Energy-aware cluster head optimization and secure blockchain integration for heterogeneous 6G-enabled IoMT networks

Area of Science:

• Telecommunications Engineering
• Cybersecurity
• Artificial Intelligence in Healthcare

Background:

• 6G-enabled Internet of Medical Things (IoMT) networks offer secure, decentralized communication but face challenges in energy efficiency, latency, and authentication.
• Current clustering and security methods struggle to balance these critical aspects in heterogeneous IoMT environments.

Purpose of the Study:

• To propose a novel energy-aware cluster head selection framework for 6G IoMT networks.
• To enhance network security, data integrity, and operational efficiency using a lightweight blockchain layer and advanced optimization algorithms.

Main Methods:

• Developed an Artificial Democratic Cuckoo Glowworm Remora Optimization (ADCGRO) algorithm for energy-aware cluster head selection.
• Integrated a lightweight blockchain layer for secure authentication and ensuring data integrity within the IoMT network.
• Optimized task allocation across diverse IoMT devices (advanced, intermediate, normal) to minimize energy consumption while meeting URLLC requirements.

Main Results:

• The proposed ADCGRO-based blockchain framework demonstrated a 27% enhancement in network lifetime.
• Achieved a 35% reduction in average network latency, crucial for real-time medical applications.
• Attained 99% authentication accuracy, significantly outperforming existing protocols like LEACH and HEED.

Conclusions:

• The integration of ADCGRO optimization with blockchain effectively addresses the performance and security challenges in 6G IoMT networks.
• The proposed framework offers a viable solution for secure, efficient, and low-latency communication in next-generation medical IoT systems.