skip to main content
10.1145/3384419.3430469acmconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
short-paper

HARaaS: HAR as a service using wifi signal in IoT-enabled edge computing: poster abstract

Published:16 November 2020Publication History

ABSTRACT

Human activity recognition (HAR) is an important component in context awareness IoT applications such smart home, smart building etc. With the proliferation of WiFi-integrated devices, researchers exploit WiFi signals to recognize various human activities. In this work, we introduce a HAR as a Service (HARaaS) model for activity recognition services applied in IoT areas. HARaaS proposes a novel edge computing model in the concept of the Sensing as a Service (S2aaS) architecture to offer accurate and real-time activities recognition services with good energy efficiency. HARaaS distributes the resource-hungry computing workload i.e. training recognition model to edge terminals, and exploits the built-in intelligence of IoT devices. A WiFi-based activity recognition service is designed following the HARaaS architecture, and the lightweight machine learning and deep learning model are incorporated in the service for accurate activity recognition. Experiments are conducted and demonstrate the service achieves an activity recognition accuracy of 95% with extremely low latency and high energy efficiency.

References

  1. Z. Zhou, Z. Yang, C. Wu, L. Shangguan, and Y. Liu, "Omnidirectional coverage for device-free passive human detection," IEEE TPDS, 2013.Google ScholarGoogle Scholar
  2. B. Wei, W. Hu, M. Yang, and C. T. Chou, "From real to complex: enhancing radio-based activity recognition using complex-valued csi," ACM Transactions on Sensor Networks (TOSN), vol. 15, no. 3, p. 35, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. B. Wei et al., "Radio-based device-free activity recognition with radio frequency interference," in International Conference on Information Processing in Sensor Networks. ACM, 2015, pp. 154--165.Google ScholarGoogle Scholar
  4. Y. Ma, G. Zhou, S. Wang, H. Zhao, and W. Jung, "Signfi: Sign language recognition using wifi," Proceedings of Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp), vol. 2, no. 1, pp. 1--21, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Zhang, B. Wei, W. Hu, and S. S. Kanhere, "Wifi-id: Human identification using wifi signal," in International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 2016, pp. 75--82.Google ScholarGoogle Scholar
  6. J. Zhang et al., "Human identification using wifi signal," in International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). IEEE, 2016, pp. 1--2.Google ScholarGoogle Scholar
  7. M. Zhao, F. Adib, and D. Katabi, "Emotion recognition using wireless signals," in Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking. ACM, 2016, pp. 95--108.Google ScholarGoogle Scholar
  8. D. Zhang, Y. Hu, Y. Chen, and B. Zeng, "Breathtrack: Tracking indoor human breath status via commodity wifi," IEEE Internet of Things Journal, 2019.Google ScholarGoogle Scholar
  9. J. Liu, Y. Chen, Y. Wang, X. Chen, J. Cheng, and J. Yang, "Monitoring vital signs and postures during sleep using wifi signals," IEEE Internet of Things Journal, vol. 5, no. 3, pp. 2071--2084, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  10. Y. Gu, J. Zhan, Y. Ji, J. Li, F. Ren, and S. Gao, "Mosense: An rf-based motion detection system via off-the-shelf wifi devices," IEEE Internet of Things Journal, vol. 4, no. 6, pp. 2326--2341, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  11. J. Zhang, W. Xu, W. Hu, and S. S. Kanhere, "Wicare: Towards in-situ breath monitoring," in Proceedings of the 14th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. ACM, 2017, pp. 126--135.Google ScholarGoogle Scholar
  12. H. Zou, J. Yang, Y. Zhou, L. Xie, and C. J. Spanos, "Robust wifi-enabled device-free gesture recognition via unsupervised adversarial domain adaptation," in 2018 27th International Conference on Computer Communication and Networks (ICCCN). IEEE, 2018, pp. 1--8.Google ScholarGoogle Scholar
  13. Z. Chen, L. Zhang, C. Jiang, Z. Cao, and W. Cui, "Wifi csi based passive human activity recognition using attention based blstm," IEEE Transactions on Mobile Computing, vol. 18, no. 11, pp. 2714--2724, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  14. J. Zhang, F. Wu, W. Hu, Q. Zhang, W. Xu, and J. Cheng, "Wienhance: Towards data augmentation in human activity recognition using wifi signal," in Proceedings of International Conference on Mobile Ad-Hoc and Sensor Networks (MSN). IEEE, 2019, pp. 309--314.Google ScholarGoogle Scholar
  15. J. Zhang, F. Wu, B. Wei, Q. Zhang, H. Huang, S. W. Shah, and J. Cheng, "Data augmentation and dense-lstm for human activity recognition using wifi signal," IEEE Internet of Things Journal, pp. 1--1, 2020.Google ScholarGoogle Scholar
  16. C. Luo, J. Wu, J. Li, J. Wang, W. Xu, Z. Ming, B. Wei, W. Li, and A. Y. Zomaya, "Gait recognition as a service for unobtrusive user identification in smart spaces," ACM Transactions on Internet of Things, vol. 1, no. 1, pp. 1--21, 2020.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. HARaaS: HAR as a service using wifi signal in IoT-enabled edge computing: poster abstract

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
      November 2020
      852 pages
      ISBN:9781450375900
      DOI:10.1145/3384419

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 16 November 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • short-paper

      Acceptance Rates

      Overall Acceptance Rate174of867submissions,20%
    • Article Metrics

      • Downloads (Last 12 months)22
      • Downloads (Last 6 weeks)4

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader