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The proliferation of the Internet of Things is calling for new modalities that enable human interaction with smart objects. Recent research has explored RFID tags as passive sensors to detect finger touch. However, existing approaches either rely on custom-built RFID readers or are limited to pre-trained finger-swiping gestures. In this paper, we introduce KeyStub, which can discriminate multiple discrete keystrokes on an RFID tag. KeyStub interfaces with commodity RFID ICs with multiple microwave-band resonant stubs as keys. Each stub's geometry is designed to create a predefined impedance mismatch to the RFID IC upon a keystroke, which in turn translates into a known amplitude and phase shift, remotely detectable by an RFID reader. KeyStub combines two ICs' signals through a single common-mode antenna and performs differential detection to evade the need for calibration and ensure reliability in heavy multi-path environments. Our experiments using a commercial-off-the-shelf RFID reader and ICs show that up to 8 buttons can be detected and decoded with accuracy greater than 95%. KeyStub points towards a novel way of using resonant stubs to augment RF antenna structures, thus enabling new passive wireless interaction modalities.
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DBF: A General Framework for Anomaly Detection in RFID Systems
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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- Award ID(s):
- 1115548
- PAR ID:
- 10062287
- Date Published:
- Journal Name:
- Proceedings - IEEE INFOCOM
- ISSN:
- 0743-166X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
