LLM-Driven Adaptive 6G-Ready Wireless Body Area Networks: Survey and Framework

TL;DR

This paper surveys WBAN architectures and proposes a novel LLM-driven adaptive framework to enhance 6G-ready wireless body area networks with improved security, energy efficiency, and adaptability.

Contribution

It introduces a new LLM-based control framework for WBANs, integrating routing, security, and energy management for next-generation health applications.

Findings

Identified key gaps in current WBAN designs.

Proposed a novel LLM-driven adaptive WBAN framework.

Outlined a research agenda for 6G-ready medical systems.

Abstract

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.