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, 2020

Project Team

• Shiwen Mao

• Chao Yang

• Xiangyu Wang

• Xuyu Wang (guraduated Aug. 2018)

• Runze Huang (guraduated Aug. 2018)

• Lingjun Gao (graduated Aug. 2015)

Related Publications (journal & magazine)

• X. Wang, C. Yang, and S. Mao, “On CSI-based vital sign monitoring using commodity WiFi,” ACM Transactions on Computing for Healthcare, vol.1, no.3, pp.12:1-12:27, Apr. 2020. DOI: 10.1145/3377165.

• S. Duan, W. Yang, X. Wang, S. Mao, and Y. Zhang, “Forecasting of grain pile temperature from meteorological factors using machine learning,” IEEE Access Journal, Special Section on New Technologies for Smart Farming 4.0: Research Challenges and Opportunities, vol.7, no.1, pp.130721-130733, Dec. 2019. DOI: 10.1109/ACCESS.2019.2940266.

• X. Wang, J. Zhang, Z. Yu, S. Mao, S.C.G. Periaswamy, and J. Patton, “On remote temperature sensing using commercial UHF RFID tags,” IEEE Internet of Things Journal, to appear. DOI: 10.1109/JIOT.2019.2941023.

• J. Zhang, S.C.G. Periaswamy, S. Mao, and J. Patton, “Standards for passive UHF RFID,” ACM GetMobile, to appear.

• Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, “Application of machine learning in wireless networks: Key technologies and open issues,” IEEE Communications Surveys and Tutorials, to appear. DOI: 10.1109/COMST.2019.2924243.

• X. Wang, Z. Yu, and S. Mao, “Indoor localization using magnetic and light sensors with smartphones: A deep LSTM approach,” Springer Mobile Networks and Applications (MONET) Journal, Special Issue on Towards Future Ad Hoc Networks: Technologies and Applications, to appear. DOI: 10.1007/s11036-019-01302-x.

• X. Wang, X. Wang, and S. Mao, “Deep convolutional neural networks for indoor localization with CSI images,” IEEE Transactions on Network Science and Engineering, Special Issue on Network Science for Internet of Things (IoT), to appear. DOI: 10.1109/TNSE.2018.2871165.

• M. Chen, Y. Qian, J. Chen, K. Hwang, S. Mao, and L. 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.

• S. Duan, W. Yang, X. Wang, S. Mao, and Y. Zhang, “Forecasting of grain pile temperature from meteorological factors using machine learning,” IEEE Access Journal, Special Section on New Technologies for Smart Farming 4.0: Research Challenges and Opportunities, vol.7, no.1, pp.130721-130733, Dec. 2019. DOI: 10.1109/ACCESS.2019.2940266.

• X. Chen, H. Zhang, C. Wu, S. Mao, Y. Ji, and M. Bennis, “Optimized computation offloading performance in vir-tual edge computing systems via deep reinforcement learning,” IEEE Internet of Things Journal, Special Issue on Emerging Computing Offloading for IoTs: Architectures, Technologies, and Applications, vol.6, no.3, pp.4005-4018, June 2019. DOI: 10.1109/JIOT.2018.2876279.

• C. Yang, X. Wang, and S. Mao, “Unsupervised detection of apnea using commodity RFID tags with a recurrent variational autoencoder,” IEEE Access Journal, Special Section on Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications, vol.7, no.1, pp.67526-67538, June 2019. DOI: 10.1109/ACCESS.2019.2918292.

• L. Hu, Y. Qian, J. Chen, X. Shi, J. Zhang, and S. Mao, “Photo crowdsourcing based privacy-protected healthcare,” IEEE Transactions on Sustainable Computing, vol.4, no.2, pp.168-177, Apr./June 2019. DOI: 10.1109/TSUSC.2017.2705181.

• K. Xiao, S. Mao, and J.K. Tugnait, “TCP-Drinc: Model free smart congestion control based on deep reinforcement learning,” IEEE ComSoc MMTC Communications – Frontiers, Special Issue on artificial intelligence and machine learning for network resource management and data analytics, vol.14, no.3, pp.15-19, May 2019.

• Y. Sun, M. Peng, S. Mao, and S. Yan, “Hierarchical radio resource allocation for network slicing in fog radio ac-cess networks,” IEEE Transactions on Vehicular Technology, vol.68, no.4, pp.3866-3881, Apr. 2019. DOI: 10.1109/TVT.2019.2896586.

