## 6 IBC WBAN Architecture with Identification Functionality  
Biometric identity is a security method based on the technology of recognizing a person by their unique biological characteristics. A person's biometric data are uploaded to a database and serve as a pattern. During biometric identity, the user's biometric data are compared to the pattern stored in the database. If the data match, then biometric identity is considered successful.  
We proposed energy-efficient IBC WBAN architecture in [38]. In our current study, we propose to expand the IBC WBAN functionality with user biometric identity. Figure 13 shows the proposed IBC system architecture with biometric identity.  
Figure 12: Dependence of channel amplification rx_gain_1M on weight.  
Figure 13: IBC WBAN architecture with user identification.  
The system operates as follows:  
1. Implantable Devices and Signal Propagation Miniaturized implants embedded within the body generate low-power electrical signals using galvanic coupling, where the human body acts as a conductive medium. Signal propagation is modulated by the individual's unique biological parameters, including body composition, geometric dimensions [25], and tissue dielectric properties. These characteristics impart distinct channel gain and attenuation profiles to the transmitted signals.
2. IBC Transceiver The IBC transceiver employs separate coupling to establish dual communication pathways: one for signal transmission through the body and another for environmental coupling. This design minimizes cross-interference between internal and external communication channels. Simultaneously, it generates coupling to stabilize signal transmission across dynamic physiological conditions (e.g., movement, hydration changes).
3. Mobile Transceiver and Biometric Extraction A wearable or handheld mobile transceiver interface with the IBC transceiver to relay data to external networks. Integrated within this unit is a mobile identification module, which extracts real-time IBC channel characteristics, such as:  
Receiver gain.  
Frequency-dependent attenuation profiles.  
Transmitter-receiver separation distance. (The inclusion of distance as a feature mitigates false identification caused by overlapping gain values at varying transmission distances.)
4. Biometric Identification Workflow The extracted channel characteristics are compared against pre-registered biometric templates stored in a secure database. A machine learning classifier (e.g., K-Nearest Neighbors) analyzes the feature space to authenticate users. Matching thresholds are dynamically adjusted to accommodate minor physiological variations (e.g., weight fluctuations), ensuring robustness without compromising security.
5. Security and Scalability The architecture leverages the inherent physical boundaries of the human body to restrict signal propagation, reducing eavesdropping risks. Biometric templates are encrypted and stored in a decentralized database, enabling scalable deployment across large populations. The system supports real-time updates to biometric profiles, ensuring adaptability to long-term physiological changes. Technical advantages of the proposed approach consist of the following:  
* Energy Efficiency: galvanic coupling reduces power consumption by one-to-two orders of magnitude compared to RF-based systems.
* High Accuracy: biometric identification achieves 99.9% accuracy in controlled environments, driven by unique channel gain profiles.
* Interference Resilience: separate coupling pathways isolate intra-body communication from external electromagnetic noise.  
This architecture pioneers the use of IBC channel properties as biometric identifiers, enabling secure, personalized applications in healthcare monitoring, industrial wearables, and IoT access control.