Project Description

RF Sensing in Internet of Things: When Deep Learning Meets CSI Tensor

Internet of Things (IoT) refers to a worldwide network of interconnected uniquely addressable things based on standard communication protocols. In recent years, medical care and healthcare are recognized as one of the most attractive application areas of the IoT, with applications ranging from patient and equipment tracking in medical facilities to health monitoring. The goal of this project is to investigate an IoT healthcare monitoring system, which is likely to continue to grow in popularity as it utilizes IoT in various ways for improving the quality of patient care. This project aims to gain a deep understanding of RF sensing in healthcare IoT by exploiting advanced statistics and learning techniques, and to develop effective algorithms to make healthcare IoT more efficient.

April 15, 2017 ~ March 31, 2019

Project Team

Related Publications (journal & magazine)

  • Xuyu Wang*, Chao Yang*, and Shiwen Mao, “TensorBeat: Tensor decomposition for monitoring multi-person breathing beats with commodity WiFi,” ACM Transactions on Intelligent Systems and Technology, Special Issue on Data-driven Intelligence for Wireless Networking, to appear.

  • Min Chen, Yongfeng Qian, Jing Chen, Kai Hwang, Shiwen Mao, and Long Hu, “Privacy protection and intrusion avoidance for cloudlet-based healthcare big data sharing,” IEEE Transactions on Cloud Computing, Special Issue on Advances of Multimedia Big Data on the Cloud, to appear. DOI: 10.1109/TCC.2016.2617382.

  • Xuyu Wang*, Lingjun Gao*, and Shiwen Mao, “BiLoc: Bi-modality deep learning for indoor localization with 5GHz commodity Wi-Fi,” IEEE Access Journal, Special Section on Cooperative and Intelligent Sensing,vol.5, no.1, pp.4209-4220, Mar. 2017. DOI: 10.1109/ACCESS.2017.2688362.

  • Min Chen, Jun Yang, Yixue Hao, Shiwen Mao, and Kai Hwang, “A 5G cognitive system for healthcare,” MDPI Big Data and Cognitive Computing Journal, vol.1, no.1, pp.1-15, Mar. 2017. DOI: 10.3390/bdcc1010002.

  • Xuyu Wang*, Lingjun Gao*, and Shiwen Mao, “CSI phase fingerprinting for indoor localization with a deep learning approach,” IEEE Internet of Things Journal, vol.3, no.6, pp.1113-1123, Dec. 2016. DOI: 10.1109/JIOT.2016.2558659. (Top 50 most frequently accessed: Mar. 2017; Feb. 2017; Jan. 2017)

  • Xuyu Wang*, Lingjun Gao*, Shiwen Mao, and Santosh Pandey, “CSI-based fingerprinting for indoor localization: A deep learning approach,” IEEE Transactions on Vehicular Technology, vol.66, no.1, pp.763-776, Jan. 2017. DOI: 10.1109/TVT.2016.2545523. (Top 50 most frequently accessed: Mar. 2017-Top 1; Feb. 2017-Top 1; Jan. 2017-Top 3)

  • Min Chen*, Yin Zhang, Yong Li, Shiwen Mao, and Victor C.M. Leung, “EMC: Emotion-aware mobile cloud computing in 5G,” IEEE Network, Special Section on on Mobile Cloud Computing in 5G: Emerging Trends, Issues, and Challenges, vol.29, no.2, pp.32-38, Mar./Apr. 2015. DOI: 10.1109/MNET.2015.7064900.

  • Yin Zhang, Min Chen, Shiwen Mao, L. Hu, and Victor C.M. Leung, “CAP: Crowd activity prediction based on big data analysis,” IEEE Network, Special Issue on Networking for Big Data, vol.28, no.4, pp.52-57, July/Aug. 2014. DOI: 10.1109/MNET.2014.6863132.

Related Publications (conference)

  • Xuyu Wang*, Runze Huang*, and Shiwen Mao, “SonarBeat: Sonar phase for breathing beat monitoring with smartphones,” in Proc. ICCCN 2017, Vancouver, Candada, July/Aug. 2017.

  • Xuyu Wang*, Runze Huang*, and Shiwen Mao, “Demo Abstract: SonarBeat: Sonar phase for breathing beat monitoring with smartphones,” in Proc. IEEE SECON 2017, San Diego, CA, June 2017.

  • Xuyu Wang*, Chao Yang*, and Shiwen Mao, “PhaseBeat: Exploiting CSI phase data for vital sign monitoring with commodity WiFi devices,” in Proc. IEEE ICDCS 2017, Atlanta, GA, June 2017.

  • Xuyu Wang*, Xiangyu Wang*, and Shiwen Mao, “CiFi: Deep convolutional neural networks for indoor localization with 5GHz Wi-Fi,” in Proc. IEEE ICC 2017, Paris, France, May 2017.

  • Min Chen, Yixue Hao, Shiwen Mao, Di Wu, and Yuan Zhou, “User intent-oriented video QoE with emotion detection networking,” in Proc. IEEE GLOBECOM 2016, Washington DC, Dec. 2016, pp.1-6.

  • Yuan Xue, Pan Zhou, Tao Jiang, Shiwen Mao, and Xiaolei Huang, “Distributed learning for multi-channel selection in wireless network monitoring,” in Proc. IEEE SECON 2016, London, UK, June 2016, pp.1-9.

  • Xuyu Wang*, Lingjun Gao*, and Shiwen Mao, “PhaseFi: Phase fingerprinting for indoor localization with a deep learning approach,” in Proc. IEEE GLOBECOM 2015, San Diego, CA, Dec. 2015, pp.1-6.

  • Xuyu Wang*, Lingjun Gao*, Shiwen Mao, and Santosh Pandey, “DeepFi: Deep learning for indoor fingerprinting using channel state information,” in Proc. IEEE WCNC 2015, New Orleans, LA, Mar. 2015, pp.1666-1671.

  • Xuyu Wang*, Hui Zhou*, Shiwen Mao, Santosh Pandey, Prathima Agrawal, and David Bevly, “Mobility improves LMI-based cooperative indoor localization,” in Proc. IEEE WCNC 2015, New Orleans, LA, Mar. 2015, pp.2215-2220.

  • Xuyu Wang*, Shiwen Mao, Santosh Pandey, and Prathima Agrawal, “CA2T: Cooperative antenna arrays technique for pinpoint indoor localization,” invited paper, in Proc. MobiSPC 2014, Niagara Falls, Canada, Aug. 2014, pp.392-399.

Press Coverage

  • Auburn Unversity Homepage, Auburn Plainsman, Yellowhammer News, WSFA TV, WTVM TV

We acknowledge the generous support from our sponsor

This work is supported in part by the U.S. National Science Foundation (NSF) under Grant CNS-1702957. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the foundation.

 Department of Electrical and Computer Engineering | Auburn University | Auburn, Alabama 36849-5201 | (334) 844-1845 | smao@auburn.edu
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