IEEE GLOBECOM 2023 Workshop
on
Edge-AI and IoT for Connected Health
With the development of smart sensorial media, things, edge analytics along with Artificial Intelligence (AI) and cloud technologies, “Connected healthcare” is getting remarkable consideration from academia, the governments, the industry, and the healthcare community. Recently, the Internet of Things (IoT) has brought the vision of a smarter world into reality with a massive amount of data and numerous services. However, because of the massive connectivity of IoT-connected devices in providing numerous connected health services, it becomes a computation-intensive and storage burden at each edge device. To address this challenge, edge computing along with AI provides powerful computation services and massive data acquisition at edge networks in an intelligent manner for autonomous decision-making, which is quite impossible for individual human analysts. The edge-IoT services can provide a high quality of experience to physicians, clinics, and other caregivers anytime and from anywhere seamlessly. Similarly, with the outbreak of COVID-19, Artificial Intelligence (AI) has gained significant attention by utilizing its machine learning algorithms for quality patient care. However, the convergence of Edge, IoT, and AI can provide new opportunities for both technologies, as it can play a significant role in smart healthcare by offering a better insight into healthcare data to support affordable personalized care.
While researchers have been making advances to the study of Edge-AI and IoT for health services individually, very little attention has been given to developing cost-effective and affordable smart healthcare services. The Edge-AI-driven IoT for connected healthcare has the potential to revolutionize many aspects of our healthcare industry; however, many technical challenges need to be addressed before this potential can be realized. Authors are solicited to submit complete unpublished papers in the following, but not limited to the following topics of interest.
- AI-centric Mobile Edge Computing (MEC) approach for Connected health
- Explainable AI (XAI) and predictive edge analytics for COVID-19
- Edge AI-assisted COVID-19 and alike detection / diagnosis systems
- Digital Twins for Connected Healthcare
- AI-enabled IoT-edge data analytics for Connected Health
- AI-enabled edge data fusion for Connected Health
- ML-driven driven edge approach to Connected Health
- Deep Learning-based networked applications, techniques, and testbeds for Health
- AI-driven multi-access edge computing approach for Connected Health
- EdgeAI- empowered big data Analytics and cognitive computing for connected health monitoring
- Advanced AIIoT convergent services, systems, infrastructure, and techniques for healthcare
- EdgeAI-supported IoT data analytics for smart healthcare
- New opportunities, challenges, case studies, and applications of Edge-AI for Connected healthcare
- Security, Privacy, and Trust of Edge-AI for Connected health
Workshop Organizers:
M. Shamim Hossain, King Saud University, Saudi Arabia. mshossain(at)ksu(dot)edu.sa
Nadra Guizani, Univeristy of Texas Arlington, USA, nadra.guizani@uta.edu
Victor C.M. Leung, Shenzhen University, China and UBC, Canada, vleung (at)ieee.org
Submission Link: https://edas.info/N31201
Important Dates:
- Workshop Papers Due: August 12, 2023
- Workshop Papers Acceptance Notification: 1 September 2023
- Final Camera-Ready Paper Due: October 1, 2023
