A Self-Enhancement Solution for Standard RFIDs: Software-based Cross-protocol Communication and Localization

Radio Frequency Identification (RFID) is widely recognized for its cost-effectiveness, energy efficiency, and ease of manufacturing, promising versatile applications such as material recognition, vibration detection, and localization. However, these inherent lightweight characteristics necessitate e...

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Veröffentlicht in:Proceedings of ACM on interactive, mobile, wearable and ubiquitous technologies mobile, wearable and ubiquitous technologies, 2024-11, Vol.8 (4), p.1-22, Article 159
Hauptverfasser: Zhao, Cui, Li, Zhenjiang, Ding, Han, Sun, Xieyang, Xi, Wei, Zhao, Jizhong
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Sprache:eng
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Zusammenfassung:Radio Frequency Identification (RFID) is widely recognized for its cost-effectiveness, energy efficiency, and ease of manufacturing, promising versatile applications such as material recognition, vibration detection, and localization. However, these inherent lightweight characteristics necessitate extensive computations or tailored devices for specific tasks. Consequently, there is a critical imperative to explore and augment the sensing and communication capabilities of standard RFIDs to better accommodate a broader spectrum of Internet of Things (IoT) services. In this paper, we introduce SENSO, a self-enhancement solution designed to facilitate diverse tasks without the need for hardware modifications or additional device support. Our approach introduces two key innovations to enable cross-protocol communication (RFID-LoRa) and localization. Firstly, we seamlessly integrate Chirp Spread Spectrum (CSS) modulation into RFID, enabling simultaneous communication with commercial tags and LoRa devices. Secondly, we realize the transmission of wider-band chirps to activate and localize the tag concurrently. By harnessing these techniques, we developed a prototype while addressing several challenges. Our evaluation demonstrates that when maintaining the fundamental RFID communication, SENSO can effectively deliver additional messages to LoRa devices across various indoor scenarios, e.g., through multiple floors, rooms, and corners, and extending over outdoor distances spanning kilometers. Furthermore, it achieves an average accuracy of approximately 5 to 10 centimeters in tag ranging and localization. The source codes of SENSO are available at https://github.com/Cui-Zhao/SENSO.
ISSN:2474-9567
2474-9567
DOI:10.1145/3699777