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Food and liquid sensing in practical environments using RFIDs

Published: 25 February 2020 Publication History

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 non-invasive 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.

References

[1]
Alert for methanol. https://foodsafety.neogen.com/en/alert-methanol.
[2]
ALN-9640 Squiggle Inlay. www.alientechnology.com. Alien Technology Inc.
[3]
Children's tylenol cold & cough. https://www.tylenol.ca/products/infants-children/childrens-tylenol-cold-cough.
[4]
Counterfeit soft drinks? https://www.ameribev.org/education-resources/blog/post/counterfeit-soft-drinks/.
[5]
E8362B PNA Network Analyzer, 10 MHz to 20 GHz. https://www.keysight.com/en/pd-72279-pn-E8362B/pna-series?cc=US&lc=eng. keySight technologies.
[6]
Enfamil neuropro infant formula, ready to use. https://www.enfamil.com/products/enfamil-neuropro-infant/8-fl-oz-ready-to-use-bottles-case-24.
[7]
Extra virgin olive oil. https://www.goya.com/en/products/extra-virgin-olive-oil.
[8]
Graves grain alcohol 190@ - 750ml. https://www.worldwidebev.com/graves-grain-alcohol-190at-4360.html.
[9]
N1501A Dielectric Probe Kit. https://www.keysight.com/en/pd-2492144-pn-N1501A/dielectric-probe-kit?cc=US&lc=eng. keySight technologies.
[10]
Precision and recall. https://en.wikipedia.org/wiki/Precision_and_recall.
[11]
usrp n210. http://www.ettus.com, 2017. ettus inc.
[12]
ADIB, F., HSU, C.-Y., MAO, H., KATABI, D., AND DURAND, F. Capturing the human figure through a wall. ACM Transactions on Graphics (TOG) 34, 6 (2015), 219.
[13]
AN, J., AND CHO, S. Variational autoencoder based anomaly detection using reconstruction probability.
[14]
AN, Z., LIN, Q., AND YANG, L. Cross-frequency communication: Near-field identification of uhf rfids with wifi! In MobiCom (2018), pp. 623-638.
[15]
ANG, P. K., CHEN, W., WEE, A. T. S., AND LOH, K. P. Solution-gated epitaxial graphene as ph sensor. Journal of the American Chemical Society 130, 44 (2008), 14392-14393.
[16]
BANERJEE, P., KINTZIOS, S., AND PRABHAKARPANDIAN, B. Biotoxin detection using cell-based sensors. Toxins 5, 12 (2013), 2366-2383.
[17]
BATTAN, C. How fake beauty products have infiltrated amazon, target, and other reliable retailers, 2017.
[18]
BHATTACHARYYA, R., FLOERKEMEIER, C., AND SARMA, S. Low-cost, ubiquitous rfid-tag-antenna-based sensing. Proceedings of the IEEE 98, 9 (2010), 1593-1600.
[19]
CUMMING, E. The great wine fraud, 2016.
[20]
DAVASLIOGLU, K., AND SAGDUYU, Y. E. Generative adversarial learning for spectrum sensing. In 2018 IEEE International Conference on Communications (ICC) (2018), IEEE, pp. 1-6.
[21]
DHEKNE, A., GOWDA, M., ZHAO, Y., HASSANIEH, H., AND CHOUDHURY, R. R. Liquid: A wireless liquid identifier. In ACM MobiSys (2018).
[22]
FELIX, A., CAMMERER, S., DÖRNER, S., HOYDIS, J., AND TEN BRINK, S. Ofdm-autoencoder for end-to-end learning of communications systems. In 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (2018), IEEE, pp. 1-5.
[23]
FENG, C., LI, X., CHANG, L., XIONG, J., CHEN, X., FANG, D., LIU, B., CHEN, F., AND ZHANG, T. Material identification with commodity wi-fi devices. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems (2018), ACM, pp. 382-383.
[24]
GOSSNER, C. M.-E., SCHLUNDT, J., BEN EMBAREK, P., HIRD, S., LO-FO-WONG, D., BELTRAN, J. J. O., TEOH, K. N., AND TRITSCHER, A. The melamine incident: implications for international food and feed safety. Environmental health perspectives 117, 12 (2009), 1803-1808.
[25]
HA, U., MA, Y., ZHONG, Z., HSU, T.-M., AND ADIB, F. Learning food quality and safety from wireless stickers. In Proceedings of the 17th ACM Workshop on Hot Topics in Networks (2018), ACM, pp. 106-112.
[26]
HENLEY, J. How to tell if your olive oil is the real thing, 2012.
[27]
HUANG, W.-D., CAO, H., DEB, S., CHIAO, M., AND CHIAO, J.-C. A flexible ph sensor based on the iridium oxide sensing film. Sensors and Actuators A: Physical 169, 1 (2011), 1-11.
[28]
JIANG, W., MIAO, C., MA, F., YAO, S., WANG, Y., YUAN, Y., XUE, H., SONG, C., MA, X., KOUTSONIKOLAS, D., ET AL. Towards environment independent device free human activity recognition. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking (2018), pp. 289-304.
[29]
JOHNSON, R. C., AND JASIK, H. Antenna engineering handbook. New York, McGraw-Hill Book Company, 1984, 1356 p. No individual items are abstracted in this volume. (1984).
[30]
