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 description 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.

The paper presents the significance of microfluidic technology in advancing Molecular Communications (MC). It highlights the transition from theoretical MC models to practical applications, emphasizing the role of microfluidics in validating and advancing MC concepts. The paper covers various aspects including theoretical principles, simulation tools, practical realizations, and envisioned applications. We also present various microfluidic testbeds, detailing their design, capabilities, and applications in advancing MC. To bridge the gap between theoretical models and practical outcomes in MC, this work demonstrates the potential of microfluidics in the practical realization of MC systems.

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.

This paper introduces an adaptive transceiver scheme for bio-nano things (NTs) situated within blood vessels communicating through a time-varying molecular channel. The proposed scheme employs a Q-learning-based adaptive transceiver (a so-called QL-ADT), wherein an agent gradually learns how to adapt the transmission parameters to the current state of the channel. A real heart rate dataset is used to estimate the blood flow velocities over time, based on which a time-varying molecular channel is modelled. In the practical implementation of the QL-ADT, an external gateway, situated on the skin, monitors the bodys heart rate over time and interfaces with the NTs through implantable nano devices. The gateway dynamically adjusts the communication parameters of the NTs based on the measured heart rate and what it has learned during the training phase. The proposed QL-ADT scheme showed significant improvement in the achievable raw bit rate (RBR) and error performance for a real heart rate dataset.

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 navigate the challenges of this particular domain. 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 of health care.

In this paper, we present the first results of a novel asymmetric auto-encoder for end-to-end learning of communication systems. This concept reduces the complexity of the receiver by shifting the computational effort to the transmitter. In particular, a neural network-based transmitter and a low-complexity slope detector are used in this work. Such an architecture finds application in the context of the Internet of Bio-Nano Things, when the transmitter located outside the human body, having abundant resources, communicates with the receiver inside the human body, having limited resources. The first simulation results showed that the proposed approach significantly outperforms the transmission using conventional modulated signals.