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 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.

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Publication Details

Journal
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Published
2024-11-21
DOI
https://doi.org/10.1145/3699777
Citations
8
Primary Topic
Indoor and Outdoor Localization Technologies
Type
article
Field-Weighted Citation Impact
1.67
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article

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

Xieyang Sun, Cui Zhao, Wei Xi, Han Ding et al.
8 citations
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Indoor and Outdoor Localization Technologies
1.67
article

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

Xieyang Sun, Cui Zhao, Wei Xi, Han Ding, Zhenjiang Li, Jizhong Zhao
article en
8 citations

Abstract

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.

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous TechnologiesVol. 8(4)
City University of Hong Kong (HK), Xi'an Jiaotong University (CN)
Affordable and clean energy
Openalex Percentile: Top 15%
Indoor and Outdoor Localization Technologies
1.67
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