JOSHI, K., BHARADIA, D., KOTARU, M., AND KATTI, S. Wideo: Fine-grained device-free motion tracing using {RF} backscatter. In 12th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI}15) (2015), pp. 189-204.
[31]
JOSHI, K., HONG, S., AND KATTI, S. Pinpoint: Localizing interfering radios. In Usenix NSDI (2013).
[32]
KARIMI, F. US warns travelers about tainted alcohol in Mexico. CNN, 2017. http://www.cnn.com/2017/07/27/us/mexico-state-department-alcohol-warning/index.html.
[33]
KINGMA, D. P., AND WELLING, M. Autoencoding variational bayes. In 2nd International Conference on Learning Representations (ICLR) (2013).
[34]
KOTARU, M., JOSHI, K., BHARADIA, D., AND KATTI, S. Spotfi: Decimeter level localization using wifi. In ACM SIGCOMM computer communication review (2015), vol. 45, ACM, pp. 269-282.
[35]
KUSWANDI, B., WICAKSONO, Y., ABDULLAH, A., HENG, L. Y., AHMAD, M., ET AL. Smart packaging: sensors for monitoring of food quality and safety. Sensing and Instrumentation for Food Quality and Safety 5, 3-4 (2011), 137-146.
[36]
LI, Q., QU, H., LIU, Z., ZHOU, N., SUN, W., AND LI, J. Af-dcgan: Amplitude feature deep convolutional gan for fingerprint construction in indoor localization system. arXiv preprint arXiv:1804.05347 (2018).
[37]
LI, Z., AND SUSLICK, K. S. A hand-held optoelectronic nose for the identification of liquors. ACS sensors 3, 1 (2018), 121-127.
[38]
LI, Z., XIAO, Z., WANG, B., ZHAO, B. Y., AND ZHENG, H. Scaling deep learning models for spectrum anomaly detection. In Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing (2019), ACM, pp. 291-300.
[39]
LUO, Z., ZHANG, Q., MA, Y., SINGH, M., AND ADIB, F. 3d backscatter localization for fine-grained robotics. In 16th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI}19) (2019), pp. 765-782.
[40]
MA, Y., SELBY, N., AND ADIB, F. Minding the billions: Ultrawideband localization for deployed rfid tags. ACM MobiCom (2017).
[41]
MAKHZANI, A., SHLENS, J., JAITLY, N., AND GOODFELLOW, I. Adversarial autoencoders. In International Conference on Learning Representations (2016).
[42]
NARSAIAH, K., JHA, S. N., BHARDWAJ, R., SHARMA, R., AND KUMAR, R. Optical biosensors for food quality and safety assurance: a review. Journal of food science and technology 49, 4 (2012), 383-406.
[43]
NEW YORK TIMES. China's Top Food Official Resigns. http://www.nytimes.com/2008/09/23/world/asia/23milk.html.
[44]
NGUYEN, D. S., LE, N. N., LAM, T. P., FRIBOURG-BLANC, E., DANG, M. C., AND TEDJINI, S. Development of novel wireless sensor for food quality detection. Advances in Natural Sciences: Nanoscience and Nanotechnology 6, 4 (2015), 045004.
[45]
NIITSOO, A., EDELHÄUßER, T., AND MUTSCHLER, C. Convolutional neural networks for position estimation in tdoa-based locating systems. In 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (2018), IEEE, pp. 1-8.
[46]
ONG, K. G., BITLER, J. S., GRIMES, C. A., PUCKETT, L. G., AND BACHAS, L. G. Remote query resonant-circuit sensors for monitoring of bacteria growth: Application to food quality control. Sensors 2, 6 (2002), 219-232.
[47]
ONG, K. G., BITLER, J. S., GRIMES, C. A., PUCKETT, L. G., AND BACHAS, L. G. Remote query resonant-circuit sensors for monitoring of bacteria growth: Application to food quality control. Sensors 2, 6 (2002), 219-232.
[48]
O'SHEA, T. J., ROY, T., AND CLANCY, T. C. Over-the-air deep learning based radio signal classification. IEEE Journal of Selected Topics in Signal Processing 12, 1 (2018), 168-179.
[49]
PASZKE, A., GROSS, S., CHINTALA, S., CHANAN, G., YANG, E., DEVITO, Z., LIN, Z., DESMAISON, A., ANTIGA, L., AND LERER, A. Automatic differentiation in pytorch.
[50]
POLGREEN, L. 84 children are killed by medicine in nigeria, 2009.
[51]
POTYRAILO, R. A., NAGRAJ, N., TANG, Z., MONDELLO, F. J., SURMAN, C., AND MORRIS, W. Battery-free radio frequency identification (rfid) sensors for food quality and safety. Journal of agricultural and food chemistry 60, 35 (2012), 8535- 8543.
[52]
PU, Q., JIANG, S., GOLLAKOTA, S., AND PATEL, S. Whole-home gesture recognition using wireless signals. In ACM MobiCom (2013).
[53]
PU, Y., GAN, Z., HENAO, R., YUAN, X., LI, C., STEVENS, A., AND CARIN, L. Variational autoencoder for deep learning of images, labels and captions. In Advances in neural information processing systems (2016), pp. 2352-2360.
[54]
PUREFOY, C. Poisoned medicine kills dozens of children in nigeria, 2008.
[55]
RADFORD, A., METZ, L., AND CHINTALA, S. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015).
[56]
