6G Wireless Communication Cyber Physical System Based Smart Healthcare Using Quantum Optimization with Machine Learning

The ground-breaking paradigm of artificial intelligence (AI) will provide universal access to sixth generation (6G) edge computing-based E-healthcare. Internet world, where people and their personal gadgets like laptops, wearables, and cell phones, play a major role in facilitating the healthcare environment, is home to the cyber physical system (CPS). When it comes to availability as well as integrity of CPSs, blackhole, greyhole assaults are among the most dangerous. Ineffective protection results from the present detection and mitigation techniques' frequent inability to distinguish between harmful and authorised activity. In this research the proposed model is based on 6G wireless communication network in smart healthcare system optimization and cyber physical system analysis. The smart healthcare data analysis and optimization is carried out using quantum dirichlet convolutional learning coyote foraging optimizer. Then the network CPS analysis is carried out using federated honeypot transfer decentralized authentication model. Experimental analysis is carried out in terms of mean average precision, training accuracy, F-1 score, convergence rate, end-end delay. Proposed technique network security of 96%, mean average precision (MAP) 97%, training accuracy of 95%, F-1 score 77%, convergence rate of 88%. Regarding the suggested predictive model for a health system, the experimental findings provide positive findings. As a result of suggested work, CPS employing a suggested model improves medical data security with a high accuracy rate.