ABSTRACT
Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using the Reynolds-averaged Navier-Stokes equations coupled with the realizable k-model and the discrete random walk model, respectively. Via simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection, which shows itself by the multi-modal distribution as a weighted sum of normal and Weibull distributions. Furthermore, it is shown that the turbulent MC channel is characterized via multi-modal, i.e., sum of weighted normal distributions, or stable distributions, depending on the air velocity. Crown
ABSTRACT
Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using Reynolds-averaged Navier-Stokes equations coupled with realizable k-epsilon model and the discrete random walk model, respectively. Via the simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection which shows itself by the multimodal distributions as a weighted sum of normal and Weibull distributions. © 2022 IEEE.
ABSTRACT
Besides mimicking bio-chemical and multi-scale communication mechanisms, molecular communication forms a theoretical framework for virus infection processes. Towards this goal, aerosol and droplet transmission has recently been modeled as a multiuser scenario. In this letter, the "infection performance" is evaluated by means of a mutual information analysis, and by an even simpler probabilistic performance measure which is closely related to absorbed viruses. The so-called infection rate depends on the distribution of the channel input events as well as on the transition probabilities between channel input and output events. The infection rate is investigated analytically for five basic discrete memoryless channel models. Numerical results for the transition probabilities are obtained by Monte Carlo simulations for pathogen-laden particle transmission in four typical indoor environments: two-person office, corridor, classroom, and bus. Particle transfer contributed significantly to infectious diseases like SARS-CoV-2 and influenza.
ABSTRACT
A number of transmission models for airborne pathogens transmission, as required to understand airborne infectious diseases such as COVID-19, have been proposed independently from each other, at different scales, and by researchers from various disciplines. We propose a communication engineering approach that blends different disciplines such as epidemiology, biology, medicine, and fluid dynamics. The aim is to present a unified framework using communication engineering, and to highlight future research directions for modeling the spread of infectious diseases through airborne transmission. We introduce the concept of mobile human ad hoc networks (MoHANETs), which exploits the similarity of airborne transmission-driven human groups with mobile ad hoc networks and uses molecular communication as the enabling paradigm. In the MoHANET architecture, a layered structure is employed where the infectious human emitting pathogen-laden droplets and the exposed human to these droplets are considered as the transmitter and receiver, respectively. Our proof-of-concept results, which we validated using empirical COVID-19 data, clearly demonstrate the ability of our MoHANET architecture to predict the dynamics of infectious diseases by considering the propagation of pathogen-laden droplets, their reception and mobility of humans.
ABSTRACT
Motivated by various health care applications and many other novel fields of Molecular Communication (MC), it has become an important field of research since the last decade. This paper proposes a molecular communication-based model for the spread of the SARS-CoV2 virus in the human body. The virus uses the ACE2 receptor as a gateway to enter the blood vessels, organs and then replicate itself. In response to the infection, the immune system synthesizes pro-inflammatory cytokines such as IL6, IL2, and TNFa. This active bodily response may be further compromised by the generation of anti-inflammatory cytokines such as IL4 and IL10. We also propose a mathematical model using a Markov state transition for a flow-based molecular communication system which contributes to the detection of these pro-inflammatory cytokines level and gives a further inference about the infection in the body by taking multiple cytokines into account. © 2022 IEEE.
ABSTRACT
With the emergence of the COVID-19 pandemic, the World Health Organization (WHO) has urged scientists and industrialists to explore modern information and communication technology (ICT) as a means to reduce or even eliminate it. The World Health Organization recently reported that the virus may infect the organism through any organ in the living body, such as the respiratory, the immunity, the nervous, the digestive, or the cardiovascular system. Targeting the abovementioned goal, we envision an implanted nanosystem embedded in the intra living-body network. The main function of the nanosystem is either to perform diagnosis and mitigation of infectious diseases or to implement a targeted drug delivery system (i.e., delivery of the therapeutic drug to the diseased tissue or targeted cell). The communication among the nanomachines is accomplished via communication-based molecular diffusion. The control/interconnection of the nanosystem is accomplished through the utilization of Internet of bio-nano things (IoBNT). The proposed nanosystem is designed to employ a coded relay nanomachine disciplined by the decode and forward (DF) principle to ensure reliable drug delivery to the targeted cell. Notably, both the sensitivity of the drug dose and the phenomenon of drug molecules loss before delivery to the target cell site in long-distance due to the molecules diffusion process are taken into account. In this paper, a coded relay NM with conventional coding techniques such as RS and Turbo codes is selected to achieve minimum bit error rate (BER) performance and high signal-to-noise ratio (SNR), while the detection process is based on maximum likelihood (ML) probability and minimum error probability (MEP). The performance analysis of the proposed scheme is evaluated in terms of channel capacity and bit error rate by varying system parameters such as relay position, number of released molecules, relay and receiver size. Analysis results are validated through simulation and demonstrate that the proposed scheme can significantly improve delivery performance of the desirable drugs in the molecular communication system.
ABSTRACT
DNA-based molecular communication is a novel paradigm for nanoscale computation and communication that uses self-assembling DNA message molecules. Due to their design, these message molecules can compute mathematical operations while self-assembling. They can be used in DNA-based nanonetworks to detect DNA sequences and compute information for releasing either medication or other molecules. This paradigm avoids many limitations that electromagnetic nanonetworks currently face. This paper presents a variety of novel advantages and use cases for DNA-based molecular communication. For many of those, no feasible solution exists today. DNA-based molecular communication can even detect and consider multiple different DNA sequences for decision-making. Furthermore, it allows for adjustable error correction, immediate treatments, bio-compatibility, and the use of already available materials.