• Y. Sun, M. Peng, and S. Mao, “Deep reinforcement learning based mode selection and resource management for green fog radio access networks,” IEEE Internet of Things Journal, Special Issue on AI-Enabled Cognitive Communications and Networking for IoT, vol.6, no.2, pp.1960-1971, Apr. 2019. DOI: 10.1109/JIOT.2018.2871020.

• M. Feng and S. Mao, “Dealing with limited backhaul in millimeter wave systems: A deep reinforcement learn-ing approach,” IEEE Communications, Feature Topic on Applications of Artificial Intelligence in Wireless Communications, vol.57, no.3, pp.50-55, Mar. 2019. DOI: 10.1109/MCOM.2019.1800565.

• K. Xiao, S. Mao, and J.K. Tugnait, “TCP-Drinc: Model free smart congestion control based on deep reinforce-ment learning,” IEEE Access Journal, Special Section on Artificial Intelligence and Cognitive Computing for Com-munications and Networks, vol.7, no.1, pp.11892-11904, Jan. 2019. DOI: 10.1109/ACCESS.2019.2892046.

• Y. Xue, P. Zhou, S. Mao, D. Wu, and Y. Zhou, “Pure-exploration bandits for channel probing in mission-critical wireless communications,” IEEE Transactions on Vehicular Technology, vol.67, no.11, pp. 10995-11007, Nov. 2018. DOI: 10.1109/TVT.2018.2866198.

• X. Wang, X. Wang, and S. Mao, “RF sensing for Internet of Things: A general deep learning framework,” IEEE Communications, Feature Topic on Exploring Caching, Communications, Computing and Security for the Emerg-ing Smart Internet of Things, vol.56, no.9, pp.62-67, Sept. 2018. DOI: 10.1109/MCOM.2018.1701277.

• Z. Chang, L. Lei, Z. Zhou, S. Mao, and T. Ristaniemi, “Learn to cache: Machine learning for network edge caching in the big data era,” IEEE Wireless Communications, Special Issue on Content-Centric Collaborative Edge Caching in 5G Mobile Internet, vol.25, no.3, pp.28-35, June 2018. DOI: 10.1109/MWC.2018.1700317.

• J. Zhao, Q. Liu, X. Wang, and S. Mao, “Scheduled sequential compressed spectrum sensing for wideband cognitive radios,” IEEE Transactions on Mobile Computing, vol.17, no.4, pp.913-926, Apr. 2018. DOI: 10.1109/TMC.2017.2744621.

• J. Zhao, Q. Liu, X. Wang, and S. Mao, “Scheduling of collaborative sequential compressed sensing over wide spectrum band,” IEEE/ACM Transactions on Networking, vol.26, no.1, pp.492-505, Feb. 2018. DOI: 10.1109/TNET.2017.2787647.

• X. Wang, S. Mao, and M.X. Gong, “An overview of 3GPP cellular vehicle-to-everything standards,” ACM GetMobile: Mobile Computing and Communications Review, vol.21, no.3, pp.19-25, Sept. 2017. DOI: 10.1145/3161587.3161593.

• X. Wang, C. Yang, and S. Mao, “TensorBeat: Tensor decomposition for monitoringXuyu multi-person breathing beats with commodity WiFi,” ACM Transactions on Intelligent Systems and Technology, Special Issue on Data-driven Intelligence for Wireless Networking, vol.9, no.1, Article 8, pp.8:1-8:27, Sept. 2017. DOI: 10.1145/3078855.

• X. Wang, L. Gao, and S. 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.

• M. Chen, J. Yang, Y. Hao, S. Mao, and K. 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.

• 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, Jan. 2017. DOI: 10.1109/TVT.2016.2545523. (The 2018 IEEE ComSoc MMTC Best Journal Paper Award) (The IEEE ComSoc Best Readings in Machine Learning in Communications)

• X. Wang, L. Gao, and S. 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)

• M. Chen, Y. Zhang, Y. Li, S. Mao, and V.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.

