## 1 Introduction and Motivation
These challenges hinder the reliability of communication between devices in WBANs, thereby limiting the potential applications of IBC technology. Numerical biological tissue modeling plays a pivotal role in the development and optimization of intrabody communication devices. Modeling is widely used to determine the characteristics and parameters of body area networks. IBC modeling is a complex task because of the propagation of the signal through tissues with varying dielectric properties. Modeling plays a crucial role in IBC design, enabling the optimization of wearable devices to achieve maximum reliability and data transfer rates while minimizing power consumption. The goal of IBC modeling is to assess the functional dependencies between the parameters of a real system during operation, such as the relationship between signal quality and the properties of external and internal tissues, the distance between transceivers, and the size and shape of the tissue channel [19]. However, experimental studies are necessary to validate these simulation results. The use of physical tissue models or phantoms allows repeatable measurements under controlled conditions, significantly simplifying the verification process. In [20], the authors measured the characteristics of the IBC channel in five subjects and two phantoms to investigate the feasibility of using these characteristics for biometric identification. This study confirmed the possibility of biometric identification with an accuracy of 98.5% using machine learning algorithms for data analysis. This paper models the feasibility and effectiveness of using IBC channel characteristics for biometric identity, which further can be used in industrial applications based on the last known dataset containing experimentally measured signal loss data in IBC channels [21]. The K-Nearest Neighbors (KNN) algorithm was used to assess the feasibility of identifying and recognizing individual subjects in the dataset based on features extracted from their IBC channel characteristics. The main contributions of our study are the following:  
* Our research presents a study that integrates biometric identification techniques into the IBC system, utilizing channel gain influenced by individual physical characteristics (such as height, weight, and body composition) to enhance user identification and authentication from an experimental dataset containing data about real subjects. By using the KNN algorithm, we achieved an accuracy of 99.9%, demonstrating the potential for IBC to serve as reliable biometric identifiers. * We proposed a novel IBC WBAN architecture that incorporates biometric identification functionalities, enhancing the performance and security of existing systems. This architectural advancement provides a framework for the development of future IBC-enabled devices and applications focused on personalized user experiences. * By exploring the synergy between IBC technologies and biometric identification, our study addresses significant challenges in the realms of security and energy efficiency relevant to Industry 4.0. Our findings suggest pathways for the deployment of energy-efficient, secure WBANs within industrial settings, highlighting opportunities for health monitoring and workforce management. * This analysis lays the groundwork for future studies assessing the applicability of IBC in diverse contexts. This paper is organized as follows. Section 2 covers related work in IBC. Section 3 provides a brief overview of types, identification, and measurement of signal loss in IBC channels. Section 4 is devoted to the analysis of the dataset and description of the influence of individual human body characteristics on IBC channel gain. Section 5 presents a biometric identity modeling based on IBC channel gain data from the dataset using the KNN algorithm. Section 6 is devoted to the architecture for an IBC system with biometric identity. Section 7 is focused on discussion of the results and future studies. Finally, Section 8 concludes the paper.