Food and liquid sensing in practical environments using RFIDs
Name
RFEATS-paper.pdf
Description
Accepted version
Size
11.78 MB
Format
Adobe PDF
Checksum (MD5)
2b2bcedcc3234383d59e9827e8d111a4
Author(s)
Ha, U
Leng, J
Khaddaj, A
Adib, Fadel
Date Issued
2020
Journal
Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2020
Citation
Ha, U, Leng, J, Khaddaj, A and Adib, F. 2020. "Food and liquid sensing in practical environments using RFIDs." Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2020.
Version
Author's final manuscript
Abstract
We present the design and implementation of RF-EATS, a system that can sense food and liquids in closed containers without opening them or requiring any contact with their contents. RF-EATS uses passive backscatter tags (e.g., RFIDs) placed on a container, and leverages near-field coupling between a tag's antenna and the container contents to sense them noninvasively. In contrast to prior proposals that are invasive or require strict measurement conditions, RF-EATS is noninvasive and does not require any calibration; it can robustly identify contents in practical indoor environments and generalize to unseen environments. These capabilities are made possible by a learning framework that adapts recent advances in variational inference to the RF sensing problem. The framework introduces an RF kernel and incorporates a transfer model that together allow it to generalize to new contents in a sample-efficient manner, enabling users to extend it to new inference tasks using a small number of measurements. We built a prototype of RF-EATS and tested it in seven different applications including identifying fake medicine, adulterated baby formula, and counterfeit beauty products. Our results demonstrate that RF-EATS can achieve over 90% classification accuracy in scenarios where state-of-the-art RFID sensing systems cannot perform better than a random guess.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://www.usenix.org/system/files/nsdi20-paper-ha.pdf
