The Internet of Bio-Nano Things is a promising technology to reduce turnover and increase resolution of medical data collection. Therefore, tiny devices, so-called Nano Machines (NMs) are placed inside the human body, where the collect, process and transmit information to devices outside the human body. Interfaces between in-body and the external electrical domain are crucial to the realization of this technology. In this paper, we propose sweat glands, a pre-existing infrastructure, as a novel interface through which NMs can transmit information from the inside to the outside of the human body. We provide a detailed mathematical model of the envisioned communication system and evaluate its communication performance using different types of detectors (e.g., Neural Network based detector).

The Internet of Bio-Nano Things (IoBNT) is an envisioned extension of the Internet of Things (IoT), which aims to connect natural and synthetic biological systems and networks to the Internet. Due to the access to new domains (e.g., human body) this concept may help to enable transformative applications in healthcare and nanomedicine. However, it also faces several challenges, such as suitable interfaces and appropriate communication methods. Synthetic Molecular Communications (MC), a molecule-based bio-compatible communication concept, is among the most promising solution, which also defines the requirements for the respective interfaces. Typically, MC systems require a digital representation of the information to be transmitted and, thus, the development of devices for the conversion of analog biological signals to digital signals is crucial, but not well investigated. Thus, in this paper we propose a novel Molecular Analog-to-Digital converter (MADC). The MADC is based on a new neural network representation of the electronic flash ADC concept. This representation enables the implementation of the MADC using the recently proposed Molecular Nano Neural Networks (M3N). In particular, the proposed MADC consists of two matrix multiplication layers that are connected via a ReLU and threshold layer. We derive general design guidelines for the MADC and successfully validate it through computer simulations.

Intelligent behavior is an emergent phenomenon observed in biological organisms across all scales. It describes the cooperative behavior of low complexity entities to accomplish complex tasks, which exceed their individual capabilities. This property is particularly important for the Internet of Bio-Nano Things (IoBNT), which consists of Bio-Nano Things (BNTs) used in the human body, where they face many restrictions, such as bio-compatibility and size constraints. In this paper, we present a novel BNT-architecture, called Molecular Nano Neural Networks (M3N), which allows the implementation of intelligence on the micro-/nano-scale. The proposed structure consists of compartments (low complexity entities) that are connected to each other to form a network. Based on reaction and diffusion of molecules in and between connected compartments, this network mimics an artificial neural network, which is an important step towards artificial intelligence in the IoBNT. We provide design guidelines for the proposed M3N and successfully validate it by applying a regression and classification task.

In this work, we propose a novel nano-scale architecture that performs matrix multiplications. Matrix multiplications are the basic operations of machine learning (ML) algorithms and, thus, the presented approach enables their application at the nano-scale, for example inside the human body in the Internet of Bio-Nano-Things (IoBNT). It is based on the molecule exchange between connected compartments and introducing chemical reactions in some of them. The matrix entries are solely defined by the volumes of the compartment. We provide a detailed mathematical description of the stochastic and dynamic behavior of the system. Moreover, we derive design guidelines for the proposed architecture. Finally, we validated the proposed approach through particle-based simulations.