Houbing Song is an Assistant Professor of Electrical Engineering and Computer Science (EE&CS) and the Director of the Security and Optimization for Networked Globe Laboratory (SONG Lab, www.SONGLab.us) at Embry-Riddle Aeronautical University (ERAU), Daytona Beach, FL. He received the Ph.D. degree in electrical engineering from the University of Virginia, Charlottesville, VA, in August 2012. He has served as an Associate Technical Editor for IEEE Communications Magazine (2017-present), an Associate Editor for IEEE Internet of Things Journal (2020-present) and IEEE Journal on Miniaturization for Air and Space Systems (J-MASS) (2020-present), and a Guest Editor for IEEE Journal on Selected Areas in Communications (J-SAC), IEEE Internet of Things Journal, IEEE Transactions on Industrial Informatics, IEEE Sensors Journal, IEEE Transactions on Intelligent Transportation Systems, and IEEE Network. He is the editor of six books, including Big Data Analytics for Cyber-Physical Systems: Machine Learning for the Internet of Things, Elsevier, 2019, Smart Cities: Foundations, Principles and Applications, Hoboken, NJ: Wiley, 2017, Security and Privacy in Cyber-Physical Systems: Foundations, Principles and Applications, Chichester, UK: Wiley-IEEE Press, 2017, Cyber-Physical Systems: Foundations, Principles and Applications, Boston, MA: Academic Press, 2016, and Industrial Internet of Things: Cybermanufacturing Systems, Cham, Switzerland: Springer, 2016. He is the author of more than 100 articles. His research interests include cyber-physical systems, cybersecurity and privacy, internet of things, edge computing, AI/machine learning, big data analytics, unmanned aircraft systems, connected vehicle, smart and connected health, and wireless communications and networking. His research has been featured by popular news media outlets, including IEEE GlobalSpec's Engineering360, USA Today, U.S. News & World Report, Fox News, Association for Unmanned Vehicle Systems International (AUVSI), Forbes, WFTV, and New Atlas.
Dr. Song is a senior member of ACM, an ACM Distinguished Speaker, the faculty advisor of ERAU chapter of ACM’s Upsilon Pi Epsilon (UPE), and a senior member of IEEE. Dr. Song was a recipient of the Best Paper Award from the 12th IEEE International Conference on Cyber, Physical and Social Computing (CPSCom-2019), the Best Paper Award from the 2nd IEEE International Conference on Industrial Internet (ICII 2019), the Best Paper Award from the 19th IEEE/AIAA Integrated Communication, Navigation and Surveillance technologies (ICNS 2019) Conference, the Best Paper Award from the 6th IEEE International Conference on Cloud and Big Data Computing (CBDCom 2020), and the Best Paper Award from the 15th International Conference on Wireless Algorithms, Systems, and Applications (WASA 2020).
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AI/Machine Learning for Internet of Dependable and Controllable Things
The Internet of Things (IoT) has the potential to enable a variety of applications and services. However, it also presents grand challenges in security, safety, and privacy. Therefore, there is a...
Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends
There is an increasing need to fly Unmanned Aircraft Systems (UAS, commonly known as drones) in the airspace to perform missions of vital importance to national security and defense, emergency...
Data-Efficient Machine Learning
Most research on machine learning has focused on learning from massive amounts of data resulting in large advancements in machine learning capabilities and applications. However, many...
Real-Time Machine Learning for Quickest Detection
Quickest detection, which refers to real-time detection of abrupt changes in the behavior of an observed signal or time series as quickly as possible after they occur, is essential to enable...
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