COVID-19 OUTBREAK: Under all circumstances, IEEE COINS 2020 proceedings will be published, and authors of accepted papers will have the option of presenting the paper remotely.


Supported and organized by IEEE Robotics and Automation Society, IEEE Council on Electronic Design Automation, IEEE Circuits and Systems Society, IEEE e-Health Technical Committee (ComSoc), IEEE Big Data Technical Committee (ComSoc), and IEEE IoT.

IEEE COINS is the premier conference devoted to omni-layer techniques for smart IoT systems, by identifying new perspectives and highlighting impending research issues and challenges. The e-Health and Wearable IoT track at COINS seeks the latest research advancements in the convergence of automation technology, artificial intelligence, biomedical engineering, wearable and mobile computing, Internet-of-Things, and healthcare. Topics of interest include, but are not limited to, the following:

• Internet of things for medical and healthcare applications
• Mobile and e-Health sensing
• Wearable, outdoor and home-based sensors
• Novel devices and circuits, and architectural support for e-health
• Printable electronics
• Harvesting management and optimization
• Nano-CMOS and Post-CMOS based sensors, circuits, and controller
• Wearable and implantable computing and biosensors
• Cloud-enabled body sensor networks
• Secure middleware for eHealth and IoT
• Energy-efficient PHY/MAC and networking protocols for eHealth applications
• Reprogrammable and reconfigurable embedded systems for eHealth
• eHealth traffic characterization
• Biomedical signal processing
• AI-based decision support systems for healthcare
• eHealth oriented software architectures (Agent, SOA, Middleware, etc.)
• Big-data analytics, machine learning algorithms, and scalable/parallel/distributed algorithms
• Theory and practice of engineering semantic e-health systems, especially methods, means and best cases
• Fog computing/Edge clouds for health care cloud resource allocation and monitoring
• Privacy-preserving and Security approaches for large scale analytics
• Privacy-Preserving Machine Learning (PPML) and Multi-party computation (MPC) techniques
• AI bias reduction approaches for mhealth and ehealth applications and ethical issues regarding nudging
• Blockchain: Opportunities for health care
• Fault tolerance, reliability, and scalability
• Autonomic analysis, monitoring and situation alertness
• Case studies of smart eHealth architectures (telemedicine applications, health management applications, etc.)