AI-Based Energy Efficiency Optimization in 6G Wearable Medical Networks

Unmanned Aerial Vehicles (UAVs) have emerged as a crucial technology in healthcare, facilitating medical supply deliveries, organ transportation, and emergency response in remote and hazardous environments. Additionally, Mobile Edge Computing (MEC) enhances healthcare networks by processing medical data closer to the source, reducing latency and improving responsiveness. However, optimizing UAV-enabled MEC networks remains a challenge due to limited computing resources, energy constraints, dynamic network conditions, and security vulnerabilities.

This thesis focuses on improving the energy efficiency and security of UAV-assisted 6G Wearable Medical Networks (WMNs) by (1) developing an optimized UAV-based offloading scheme that utilizes Non-Orthogonal Multiple Access (NOMA) for uplink and Orthogonal Frequency Division Multiple Access (OFDMA) for downlink to enhance network efficiency, (2) integrating blockchain technology to establish a decentralized, transparent, and tamper-proof medical data management system, and (3) designing an energy-efficient resource allocation framework using Deep Reinforcement Learning (DRL) to optimize UAV deployment, computational load balancing, and data transmission strategies.

Some Sources to consider:

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