RAHMAN, T., ADAMS, A. T., SCHEIN, P., JAIN, A., ERICKSON, D., AND CHOUDHURY, T. Nutrilyzer: A mobile system for characterizing liquid food with photoacoustic effect. In Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM (2016), ACM, pp. 123- 136.
[57]
RAHMAN, T., ADAMS, A. T., SCHEIN, P., JAIN, A., ERICKSON, D., AND CHOUDHURY, T. Nutrilyzer: A mobile system for characterizing liquid food with photoacoustic effect. In Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM (New York, NY, USA, 2016), SenSys '16, ACM, pp. 123-136.
[58]
RUCH, P., HU, R., CAPUA, L., TEMIZ, Y., PAREDES, S., LOPEZ, A., BARROSO, J., COX, A., NAKAMURA, E., AND MATSUMOTO, K. A portable potentiometric electronic tongue leveraging smartphone and cloud platforms. pp. 1-3.
[59]
SMARTRAC GROUP. Smartrac Shortdipole Inlay. www.smartrac-group.com.
[60]
SRIDHAR, M., AND REDDY, C. R. Surface tension of polluted waters and treated wastewater. Environmental Pollution Series B, Chemical and Physical 7, 1 (1984), 49-69.
[61]
SUN, C., AND TRUEMAN, C. W. Unconditionally stable crank-nicolson scheme for solving two-dimensional maxwell's equations. Electronics Letters 39, 7 (April 2003), 595-597.
[62]
TAN, E. L., NG, W. N., SHAO, R., PERELES, B. D., AND ONG, K. G. A wireless, passive sensor for quantifying packaged food quality. Sensors 7, 9 (2007), 1747-1756.
[63]
TSE, D., AND VISWANATH, P. Fundamentals of wireless communication. Cambridge university press, 2005.
[64]
ULABY, F. T., MICHIELSSEN, E., AND RAVAIOLI, U. Fundamentals of applied electromagnetics 6e. Boston, Massachussetts: Prentice Hall (2010).
[65]
VASILESCU, A., AND MARTY, J.-L. Electrochemical aptasensors for the assessment of food quality and safety. TrAC 79 (2016), 60-70.
[66]
WANG, J., XIONG, J., CHEN, X., JIANG, H., BALAN, R., AND FANG, D. Tagscan: Simultaneous target imaging and material identification with commodity rfid devices. ACM MobiCom (2017).
[67]
WANG, X., GAO, L., AND MAO, S. Csi phase fingerprinting for indoor localization with a deep learning approach. IEEE Internet of Things Journal 3, 6 (2016), 1113-1123.
[68]
WANG, X., GAO, L., MAO, S., AND PANDEY, S. Csi-based fingerprinting for indoor localization: A deep learning approach. IEEE Transactions on Vehicular Technology 66, 1 (2016), 763-776.
[69]
WANG, X., WANG, X., AND MAO, S. Cifi: Deep convolutional neural networks for indoor localization with 5 ghz wi-fi. In 2017 IEEE International Conference on Communications (ICC) (2017), IEEE, pp. 1-6.
[70]
WANG, Y. In Flint, Mich., there's so much lead in children's blood that a stat of emergency is declared. The Washington Post, 2015. https://www.washingtonpost.com/news/morning-mix/wp/2015/12/15/toxic-water-soaring-lead-levels-in-childrens-blood-create-state-of-emergency-in-flint-mich/.
[71]
WEI, M., HUANG, S., WANG, J., LI, H., YANG, H., AND WANG, S. The study of liquid surface waves with a smartphone camera and an image recognition algorithm. European Journal of Physics 36, 6 (2015), 065026.
[72]
WU, S.-Y., YANG, C., HSU, W., AND LIN, L. 3d-printed microelectronics for integrated circuitry and passive wireless sensors. Microsystems & Nanoengineering 1 (2015), 15013.
[73]
XIONG, J., AND JAMIESON, K. ArrayTrack: a fine-grained indoor location system. In Usenix NSDI (2013).
[74]
YANZI, Z., YIBO, Z., BEN, Y. Z., AND HAITAO, Z. Reusing 60ghz radios for mobile radar imaging. In ACM MobiCom (2015).
[75]
YEO, H.-S., FLAMICH, G., SCHREMPF, P., HARRIS-BIRTILL, D., AND QUIGLEY, A. Radarcat: Radar categorization for input & interaction. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (2016), ACM, pp. 833-841.
[76]
YOON, J.-Y., AND KIM, B. Lab-on-a-chip pathogen sensors for food safety. Sensors 12, 8 (2012), 10713-10741.
[77]
YUE, S., AND KATABI, D. Liquid testing with your smartphone. In Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services (2019), ACM, pp. 275- 286.
[78]
ZHANG, J., AND TIAN, G. Y. Uhf rfid tag antenna-based sensing for corrosion detection & characterization using principal component analysis. IEEE TAP 64, 10 (2016), 4405-4414.
[79]
ZHAO, M., TIAN, Y., ZHAO, H., ALSHEIKH, M. A., LI, T., HRISTOV, R., KABELAC, Z., KATABI, D., AND TORRALBA, A. Rf-based 3d skeletons. In Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication (2018), ACM, pp. 267-281.
[80]
ZHAO, M., YUE, S., KATABI, D., JAAKKOLA, T. S., AND BIANCHI, M. T. Learning sleep stages from radio signals: a conditional adversarial architecture. In International Conference on Machine Learning (2017), pp. 4100-4109.

