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Appliance fingerprinting using sound from power supply

Published: 12 September 2020 Publication History

Abstract

Recognizing the working appliances is of great importance for smart environment to provide services including energy conservation, user activity recognition, fire hazard prevention, etc. There have been many methods proposed to recognize appliances by analyzing the power voltage, current, electromagnetic emissions, vibration, light, and sound from appliances. Among these methods, measuring the power voltage and current requires installing intrusive sensors to each appliance. Measuring the electromagnetic emissions and vibration requires sensors to be attached or close (e.g., < 15cm) to the appliances. Methods relying on light are not universally applicable since only part of appliances generate light. Similarly, methods using sound relying on the sound from motor vibration or mechanical collision so are not applicable for many appliances. As a result, existing methods for appliance fingerprinting are intrusive, have high deployment cost, or only work for part of appliances. In this work, we proposed to use the inaudible high-frequency sound generated by the switching-mode power supply (SMPS) of the appliances as fingerprints to recognize appliances. Since SMPS is widely adopted in home appliances, the proposed method can work for most appliances. Our preliminary experiments on 18 household appliances (where 10 are of the same models) showed that the recognition accuracy achieves 97.6%.

References

[1]
Y. Kim, T. Schmid, Z. M. Charbiwala, and M. B. Srivastava. Viridiscope: design and implementation of a fine grained power monitoring system for homes. In Proceedings of the 11th international conference on Ubiquitous computing, pages 245--254, 2009.
[2]
D. Liu and J. Jiang. High frequency characteristic analysis of emi filter in switch mode power supply (smps). In 2002 IEEE 33rd Annual IEEE Power Electronics Specialists Conference. Proceedings (Cat. No. 02CH37289), volume 4, pages 2039--2043. IEEE, 2002.
[3]
N. Pathak, M. A. A. H. Khan, and N. Roy. Acoustic based appliance state identifications for fine-grained energy analytics. In 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 63--70. IEEE, 2015.
[4]
A. Schoofs, A. Guerrieri, D. T. Delaney, G. M. O'Hare, and A. G. Ruzzelli. Annot: Automated electricity data annotation using wireless sensor networks. In 2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pages 1--9. IEEE, 2010.
[5]
Statista. Household appliance retail sales value worldwide in 2014, 2018 and 2023, by category. http://https://www.statista.com/statistics/1033516, 2019.
[6]
Z. C. Taysi, M. A. Guvensan, and T. Melodia. Tinyears: spying on house appliances with audio sensor nodes. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, pages 31--36, 2010.

Cited By

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  • (2023)LeakThief: Stealing the Behavior Information of Laptop via Leakage Current2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)10.1109/SECON58729.2023.10287495(186-194)Online publication date: 11-Sep-2023
  • (2022)Inaudible Sounds From Appliances as Anchors: A New Signal of Opportunity for Indoor LocalizationIEEE Sensors Journal10.1109/JSEN.2022.321109822:23(23267-23276)Online publication date: 1-Dec-2022
  • (2021)CapSpeaker: Injecting Voices to Microphones via CapacitorsProceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security10.1145/3460120.3485389(1915-1929)Online publication date: 13-Nov-2021

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Published In

cover image ACM Conferences
UbiComp/ISWC '20 Adjunct: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers
September 2020
732 pages
ISBN:9781450380768
DOI:10.1145/3410530
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2020

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Author Tags

  1. SMPS
  2. acoustic signals
  3. appliance fingerprinting

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  • Poster

Funding Sources

  • the Joint Key Project of the NSFC
  • National Key R&D Program of China
  • Startup Fund for Youngman Research at SJTU
  • NSFC under Grants

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UbiComp/ISWC '20

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

View all
  • (2023)LeakThief: Stealing the Behavior Information of Laptop via Leakage Current2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)10.1109/SECON58729.2023.10287495(186-194)Online publication date: 11-Sep-2023
  • (2022)Inaudible Sounds From Appliances as Anchors: A New Signal of Opportunity for Indoor LocalizationIEEE Sensors Journal10.1109/JSEN.2022.321109822:23(23267-23276)Online publication date: 1-Dec-2022
  • (2021)CapSpeaker: Injecting Voices to Microphones via CapacitorsProceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security10.1145/3460120.3485389(1915-1929)Online publication date: 13-Nov-2021

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