Transforming Healthcare with Secure MECC in 6G Networks
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Abstract
The rise of 6G networks is expected to have a significant impact on various industries, including healthcare. Mobile edge cloud computing (MECC) has become a popular solution for providing fast and reliable services to 6G networks. However, the security of MECC systems is a major concern due to the distributed nature of resources and potential risks associated with cloud and server access. Task scheduling is critical in allocating resources to tasks while considering security constraints. This paper proposes a new task scheduling framework based on Artificial Intelligence (AI) and Deep Learning (DL) for MECC server and cloud security in 6G networks, with a focus on the healthcare sector. The framework uses supervised and unsupervised learning techniques to learn from historical data and predict future demands, intelligently allocating resources based on task priority, urgency, and security constraints. Extensive simulations on a 6G testbed demonstrate that our approach outperforms traditional scheduling algorithms in terms of security, efficiency, and resource utilization. We believe our framework is a valuable tool for designing secure and efficient MECC systems in 6G networks, particularly in healthcare.
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