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  • (2024)ZenseTag: An RFID assisted Twin-Tag Single Antenna COTS Sensor InterfaceProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699342(336-350)Online publication date: 4-Nov-2024
  • (2024)ZenseTag: An RFID assisted Twin-Tag Single Antenna COTS Sensor InterfaceProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3698850(1757-1759)Online publication date: 4-Dec-2024
  • (2023)Contactless Material Identification with Millimeter Wave VibrometryProceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services10.1145/3581791.3596850(475-488)Online publication date: 18-Jun-2023
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    cover image Guide Proceedings
    NSDI'20: Proceedings of the 17th Usenix Conference on Networked Systems Design and Implementation
    February 2020
    1129 pages
    ISBN:9781939133137

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    • amazon: amazon
    • Google Inc.
    • NSF
    • Microsoft: Microsoft

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    Published: 25 February 2020

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    • (2024)ZenseTag: An RFID assisted Twin-Tag Single Antenna COTS Sensor InterfaceProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699342(336-350)Online publication date: 4-Nov-2024
    • (2024)ZenseTag: An RFID assisted Twin-Tag Single Antenna COTS Sensor InterfaceProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3698850(1757-1759)Online publication date: 4-Dec-2024
    • (2023)Contactless Material Identification with Millimeter Wave VibrometryProceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services10.1145/3581791.3596850(475-488)Online publication date: 18-Jun-2023
    • (2021)RF-rayProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34781155:3(1-24)Online publication date: 14-Sep-2021

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