LLM-Driven Adaptive 6G-Ready Wireless Body Area Networks: Survey and Framework
It addresses the need for adaptive, energy-efficient, and quantum-resistant WBANs for medical monitoring, but is incremental as it builds on existing technologies with a new control mechanism.
This paper tackles the challenge of integrating 6G, post-quantum cryptography, and energy harvesting into adaptive Wireless Body Area Networks (WBANs) by proposing a novel Large Language Model-driven framework that coordinates routing, security, and energy management in real time to enable ultra-reliable, secure, and self-optimizing systems for mobile health applications.
Wireless Body Area Networks (WBANs) enable continuous monitoring of physiological signals for applications ranging from chronic disease management to emergency response. Recent advances in 6G communications, post-quantum cryptography, and energy harvesting have the potential to enhance WBAN performance. However, integrating these technologies into a unified, adaptive system remains a challenge. This paper surveys some of the most well-known Wireless Body Area Network (WBAN) architectures, routing strategies, and security mechanisms, identifying key gaps in adaptability, energy efficiency, and quantum-resistant security. We propose a novel Large Language Model-driven adaptive WBAN framework in which a Large Language Model acts as a cognitive control plane, coordinating routing, physical layer selection, micro-energy harvesting, and post-quantum security in real time. Our review highlights the limitations of current heuristic-based designs and outlines a research agenda for resource-constrained, 6G-ready medical systems. This approach aims to enable ultra-reliable, secure, and self-optimizing WBANs for next-generation mobile health applications.