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.

This paper introduces a novel communication interface between the Internet of Things (IoT) and the Internet of Bio-Nano Things (IoBNT). In particular, we propose a transmitter (TX) that emits acoustic waves as the information carriers and a molecular receiver Nano-Machine (NM) that converts the acoustic to a molecular signal. We show, that a combination of mechanosensitive and voltage sensitive ion-channels on the surface of the NM can be used for this conversion. We provide a detailed mathematical model for the system and study the signal characteristics and communication performance via computer simulation. Our concept allows acoustical information transmission in the IoBNT, which is much faster than conventional molecular communications. Nevertheless, conversion from acoustic to molecular signal at the receiver allows compatibility with existing methods.

The Internet of Bio-Nano Things (IoBNT) is a new type of communication network, which aims to bridge the gap between classical electromagnetic communications and the biological world, especially the human body. The establishment of such a system promises transformative breakthroughs in disciplines like information technology, pharmacology and medicine. However, before this concept becomes reality, a number of challenges need to be addressed. In this work, we address the two most important ones: bio-compatibility and electrical interfaces. We propose a novel thermomolecular communication interface, which allows data transmission through the human skin by generating variations in the temperature. Importantly, our concept realizes this transmission, without damaging this critical protective layer of the organism. Furthermore, we present a novel micro/nano-scale receiver, which is based on the Arrhenius principle and converts temperature variations into a molecular signal. Thus, our approach is compatible with existing IoBNT communication schemes, especially Molecular Communications (MC). We provide a detailed physical and control theoretical model of the proposed thermomolecular system and evaluate the system dynamics, noise behavior and error performance via computer simulations.

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).

Intelligent nano-machines are a promising candidate technology for the next generation of health care. The realization of such units relies on novel, unconventional approaches, to allow for bio-compatibility and managing space constraints. In this work, we present three chemical processes, that can be used to realize a recently proposed molecular matrix multiplication unit on the lab-scale. The matrix multiplication is the fundamental operation for the realization of neural networks and, therefore, artificial intelligence. Hence, this work presents an important step towards practical realization of intelligent nano-machines for the next generation health care.

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.

The Internet of Bio-Nano Things (IoBNT) is a novel framework that has the potential to enable transformative applications in healthcare and nano-medicine. It consists of artificial or natural tiny devices, so-called Bio Nano Things (BNTs), that can be placed in the human body to carry out specific tasks (e.g., sensing) and are connected to the internet. However, due to their small size their computation capabilities are limited, which restricts their ability to process data and make decision directly in the human body. Thus, we address this issue and propose a novel nano-scale computing architecture that performs matrix multiplications, which is one of the most important operations in signal processing and machine learning. The computation principle is based on diffusion-based propagation between connected compartments and chemical reactions within some compartments. The weights of the matrix can be set independently through adjusting the volume of the compartments. We present a stochastic and a dynamical model of the proposed structure. The stochastic model provides an analytical solution for the input-output relation in the steady state, assuming slow reaction rates. The dynamical model provides important insights into the systems temporal dynamics. Finally, micro- and mesoscopic simulations verify the proposed approach.

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.

In this work, we present a novel portable, flexible and easy-to-use experimental setup for investigating salinity- based information transmission in microfluidic channels. At the receiver, the different salinity-levels are detected by a customized electronic circuit, which measures electrical conductivity via electrodes in the microfluidic channel. We provide a detailed de- scription of the setup, including the microfluidic chip fabrication. Moreover, we develop a rigorous mathematical model of each testbed component and an end-to-end model of the system, which we have verified through experiments. Finally, we analyzed the error performance of the setup using optimum and sub-optimum detection algorithms, such as the Viterbi algorithm and threshold detection.