• Y. Zhang, M. Chen, S. Mao, L. Hu, and V.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)

• C. Yang, X. Wang, and S. Mao, “RFID-based driving fatigue detection,” in Proc. IEEE GLOBECOM 2019, Waikoloa, HI, Dec. 2019. (The IEEE GLOBECOM 2019 Best Paper Award)

• P. Hu, W. Yang, X. Wang, and S. Mao, “MiFi: Device-free wheat mildew detection using off-the-shelf WiFi devices,” in Proc. IEEE GLOBECOM 2019, Waikoloa, HI, Dec. 2019.

• S. Duan, W. Yang, X. Wang, S. Mao, and Y. Zhang, “Grain pile temperature forecasting from weather factors: A support vector regression approach,” invited paper, in Proc. IEEE/CIC ICCC 2019, Changchun, China, Aug. 2019.

• C. Yang, X. Wang, and S. Mao, “SparseTag: High-precision backscatter indoor localization with sparse RFID tag arrays,” in Proc. IEEE SECON 2019, Boston, MA, June 2019.

• X. Wang, X. Wang, S. Mao, J. Zhang, S.C.G. Periaswamy, and J. Patton, “DeepMap: Deep Gaussian Process for indoor radio map construction and location estimation,” in Proc. IEEE GLOBECOM 2018, Abu Dhabi, United Arab Emirates, Dec. 2018.

• C. Yang, X. Wang, and S. Mao, “AutoTag: Recurrent vibrational autoencoder for unsupervised apnea detec-tion with RFID tags,” in Proc. IEEE GLOBECOM 2018, Abu Dhabi, United Arab Emirates, Dec. 2018.

• J. Zhang, Z. Yu, X. Wang, Y. Lyu, S. Mao, S. CG Periaswamy, J. Patton, X. Wang, “RFHUI: An intuitive and easy-to-operate human-UAV interaction system for controlling a UAV in a 3D space,” in Proc. EAI MobiQuitous 2018, New York City, NY, Nov. 2018, pp.69-76.

• X. Chen, H. Zhang, C. Wu, S. Mao, Y. Ji, and M. Bennis, “Performance optimization in mobile-edge computing via deep reinforcement learning,” in Proc. IEEEE VTC-Fall 2018, Chicago, IL, Aug. 2018.

• W. Yang, X. Wang, S. Cao, H. Wang, and S. Mao, “Multi-class wheat moisture detection with 5GHz Wi-Fi: A deep LSTM approach,” in Proc. ICCCN 2018, Huangzhou, China, July/Aug. 2018.

• W. Yang, X. Wang, A. Song, and S. Mao, “Wi-Wheat: Contact-free wheat moisture detection using commodity WiFi,” in Proc. IEEE ICC 2018, Kansas City, MO, May 2018.

• X. Wang, Z. Yu, and S. Mao, “DeepML: Deep LSTM for Indoor Localization with Smartphone Magnetic and Light Sensors,” in Proc. IEEE ICC 2018, Kansas City, MO, May 2018.

• X. Wang, C. Yang, and S. Mao, “ResBeat: Resilient breathing beats monitoring with online bimodal CSI data,” Proc. IEEE GLOBECOM 2017, Singapore, Dec. 2017.

• X. Wang, X. Wang, and S. Mao, “ResLoc: Deep residual sharing learning for indoor localization with CSI tensors,” in Proc. IEEE PIMRC 2017, Montreal, QC, Canada, Oct. 2017. (The IEEE PIMRC 2017 Best Student Paper Award)

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

• X. Wang, R. Huang, and S. Mao, “Demo Abstract: SonarBeat: Sonar phase for breathing beat monitoring with smartphones,” in Proc. IEEE SECON 2017, San Diego, CA, June 2017. (The IEEE SECON 2017 Best Demo Award)

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

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

• M. Chen, Y. Hao, S. Mao, D. Wu, and Y. Zhou, “User intent-oriented video QoE with emotion detection networking,” in Proc. IEEE GLOBECOM 2016, Washington DC, Dec. 2016, pp.1-6.

• Y. Xue, P. Zhou, T. Jiang, S. Mao, and X. Huang, “Distributed learning for multi-channel selection in wireless network monitoring,” in Proc. IEEE SECON 2016, London, UK, June 2016, pp.1-9.

• X. Wang, L. Gao, and S. 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.

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

• X. Wang, H. Zhou, S. Mao, S. Pandey, P. Agrawal, and D. Bevly, “Mobility improves LMI-based cooperative indoor localization,” in Proc. IEEE WCNC 2015, New Orleans, LA, Mar. 2015, pp.2215-2220.