ABSTRACT
Molecular communications essentially analyze the transmission of the information at the nano level in cells, the smart devices that constitute our bodies. This emerging field uses traditional communication systems elements and maps them to molecular signaling and communication found inside and outside the body. Hence, molecular communications’ fundamental importance denotes the necessity to develop a new technology framework that provides a novel perspective to fight human diseases (the COVID-19 pandemic has highlighted this challenge). Thus, the architecture for molecular communications can be explored from the perspective of computer networks, i.e., the TCP/IP reference model and the basic model of MC can also be represented using Shannon’s communication model (i.e., transmitter, communication channel, and receiver). In this field, IEEE impulses the 1906.1 and 1906.1.1 standards that establish definitions, terminology, and a conceptual model for ad hoc network communication at the nanoscale. With these ICT perspectives, we appropriately have analyzed gene expression in eukaryotes organisms as a layered stack (network, link, and physical layer) of a nano communication network. In this biological communication process, the cellular nucleus behaves as the DTE, the ribosomes, and Endoplasmic Reticulum represent the DCE, the Golgi Apparatus represents a border router. The proteins secreted by the cell move through the bloodstream (physical transmission medium) and reaching the receiver (DCE-DTE), which processes the information through ligands and their receptors. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
ABSTRACT
Communication between nanomachines is still an important topic in the construction of the Internet of Bio-Nano Things (IoBNT). Currently, molecular communication (MC) is expected to be a promising technology to realize IoBNT. To effectively serve the IoBNT composed of multiple nanomachine clusters, it is imperative to study multiple-access MC. In this article, based on the molecular division multiple access technology, we propose a novel multiuser MC system, where information molecules with different diffusion coefficients are first employed. Aiming at the user fairness in the considered system, we investigate the optimization of molecular resource allocation, including the assignment of the types of molecules and the number of molecules of a type. Specifically, three performance metrics are considered, namely, min-max fairness for error probability, max-min fairness for achievable rate, and weighted sum-rate maximization. Moreover, we propose two assignment strategies for types of molecules, i.e., best-to-best (BTB) and best-to-worst (BTW). Subsequently, for a two-user scenario, we analytically derive the optimal allocation for the number of molecules when types of molecules are fixed for all users. In contrast, for a three-user scenario, we prove that the BTB and BTW schemes with the optimal allocation for the number of molecules can provide the lower and upper bounds on system performance, respectively. Finally, numerical results show that the combination of BTW and the optimal allocation for the number of molecules yields better performance than the benchmarks.
ABSTRACT
As an alternative to ongoing efforts for vaccine development, scientists are exploring novel approaches to provide innovative therapeutics, such as nanoparticle- and stem cell-based treatments. Thus, understanding the transmission and propagation dynamics of coronavirus inside the respiratory system has attracted researchers' attention. In this work, we model the transmission and propagation of coronavirus inside the respiratory tract, starting from the nasal area to alveoli using molecular communication theory. We performed experiments using COMSOL, a finite-element multiphysics simulation software, and Python-based simulations to analyze the end-to-end communication model in terms of path loss, delay, and gain. The analytical results show the correlation between the channel characteristics and pathophysiological properties of coronavirus. For the initial 50% of the maximum production rate of virus particles, the path loss increases more than 16 times than the remaining 50%. The delayed response of the immune system and increase in the absorption of virus particles inside the respiratory tract delay the arrival of virus particles at the alveoli. Furthermore, the results reveal that the virus load is more in case of asthmatic patients as compared to the normal subjects.
ABSTRACT
This contribution exploits the duality between a viral infection process and macroscopic air-based molecular communication. Airborne aerosol and droplet transmission through human respiratory processes is modeled as an instance of a multiuser molecular communication scenario employing respiratory-event-driven molecular variable-concentration shift keying. Modeling is aided by experiments that are motivated by a macroscopic air-based molecular communication testbed. In artificially induced coughs, a saturated aqueous solution containing a fluorescent dye mixed with saliva is released by an adult test person. The emitted particles are made visible by means of optical detection exploiting the fluorescent dye. The number of particles recorded is significantly higher in test series without mouth and nose protection than in those with a well-fitting medical mask. A simulation tool for macroscopic molecular communication processes is extended and used for estimating the transmission of infectious aerosols in different environments. Towards this goal, parameters obtained through self experiments are taken. The work is inspired by the recent outbreak of the coronavirus pandemic.
ABSTRACT
Severe Acute Respiratory Syndrome-CoronaVirus 2 (SARS-CoV2) caused the ongoing pandemic. This pandemic devastated the world by killing more than a million people, as of October 2020. It is imperative to understand the transmission dynamics of SARS-CoV2 so that novel and interdisciplinary prevention, diagnostic, and therapeutic techniques could be developed. In this work, we model and analyze the transmission of SARS-CoV2 through the human respiratory tract from a molecular communication perspective. We consider that virus diffusion occurs in the mucus layer so that the shape of the tract does not have a significant effect on the transmission. Hence, this model reduces the inherent complexity of the human respiratory system. We further provide the impulse response of SARS-CoV2-ACE2 receptor binding event to determine the proportion of the virus population reaching different regions of the respiratory tract. Our findings confirm the results in the experimental literature on higher mucus flow rate causing virus migration to the lower respiratory tract. These results are especially important to understand the effect of SARS-CoV2 on the different human populations at different ages who have different mucus flow rates and ACE2 receptor concentrations in the different regions of the respiratory tract.