Enhancing Digital Health in 6G Networks: A Cybertwin-driven Fuzzy Approach to Medical Traffic Identification and Classification

The advent of 6G networks heralds a transformative era for digital health, where applications such as telemedicine, remote surgeries, and the Internet of Medical Things (IoMT) are expected to proliferate. This paper introduces a novel Cybertwin-driven fuzzy framework designed to meet the intricate demands of 6G-enabled healthcare applications. The proposed architecture integrates edge and cloud computing with Cybertwins, utilizing fuzzy algorithms for real-time traffic classification and resource allocation. This architecture ensures priority-based traffic handling, leveraging the advanced capabilities of 6G to enhance the responsiveness and efficiency of digital health services. The proposed network will be assessed against key metrics such as accuracy, latency, and throughput through a MATLAB-based simulation, demonstrating the proposed approach’s benefits over traditional traffic management solutions. The expected results indicate a significant improvement in managing diverse health data traffic, offering insights into a scalable and adaptable solution for future healthcare networks. Looking ahead, this research contributes to the standardization efforts in 6G communications, emphasizing fostering a global ecosystem for digital health innovation.