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Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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CALS: Channel State Information Auto-Labeling System for Large-scale Deep Learning-based Wi-Fi Sensing

딥러닝 기반 Wi-Fi 센싱 시스템의 효율적인 구축을 위한 지능형 데이터 수집 기법

  • Received : 2022.07.24
  • Accepted : 2022.09.01
  • Published : 2022.09.30
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Abstract

Wi-Fi Sensing, which uses Wi-Fi technology to sense the surrounding environments, has strong potentials in a variety of sensing applications. Recently several advanced deep learning-based solutions using CSI (Channel State Information) data have achieved high performance, but it is still difficult to use in practice without explicit data collection, which requires expensive adaptation efforts for model retraining. In this study, we propose a Channel State Information Automatic Labeling System (CALS) that automatically collects and labels training CSI data for deep learning-based Wi-Fi sensing systems. The proposed system allows the CSI data collection process to efficiently collect labeled CSI for labeling for supervised learning using computer vision technologies such as object detection algorithms. We built a prototype of CALS to demonstrate its efficiency and collected data to train deep learning models for detecting the presence of a person in an indoor environment, showing to achieve an accuracy of over 90% with the auto-labeled data sets generated by CALS.

Wi-Fi가 거의 모든 곳에서 사용이 가능한 환경이 도래하면서 Wi-Fi 기반의 센싱 시스템의 활용가능성에 대한 학계의 주목과 함께 활발한 연구가 진행되고 있다. 최근에는 채널 상태 정보(CSI)를 활용한 딥러닝 기술의 비약적 발달로 높은 감지 성능을 달성하고 있다. 하지만, 새로운 대상 도메인에 적용하기 위해서는 명시적인 데이터 수집 및 모델 재학습 과정의 값비싼 적응 노력 없이는 여전히 실질적으로는 사용하기가 어렵다. 본 연구에서는 딥러닝 기반의 Wi-Fi 센싱 시스템을 위한 훈련데이터 수집 및 레이블링을 자동으로 진행하는 CSI 자동 레이블링 시스템(CALS)를 제안한다. 제안 시스템은 CSI 데이터 수집 과정에서 컴퓨터 비전 기술을 함께 활용하여, 지도학습용으로 수집된 CSI 데이터에 대한 레이블링을 자동으로 수행토록 하였다. CALS의 효율성을 보이기 위해 라즈베리파이를 이용하여 프로토타입 시스템을 구현하고, 실내 환경에서의 사람 존재 감지를 수행하는 3가지 모델에 대해 학습과 평가를 진행하였다. 자동 수집된 데이터를 진행하여 학습을 활용하는 방식으로 실시간 데이터에 대해 평가를 진행했을 때 90% 이상의 높은 정확도를 달성하였다.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1A2C1013308).

References

  1. J. Wang, Y. Zhao, X. Fan, Q. Gao, X. ma and H. Wang, "Device-free identification using intrinsic CSI features," IEEE transactions on vehicular technology, vol.67, no.9, pp.8571-8581, 2018. DOI: 10.1109/TVT.2018.2853185
  2. H. Mo and S. Kim, "A deep learning-based human identification system with wi-fi csi data augmentation," IEEE Access, vol.9, pp.91913-91920, 2021. DOI: 10.1109/ACCESS.2021.3092435
  3. W. Wang, A. X. Liu, M. Shahzad, K. Ling and S. Lu, "Device-free human activity recognition using commercial WiFi devices," IEEE Journal on Selected Areas in Communications, vol.35, no.5, pp.1118-1131, 2017. DOI: 10.1109/JSAC.2017.2679658
  4. 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. DOI: 10.1109/TMC.2018.2878233
  5. D. Wang, J. Yang, W. Cui, L. Xie and S. Sun, "Multimodal CSI-based human activity recognition using GANs," IEEE Internet of Things Journal, vol.8, no.24, pp.17345-17355, 2021. DOI: 10.1109/JIOT.2021.3080401
  6. S. Palipana, P. Agrawal and D. Pesch, "Channel state information based human presence detection using non-linear techniques," Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, pp.177-186, 2016. DOI: 10.1145/2993422.2993579
  7. R. Zhou, X. Lu, P. Zhao and J. Chen, "Devicefree presence detection and localization with SVM and CSI fingerprinting," IEEE Sensors Journal, vol.17, no.23, pp.7990-7999, 2017. DOI: 10.1109/JSEN.2017.2762428
  8. K. Wu, J. Xiao, Y. Yi, D. Chen, X. Luo and L. M. Ni, "CSI-based indoor localization," IEEE Transactions on Parallel and Distributed Systems, vol.24, no.7, pp.1300-1309, 2012. DOI: 10.1109/TPDS.2012.214
  9. X. Wang, L. Gao, S. Mao and S. Pandey, "CSI-based fingerprinting for indoor localization: A deep learning approach," IEEE Transactions on Vehicular Technology, vol,66, no.1, pp.763-776, 2016. DOI: 10.1109/TVT.2016.2545523
  10. Q. Li, X. Liao, M. Liu and S. Valaee, "Indoor localization based on CSI fingerprint by siamese convolution neural network," IEEE Transactions on Vehicular Technology, vol.70, no.11 12168- 12173, 2021. DOI: 10.1109/TVT.2021.3107936
  11. Y. Zheng, Y. Zhang, K. Qian, G. Zhang, Y. Liu, C. Wu and Z. Yang, "Zero-effort cross-domain gesture recognition with Wi-Fi," Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, pp.313-325, 2019. DOI: 10.1145/3307334.3326081
  12. J. Zhang, Z. Tang, M. Li, D. Fang, P. Nurmi and Z. Wang, "CrossSense: Towards cross-site and large-scale WiFi sensing," Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, pp.305-320, 2018. DOI: 10.1145/3241539.3241570
  13. Y. Zhang, P. Sun, Y. Jiang, D. Yu, F. Weng, Z. Yuan, P. Luo, W. Liu and X. Wang, "Bytetrack: Multi-object tracking by associating every detection box," arXiv preprint arXiv:2110.06864, 2021.
  14. F. Gringoli, M. Schulz, J. Link and M. Hollick, "Free your CSI: A channel state information extraction platform for modern Wi-Fi chipsets," Proceedings of the 13th International Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization, pp.21-28, 2019. DOI: 10.1145/3349623.3355477
  15. Y. Ma, G. Zhou and S. Wang, "WiFi sensing with channel state information: A survey." ACM Computing Surveys (CSUR), vol.52, no.3, pp.1-36, 2019. DOI: 10.1145/3310194