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

Related Publications (book chapter)

• X. Wang and S. Mao, “Respiration monitoring using smartphone sonar,” Chapter X in Contactless Vital Signs Monitoring, W. Wang and X. Wang (editors). Amsterdam, Netherlands: Elsevier, 2020.

• C. Yang, X. Wang, and S. Mao, “RFID based unsupervised apnea detection,” Chapter X in Intelligent IoT Systems in Personalized Health Care, A.K. Sangaiah and S.C. Mukhopadhyay (editors). Amsterdam, Netherlands: Elsevier, 2020.

• X. Wang and S. Mao, “Deep learning for indoor localization based on bi-modal CSI data,” Chapter 10 in Application of Machine Learning in Wireless Communications, pp.343-369, R. He and Z. Ding (editors). London, UK: The Institution of Engineering and Technology (IET), 2019.

• X. Wang, C. Yang, and S. Mao, “Sleep monitoring using WiFI signal,” Chapter XXX in Encyclopedia of Wireless Networks, pp.XXX-XXX, X. Shen, X. Lin and K. Zhang (editors). Cham, Switzerland: Springer, 2019.

Patents

• S. Mao, X. Wang, and C. Yang, “RFID-based Driving Fatigue Detection ("NodTrack"),” US Provisional Patent Application, Application Number: 62/855,303, May 31, 2019.

• S. Mao, X. Wang, and X. Wang, “ResLoc: Deep residual sharing learning for indoor localization with CSI tensors,” US Provisional Patent Application, Application Number: 62/741,723, Oct. 5, 2018.

• S. Mao, X. Wang, R. Huang, “SonarBeat: Sonar phase for breathing beat monitoring with smartphones,” US Provisional Patent Application, Application Number: 62/519,336, June 14, 2017.

• S. Mao, X. Wang, and C. Yang, “PhaseBeat: Exploiting CSI phase data for vital sign monitoring with commodity WiFi devices,” US Provisional Patent Application, Application Number: 62/514,505, June 2, 2017.

• S. Mao, X. Wang, and C. Yang, “TensorBeat: Tensor decomposition for monitoring multi-person breathing beats with commodity WiFi,” US Provisional Patent Application, Application Number: 62/444,598, Jan. 10, 2017, Sept. 4, 2018.

• S. Mao, X. Wang, and L. Gao, “BiLoc: Bi-modal Deep Learning for Indoor Localization with Commodity 5GHz WiFi,” US Provisional Patent Application, Application Number: 62/338,737, May 19, 2016.

Presentations & Tutorials

• S. Mao, “RFID based vital sign monitoring and its application to driving fatigue detection,” Keynote Speech, The 2020 International Conference on Security and Privacy in Digital Economy (SPDE 2020), Quzhou, China, Oct./Nov. 2020.

• W. Wang, G. de Haan, S. Mao, X. Wang, and M. Zhao, “Contactless health monitoring with AI,” Tutorial, IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, June 2020.

• X. Wang and S. Mao, “On RF based vital sign monitoring,” Keynote Speech, International Workshop and Challenge on Computer Vision for Physiological Measurement (CVPM 2020), in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, June 2020.

• S. Mao, “On exploiting wireless data with deep learning for indoor localization,” presentation at the Workshop on Data Science Education in Engineering, the 2020 American Society for Engineering Education Southeastern Section (ASEE-SE) Conference, Auburn, AL, Mar. 2020.

• S. Mao, “RFID based vital sign sensing and its application to driving fatigue detection,” Invited Talk, The 2020 International Conference on Computing, Networking and Communications (ICNC 2020), Big Island, HI, Feb. 2020.

• S. Mao, “RFID based vital sign sensing and its application to driving fatigue detection,” Keynote Speech, The 11th EAI International Conference on Ad Hoc Networks (ADHOCNETS 2019), Queenstown, New Zealand, Nov. 18-21, 2019.

• S. Mao, “Recurrent variational autoencoder for unsupervised apnea detection with RFID tags,” Invited Talk to Auburn University's Honors College students, Aug. 26, 2019.

• S. Mao, “RFID based Internet of Things: New applications and techniques,” Keynote Speech, The 2nd International Workshop on Social Computing and Communications (SCC), Chongqing University of Posts and Telecommunications, Chongqing, China, July 3, 2019.

