring missing data provides a promising future to improve the performance of various RFID applications like localization and motion tracking by providing more complete observations.
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- Award ID(s):
- 2245607
- PAR ID:
- 10598020
- Publisher / Repository:
- IEEE
- Date Published:
- ISSN:
- 2836-3574
- ISBN:
- 979-8-3315-4022-7
- Page Range / eLocation ID:
- 149 to 152
- Subject(s) / Keyword(s):
- Radio-frequency identification (RFID), Ultrahigh frequency (UHF), Masked AutoEncoder (MAE), Observation restoration.
- Format(s):
- Medium: X
- Location:
- Daytona Beach, FL, USA
- Sponsoring Org:
- National Science Foundation
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