LLM-Driven Adaptive 6G-Ready Wireless Body Area Networks: Survey and Framework
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916945691934720 |
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| author | Torkamani, Mohammad Jalili Mahmoudi, Negin Kiashemshaki, Kiana |
| author_facet | Torkamani, Mohammad Jalili Mahmoudi, Negin Kiashemshaki, Kiana |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08535 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-Driven Adaptive 6G-Ready Wireless Body Area Networks: Survey and Framework Torkamani, Mohammad Jalili Mahmoudi, Negin Kiashemshaki, Kiana Networking and Internet Architecture Artificial Intelligence C.2.1; C.2.2; E.3; I.2.7 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. |
| title | LLM-Driven Adaptive 6G-Ready Wireless Body Area Networks: Survey and Framework |
| topic | Networking and Internet Architecture Artificial Intelligence C.2.1; C.2.2; E.3; I.2.7 |
| url | https://arxiv.org/abs/2508.08535 |