In the present technology revolution, the intelligent Internet of Things and Cyber–Physical Systems are evolving quickly as interdisciplinary technologies. In particular, Cognitive Cyber-Physical Systems (CCPS) plays an important role in blending the physical components and computational devices to integrate machine learning/AI techniques for drastic improvements in various industries including healthcare and especially in the medical informatics and the future of human augmentation i.e., Cyber-Physical-Human Medical Systems. But integrating the network of medical devices in medical cyber physical systems and Medical Internet of Things (MIoT) is a complicated process since the next generation computing technologies consists of tightly coupled computation and communication components.
In the current scenario, Medical Cyber-Physical System (MCPS) & MIoT have evolved towards a convergence of multiple technologies, real-time analytics, machine learning, commodity sensors, and embedded systems in addition to the traditional fields of wireless sensor networks, control systems and automation. With recent advancements in big data processing, cognitive data science AI and real time sensors, it is now possible to create even more realistic digital twins that properly models different operating situations and characteristics to process the medical intelligence systems. This special issue is more focussed on CCPS and MIoT towards medical cyber physical systems which will analyse the ongoing challenges and research to handle network, and multi-core systems further research applicability on medical informatics.
- Design and Challenges in building Cognitive Cyber-Medical Systems
- Cognitive Data in Cloud/Big Data/ IoT/Fog Computing
- IoT and Connected Devices in CCPS & MCPS
- Machine Learning Algorithms for building CCPS & MCPS
- Ultra-reliable MIoT applications
- Collaborative resource management frameworks
- Task scheduling algorithms for the cloud–edge-end continuum
- Risk management for CPS/MIoT applications
- Federated and distributed learning for large scale IoT
- Collaborative learning frameworks towards MCPS
- Security and privacy of edge computing-based learning models
