1. Secure and Efficient Internet of Body (IoB) leveraging Human Body as a Wire: Shreyas Sen

2. Information Veracity in Big Data: Jing Gao

3. Reliable Sensing with Unreliable Sensors: Ashraful Alam

Join us online: https://engineering.purdue.edu/crisp/seminar

Recording:

Secure and Efficient Internet of Body (IoB) leveraging Human Body as a Wire

Shreyas Sen

Abstract: Radiative communication using electro-magnetic (EM) fields is the state-of-the-art for connecting wearable and implantable devices enabling prime applications in the fields of connected healthcare, electroceuticals, neuroscience, augmented and virtual reality (AR/VR) and human computer interaction (HCI), forming a subset of Internet of Things called Internet of body (IoB). However, owing to such radiative nature of the traditional wireless communication, EM signals propagate in all directions, inadvertently allowing an eavesdropper to intercept the information. Moreover, since only fraction of the energy is picked up by the intended device, and the need for high carrier frequency compared to information content, wireless communication tends to suffer from poor energy-efficiency (>nJ/bit). Noting that all IoB devices share a common medium, i.e. the human body, utilizing the conductivity of the human body allows low-loss transmission, termed as human body communication (HBC) and improves energy-efficiency. Conventional HBC implementations still suffer from significant radiation compromising physical security and efficiency. Our recent work has developed Electro-Quasistatic Human Body Communication (EQS-HBC), a method for localizing signals within the body using low-frequency transmission, thereby making it extremely difficult for a nearby eavesdropper to intercept critical private data, thus producing a covert communication channel, i.e. the human body as a ‘wire’.

In this talk, I will explore the fundamentals of radio communication around human body to lead to the evolution of EQS-HBC and show recent advancements in the field which has a strong promise to become the future of Body Area Network (BAN), with applications in the fields of HCI, Medical Device Communication and Neuroscience.

Bio: Shreyas Sen is an Associate Professor in ECE, Purdue University and received his Ph.D. degree in ECE, Georgia Tech. Dr. Sen has over 5 years of industry research experience in Intel Labs, Qualcomm and Rambus. His current research interests span mixed-signal circuits/systems and electromagnetics for the Internet of Things (IoT), Biomedical, and Security. Dr. Sen is the inventor of the Electro-Quasistatic Human Body Communication, for which, he is the recipient of the MIT Technology Review top-10 Indian Inventor Worldwide under 35 (MIT TR35 India) Award. Dr. Sen's work has been covered by 250+ news releases worldwide, invited appearance on TEDx Indianapolis, Indian National Television CNBC TV18 Young Turks Program and NPR subsidiary Lakeshore Public Radio. Dr. Sen is a recipient of the NSF CAREER Award 2020, AFOSR Young Investigator Award 2016, NSF CISE CRII Award 2017, Google Faculty Research Award 2017, Intel Outstanding Researcher Award 2021, Intel Labs Quality Award for industry-wide impact on USB-C type, Intel Ph.D. Fellowship 2010, IEEE Microwave Fellowship 2008 and seven best paper awards including IEEE CICC 2019 and IEEE HOST 2017, 2018, 2019 and 2020. Dr. Sen's work was chosen as one of the top-10 papers in the Hardware Security field over the past 6 years (TopPicks 2019). He has co-authored 3 book chapters, over 160 journal and conference papers, and has 15 patents granted/pending. He serves/has served as an Associate Editor for IEEE Design & Test, Executive Committee member of IEEE Central Indiana Section and Technical Program Committee member of DAC, CICC, DATE, ISLPED, ICCAD, ITC, VLSI Design, among others. Dr. Sen is a Senior Member of IEEE.

Information Veracity in Big Data

Jing Gao

The challenges of big data are usually described by four Vs--Volume, Velocity, Variety and Veracity. In this talk, I will focus on the Veracity challenge in big data, which I believe is the challenge that matters the  most. I will present my group's efforts towards the extraction of reliable information from big data in various contexts and applications, including crowdsourcing, Ecommerce catalog validation, knowledge graph validation and fake news detection on social media. I will also briefly discuss other topics related to data quality and trustworthiness that we have investigated, including anomaly detection, bias mitigation and data poisoning attacks. I will conclude the talk by a summary of challenges that require significant research in this field.

Bio:

Jing Gao is an Associate Professor in the School of Electrical and Computer Engineering, Purdue University. Before joining Purdue in January 2021, she was an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo (UB), State University of New York. She received her PhD from Computer Science Department, University of Illinois at Urbana Champaign in 2011, and subsequently joined UB in 2012. She is broadly interested in data and information analysis with a focus on data mining. In particular, she is interested in information veracity analysis, crowdsourcing, knowledge graphs, multi-source data analysis, anomaly detection, transfer learning, data stream mining as well as various data mining applications in healthcare, bioinformatics, social science, transportation, cyber security and education. She has published over 150 papers in referred journals and conferences. Her publications have received over 10,000 citations and her H-index is 52. She is an editor of ACM Transactions on Intelligence Systems and Technology, and serves in the senior program committee of ACM KDD, IJCAI, CIKM and WSDM conferences. She is a recipient of NSF CAREER award and IBM faculty award.

Reliable Sensing with Unreliable Sensors

Muhammad A. Alam

Electronic devices, be it a computer or a communication laser, or a clinical sensor, used to have it easy: Located in temperature/humidity-controlled rooms,  monitored by expert technicians, plugged into unlimited power sources, they demonstrated impressive performance and high accuracy. No longer. Today, an implantable sensor for smart agriculture may be left buried in the dirt for a year or more with a limited energy budget, without calibration or any temperature control. A wearable sweat sensor, an insulin pump,  or a brain implant needs to function regardless of the variation in body temperature, biofouling of cannula sites, or degradation of the membrane.   In this short talk, I will discuss our initial efforts to develop a collection of physical and statistical techniques to create the theoretical foundation of self-calibrating, ultra-high signal-to-noise ratio, electrochemical sensors that provides reliable sensing with unreliable sensors without access to the ground truth. Our approach involves rethinking sensors as a communication channel with time as an active variable and borrowing techniques from social media to ferret out fake news. 

Bio: 

Professor Alam holds the Jai N. Gupta professorship at Purdue University, where his research focuses on the physics and technology of semiconductor devices. From 1995 to 2003, he was with Bell Laboratories, Murray Hill, NJ, as a Member of Technical Staff in the Silicon ULSI Research Department. Since joining Purdue in 2004,  Dr. Alam has published over 300 papers and he is among the top-20 contributors on diverse topics involving transistors, reliability, biosensors, and solar cells. He is a fellow of IEEE, APS, and AAAS. His awards include the 2006 IEEE Kiyo Tomiyasu Medal for contributions to device technology, 2015 SRC Technical Excellence Award for fundamental contributions to reliability physics, and 2018 IEEE EDS Award for educating, inspiring and mentoring students and electron device professionals around the world. More than 400,000 students worldwide have learned some aspect of semiconductor devices from his web-enabled courses.