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 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.