Citation: Kantelis, F.K. A Learning
Automaton-Based Algorithm for
Maximizing the Transfer Data Rate in
a Biological Nanonetwork. Appl. Sci.
2022, 12, 9499. https://doi.org/
10.3390/app12199499
Academic Editor: Panagiotis
Sarigiannidis, Thomas Lagkas,
Alexandros-Apostolos Boulogeorgos,
Vasileios Argyriou and Pantelis
Angelidis
Received: 21 July 2022
Accepted: 19 September 2022
Published: 22 September 2022
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Article
A Learning Automaton-Based Algorithm for Maximizing the
Transfer Data Rate in a Biological Nanonetwork
Konstantinos F. Kantelis
Department of Computer Science, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece;
kantelisk@csd.auth.gr
Featured Application: This protocol can be used for biological nanonetworks utilizing synthetic
biology in order to transfuse the biological entities with the appropriate characteristics.
Abstract:
Biological nanonetworks have been envisaged to be the most appropriate alternatives
to classical electromagnetic nanonetworks for applications in biological environments. Due to the
diffusional method of the message exchange process, transfer data rates are not proportional to their
electromagnetic counterparts. In addition, the molecular channel has memory affecting the reception
of a message, as the molecules from previously transmitted messages remain in the channel, affecting
the number of information molecules that are required from a node to perceive a transmitted message.
As a result, the ability of a node to receive a message is directly connected to the transmission rate
from the transmitter. In this work, a learning automaton approach has been followed as a way to
provide the receiver nodes with an algorithm that could firstly enhance their reception capability
and secondly boost the performance of the transfer data rate between the biological communication
parties. To this end, a complete set of simulation scenarios has been devised, simulating different
distances between nodes and various input signal distributions. Most of the operational parameters,
such as the speed of convergence for different numbers of ascension and descension steps and the
number of information molecules per message, have been tested pertaining to the performance
characteristics of the biological nanonetwork. The applied analysis revealed that the proposed
protocol manages to adapt to the communication channel changes, such as the number of remaining
information molecules, and can be successfully employed at nanoscale dimensions as a tool for
pursuing an increased transfer data rate, even with time-variant channel characteristics.
Keywords:
biological nanonetworks; diffusion; learning automaton; transfer data rate; ligand–receptor
1. Introduction
Molecular communications have recently gained attention as noteworthy candidates
in the research of nanonetworking. Referred to as biological nanonetworks (BNs), networks
of this type utilize molecular mechanisms to convey information using mostly chemical
signals. This is an emerging field which has great potentials to provide a pivotal alternative
to traditional electromagnetic communications for use in biological environments. BNs
utilize small particles and biota such as molecules or plasmid vesicles to deliver information.
Owing to the peculiarities of the communication channel, this type of network in not
suitable for the direct transfer of the established communication protocols, instead requiring
new kinds of algorithms tailored to the unique characteristics of the biological environment
and the limited capabilities of the communication nodes.
Owning unique merits such as biocompatibility, biodegradability, low energy con-
sumption and inherent energy management, BNs have attracted attention due to their
ability to be incorporated into various interdisciplinary applications (such as pharmaceuti-
cal, medical, environmental and industrial), and have become the dominant communication
paradigm in biological applications. A molecular communication nanonetwork is based
Appl. Sci. 2022, 12, 9499. https://doi.org/10.3390/app12199499 https://www.mdpi.com/journal/applsci
