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Advancements in UWB: Paving the Way for Sovereign Data Networks in Healthcare Facilities

Khan Reaz, Thibaud Ardoin, Lea Muth, Marian Margraf, Gerhard Wunder, Mahsa Kholghi, Kai Jansen, Christian Zenger, Julian Schmidt, Enrico Köppe, Zoran Utkovski, Igor Bjelakovic, Mathis Schmieder, Olaf Dressel

TL;DR

The paper tackles the security and data-communications limitations of UWB in healthcare by proposing a cohesion of ML-based device fingerprinting using CIR-derived signatures, ToA-based ranging countermeasures, and secrecy maps for spatial security assessment. It introduces a sovereign UWB data network with a TDMA MAC and mesh networking, enabling secure localization and short-range data exchange in environments where 2.4 GHz–based wireless technologies are restricted. Empirical results show varying success for fingerprinting across scenarios, with Vision Transformers offering improved generalization, and demonstrate a hospital-room implementation validating architectural feasibility and security concepts. The work highlights practical pathways to strengthen UWB security and scalability, potentially enabling broader adoption in healthcare and ultrasecure facilities through standardized enhancements such as 6LoWPAN integration and robust network governance.

Abstract

Ultra-Wideband (UWB) technology re-emerges as a groundbreaking ranging technology with its precise micro-location capabilities and robustness. This paper highlights the security dimensions of UWB technology, focusing in particular on the intricacies of device fingerprinting for authentication, examined through the lens of state-of-the-art deep learning techniques. Furthermore, we explore various potential enhancements to the UWB standard that could realize a sovereign UWB data network. We argue that UWB data communication holds significant potential in healthcare and ultra-secure environments, where the use of the common unlicensed 2.4~GHz band-centric wireless technology is limited or prohibited. A sovereign UWB network could serve as an alternative, providing secure localization and short-range data communication in such environments.

Advancements in UWB: Paving the Way for Sovereign Data Networks in Healthcare Facilities

TL;DR

The paper tackles the security and data-communications limitations of UWB in healthcare by proposing a cohesion of ML-based device fingerprinting using CIR-derived signatures, ToA-based ranging countermeasures, and secrecy maps for spatial security assessment. It introduces a sovereign UWB data network with a TDMA MAC and mesh networking, enabling secure localization and short-range data exchange in environments where 2.4 GHz–based wireless technologies are restricted. Empirical results show varying success for fingerprinting across scenarios, with Vision Transformers offering improved generalization, and demonstrate a hospital-room implementation validating architectural feasibility and security concepts. The work highlights practical pathways to strengthen UWB security and scalability, potentially enabling broader adoption in healthcare and ultrasecure facilities through standardized enhancements such as 6LoWPAN integration and robust network governance.

Abstract

Ultra-Wideband (UWB) technology re-emerges as a groundbreaking ranging technology with its precise micro-location capabilities and robustness. This paper highlights the security dimensions of UWB technology, focusing in particular on the intricacies of device fingerprinting for authentication, examined through the lens of state-of-the-art deep learning techniques. Furthermore, we explore various potential enhancements to the UWB standard that could realize a sovereign UWB data network. We argue that UWB data communication holds significant potential in healthcare and ultra-secure environments, where the use of the common unlicensed 2.4~GHz band-centric wireless technology is limited or prohibited. A sovereign UWB network could serve as an alternative, providing secure localization and short-range data communication in such environments.
Paper Structure (14 sections, 2 figures, 1 table)

This paper contains 14 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. UWB Security Enhancements
  3. Machine Learning Based Fingerprinting
  4. Specialized Deep Learning Approaches
  5. Results
  6. Countermeasures to Secure ToA-based Ranging
  7. Spatial Characterization Through Secrecy Maps
  8. Sovereign UWB Data Network
  9. Network Architecture
  10. Medium Access Control Protocol
  11. UWB Mesh Networking
  12. Payload Accommodation
  13. implementation
  14. Conclusion

Figures (2)

  • Figure 1: Qualitative depiction of a statistical secrecy characterization in an indoor environment (uplink scenario). Different colors encode different security levels. On the right-hand side we illustrate the effect of (gradually) switching on additional access points on the secrecy characterization (improvement of the secrecy outlook).
  • Figure 2: Architecture of an ultra secure UWB data network