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Securing Hybrid Wireless Body Area Networks (HyWBAN): Advancements in Semantic Communications and Jamming Techniques

Simone Soderi, Mariella Särestöniemi, Syifaul Fuada, Matti Hämäläinen, Marcos Katz, Jari Iinatti

TL;DR

The paper addresses security and energy efficiency challenges in HyWBANs for smart healthcare by combining semantic communications with a jamming receiver to harden data confidentiality and integrity across dual RF–OWC channels. It grounds the approach in laboratory measurements of through-tissue propagation for NIR and UWB, and builds a DL-driven semantic model that maps concepts to cryptographic keys, protected by intentional interference. A 64-32-64 autoencoder with a dense classifier achieves up to 94% accuracy (TensorFlow) and demonstrates favorable energy efficiency against traditional ECDH key exchange when augmented with jamming, with TinyML deployment considered for edge devices. The work provides a data-driven baseline for secure biomedical communication in HyWBANs and highlights 6G-relevant opportunities for energy-aware, semantically secure medical IoT transmissions.

Abstract

This paper explores novel strategies to strengthen the security of Hybrid Wireless Body Area Networks (HyWBANs), essential in smart healthcare and Internet of Things (IoT) applications. Recognizing the vulnerability of HyWBAN to sophisticated cyber-attacks, we propose an innovative combination of semantic communications and jamming receivers. This dual-layered security mechanism protects against unauthorized access and data breaches, particularly in scenarios involving in-body to on-body communication channels. We conduct comprehensive laboratory measurements to understand hybrid (radio and optical) communication propagation through biological tissues and utilize these insights to refine a dataset for training a Deep Learning (DL) model. These models, in turn, generate semantic concepts linked to cryptographic keys for enhanced data confidentiality and integrity using a jamming receiver. The proposed model demonstrates a significant reduction in energy consumption compared to traditional cryptographic methods, like Elliptic Curve Diffie-Hellman (ECDH), especially when supplemented with jamming. Our approach addresses the primary security concerns and sets the baseline for future secure biomedical communication systems advancements.

Securing Hybrid Wireless Body Area Networks (HyWBAN): Advancements in Semantic Communications and Jamming Techniques

TL;DR

The paper addresses security and energy efficiency challenges in HyWBANs for smart healthcare by combining semantic communications with a jamming receiver to harden data confidentiality and integrity across dual RF–OWC channels. It grounds the approach in laboratory measurements of through-tissue propagation for NIR and UWB, and builds a DL-driven semantic model that maps concepts to cryptographic keys, protected by intentional interference. A 64-32-64 autoencoder with a dense classifier achieves up to 94% accuracy (TensorFlow) and demonstrates favorable energy efficiency against traditional ECDH key exchange when augmented with jamming, with TinyML deployment considered for edge devices. The work provides a data-driven baseline for secure biomedical communication in HyWBANs and highlights 6G-relevant opportunities for energy-aware, semantically secure medical IoT transmissions.

Abstract

This paper explores novel strategies to strengthen the security of Hybrid Wireless Body Area Networks (HyWBANs), essential in smart healthcare and Internet of Things (IoT) applications. Recognizing the vulnerability of HyWBAN to sophisticated cyber-attacks, we propose an innovative combination of semantic communications and jamming receivers. This dual-layered security mechanism protects against unauthorized access and data breaches, particularly in scenarios involving in-body to on-body communication channels. We conduct comprehensive laboratory measurements to understand hybrid (radio and optical) communication propagation through biological tissues and utilize these insights to refine a dataset for training a Deep Learning (DL) model. These models, in turn, generate semantic concepts linked to cryptographic keys for enhanced data confidentiality and integrity using a jamming receiver. The proposed model demonstrates a significant reduction in energy consumption compared to traditional cryptographic methods, like Elliptic Curve Diffie-Hellman (ECDH), especially when supplemented with jamming. Our approach addresses the primary security concerns and sets the baseline for future secure biomedical communication systems advancements.
Paper Structure (12 sections, 11 figures, 2 tables)

This paper contains 12 sections, 11 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Radio-based WBAN technologies
  4. Optical communications in Wireless Sensor Networks
  5. Security Analysis of HyWBANs
  6. Security Threats Overview
  7. Enhancing the Security of HyWBAN through Semantic Communications
  8. Radio and optical channels data measurement
  9. Dataset: measured and synthetic data for medical applications using HyWBAN
  10. Proposed Model Evaluation
  11. Conclusions
  12. Acknowledgments

Figures (11)

  • Figure 1: Coding strategy for hybrid networks.
  • Figure 2: Variability of the possible sensors that can be used in the wban context.
  • Figure 3: Communications tiers system model, in which hybrid communications operate in the first two tiers.
  • Figure 4: Security threats to hywban.
  • Figure 5: AI-based data stream encoding in hywban using semantic communications and a jamming receiver to harden the security of wireless communications.
  • ...and 6 more figures