IEEE Globecom 2021 Workshop on
Securing Next-Generation Connected Healthcare Systems using Futuristic Technologies
7-11 December 2021 // Madrid, Spain
Recent advancements in computing and communication technologies have allowed the development of connected healthcare systems that depend primarily on IoT and Edge technologies. However, the security and privacy of next-generation smart healthcare systems are major concerns, for the efficient rollout of connected healthcare. To help with the efficient rollout, futuristic technologies like Blockchain and Machine Learning (ML) are pioneering system security solutions. In this context, this workshop will concentrate on the crucial aspects of IoT security in a connected healthcare environment, which will not only benefit from cutting-edge methodological approaches but also assist in the rapid scalability and improvement of these systems. It also aims to use blockchain technology for communication and data security, as well as trust management, in a smart city IoT environment. The aim of this workshop is to bring together developers, academics, and professionals interested in IoT/edge security and privacy developments and applications for the advancement of connected healthcare systems.
- Amr Mohamed, Qatar University, Qatar
Title: Pervasive AI for Securing Next Generation Health Systems
The rapid increase in the chronic disease patients along with the recent pandemic motivate the need for transforming healthcare systems away from one-on-one patient treatment into AI-based health systems, to improve services, access and scalability, while respecting patients’ security and privacy. Traditional cloud-based architectures suffer from many issues, including scalability, communication and computational efficiency, which may prohibit the support of complex security and privacy techniques. This motivated the need for new emerging trends such as Edge, Fog, and Pervasive Computing, where we merge hierarchical computing with efficient communication, leveraging learning-based distributed optimization, in order to resolve many of the issues highlighted above. In this presentation, we will highlight the realm of combining pervasive computing with AI, aka “pervasive AI” for next-generation health applications, leveraging efficient security and privacy techniques at the edge. We will discuss state-of-the-art contributions we have recently published regarding AI-based lightweight security techniques and how distributed inference/classifications can leverage such techniques in improving the security of next-generation health applications. We will also cover recent contributions regarding distributed learning scenarios using reinforcement and federated learning architectures that address heterogeneous user data to improve the learning performance, and outcomes in distributed networks.
Amr Mohamed (S’ 00, M’ 06, SM’ 14) received his M.S. and Ph.D. in electrical and computer engineering from the University of British Columbia, Vancouver, Canada, in 2001, and 2006 respectively. He has worked as an advisory IT specialist in IBM Innovation Centre in Vancouver from 1998 to 2007, taking a leadership role in systems development for vertical industries. He is currently a professor in the college of engineering at Qatar University. He has over 25 years of experience in IoT, edge computing, pervasive AI, and wireless networking research and industrial systems development. He holds 3 awards from IBM Canada for his achievements and leadership, and 4 best paper awards from IEEE conferences. His research interests include wireless networking and edge computing for IoT applications. Dr. Amr Mohamed has authored or co-authored over 200 refereed journal and conference papers, textbooks, and book chapters in reputable international journals, and conferences. He is serving as a technical editor for three international journals, has served as a guest editor in several special issues, and has served as a technical program committee (TPC) and co-chair for many IEEE conferences and workshops.
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