• S. Mao, “RFID based Internet of Things: New applications and techniques,” IEEE Vehicular Technology Society Distinguished Speaker Seminar, IEEE VTS Shanghai Chapter, Shanghai, China, May 23, 2019.

• S. Mao, “AutoTag: Recurrent variational autoencoder for unsupervised apnea detection with RFID tags,” invited talk in the Lightning Talk Session of IEEE ComSoc Multimedia Communications Technical Committee Meeting at IEEE ICC 2019, Shanghai, China, May 22, 2019.

• S. Mao, “RFID based Internet of Things: New applications and techniques,” Keynote Speech, The 2019 China Cloud Computing and Internet of Things Conference (CCCIoT’19), Chongqing, China, May 14, 2019.

• S. Mao, “Applying deep learning to indoor localization and smart grid problems,” invited Talk, CHESS Cluster Seminar, Auburn University, Auburn, AL, Jan. 31, 2019.

• S. Mao, “On deep learning based indoor localization,” Keynote Speech, The 2019 IEEE Second International Conference on Electronic Information and Communication (ICEICT 2019), Harbin, China, Jan. 2019.

• S. Mao, “RF sensing in the IoT with commodity WiFi,” invited talk, Seminar Series of the Institute for Sensing and Embedded Network Systems Engineering (ISENSE), Florida Atlantic University, Boca Raton, FL, Sept. 13, 2018.

• S. Mao, “On deep learning based indoor localization,” Keynote Speech, The 2018 IEEE International Conference on Smart Internet of Things (SmartIoT 2018), Xi’an, China, Aug. 2018.

• S. Mao, “On deep learning based indoor localization,” Keynote Speech, The 1st International Workshop on Social Computing and Communications (SCC’18), Chongqing University of Posts and Telecommunications, Chongqing, China, July 9, 2018.

• S. Mao, “On CSI based vital sign monitoring in healthcare IoT,” Tutorial, The NSF WiFiUS Summer School on Wireless Challenges in the Internet of Things, Boston, MA, June 12-14, 2018.

• S. Mao, “Indoor fingerprinting revisited: When deep learning meets CSI,” invited talk at the IEEE Education Society Singapore Chapter, Singapore, Dec. 4, 2017.

• S. Mao, “Contact-free vital sign monitoring: When CSI meets the healthcare IoT,” invited talk at the IEEE Mobile Section, Mobile, AL, Oct. 19, 2017.

• S. Mao, “Towards the age of rich wireless applications,” Future Internet Forum 2017, Tianjin University, Tianjin, China, Oct. 15, 2017.

• S. Mao, “Indoor fingerprinting revisited: When deep learning meets CSI,” IEEE Vehicular Technology Society Distinguished Lecture Seminar, IEEE VTS Montreal Chapter, Montreal, Canada, Oct. 10, 2017.

• S. Mao, “RF sensing for vital sign measurement in healthcare Internet of Things,” Keynote Speech, The 17th IEEE International Conference on Computer and Information Technology (IEEE CIT-2017), Helsinki, Finland, Aug. 2017.

• S. Mao, “Fingerprinting based indoor localization revisited: When deep learning meets CSI,” IEEE Vehicular Technology Society Distinguished Lecture Seminar, IEEE VTS Shanghai Chapter, Shanghai, China, July 18, 2017.

• S. Mao, “On contact-free vital sign measurement in healthcare Internet of Things,” IEEE Vehicular Technology Society Distinguished Lecture Seminar, IEEE VTS Nanjing Chapter, Nanjing China, June 29, 2017.

• S. Mao, “On CSI based vital sign monitoring in healthcare IoT,” Distinguished Talk, EAI MobiMedia 2017, Chongqing, China, July 14, 2017.

• S. Mao, “On contact-free vital sign measurement in healthcare Internet of Things,” IEEE Vehicular Technology Society Distinguished Lecture Seminar, IEEE VTS San Diego Chapter & IEEE ComSoc San Diego Chapter, San Diego, CA, June 12, 2017.

• S. Mao, “On contact-free vital sign measurement in healthcare Internet of Things,” Keynote Speech, The 2017 IEEE Workshop on Convergent Internet of Things - On the Synergy of IoT Systems (C-IoT), in conjunction with IEEE International Conference on Communications (ICC) 2017, Paris, France, May 2017.

Press Coverage

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

We acknowledge the generous support from our sponsor

• National Science Foundation

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.