Molecular Nanonetworks Research Articles

GLADIATOR: Transforming Glioblastoma Care

We spoke to Professor Costas Pitris about GLADIATOR, a project that aims to revolutionise brain pathology diagnosis and treatment by using Molecular Communications systems. This theranostic solution, focused on glioblastoma, integrates autonomous molecular nanonetworks of engineered cells and innovative bio-electronics for personalised interventions, marking a paradigm shift in oncology research.

Fundamentals of Clustered Molecular Nanonetworks

We present a comprehensive approach to the modeling, performance analysis, and design of clustered molecular nanonetworks in which nano-machines of different clusters release an appropriate number of molecules to transmit their sensed information to their respective fusion centers. The fusion centers decode this information by counting the number of molecules received in the given time slot. Owing to the propagation properties of the biological media, this setup suffers from both inter-and intra-cluster interference that needs to be carefully modeled. To facilitate rigorous analysis, we first develop a novel spatial model for this setup by modeling nano-machines as a Poisson cluster process with the fusion centers forming its parent point process. For this setup, we first derive a new set of distance distributions in the three-dimensional space, resulting in a remarkably simple result for the special case of the Thomas cluster process. Using this, total interference from previous symbols and different clusters is characterized and its expected value and Laplace transform are obtained. The error probability of a simple detector suitable for biological applications is analyzed, and approximate and upper-bound results are provided. The impact of different parameters on the performance is also investigated.

Coding in Diffusion-Based Molecular Nanonetworks: A Comprehensive Survey

Diffusion-based molecular nanonetworks exploit the diffusion of molecules, e.g., in free space or in blood vessels, for the purpose of communication. This article comprehensively surveys coding approaches for communication in diffusion-based molecular nanonetworks. In particular, all three main purposes of coding for communication, namely source coding, channel coding, and network coding, are covered.We organize the survey of the channel coding approaches according to the different channel codes, including linear block codes, convolutional codes, and inter-symbol interference (ISI) mitigation codes. The network coding studies are categorized into duplex network coding, physical-layer network coding, multi-hop nanonetwork coding, performance improvements of network-coded nanosystems, and network coding in mobile nanonetworks. We also present a comprehensive set of future research directions for the still nascent area of coding for diffusion-based molecular nanonetworks; specifically, we outline research imperatives for each of the three main coding purposes, i.e., for source, channel, and network coding, as well as for overarching research goals.

Extended star graph as a light-harvesting-complex prototype: Excitonic absorption speedup by peripheral energy defect tuning.

We study the quantum dynamics of a photoexcitation uniformly distributed at the periphery of an extended star network (with N_{B} branches of length L_{B}). More specifically, we address here the question of the energy absorption at the core of the network and how this process can be improved (or not) by the inclusion of peripheral defects with a tunable energy amplitude Δ. Our numerical simulations reveal the existence of optimal value of energy defect Δ^{*} which depends on the network architecture. Around this value, the absorption process presents a strong speedup (i.e., reduction of the absorption time) provided that L_{B}≤L_{B}^{*} with L_{B}^{*}≈12.5/ln(N_{B}). Analytical and numerical developments are then conducted to interpret this feature. We show that the origin of this speedup takes place in the hybridization of two upper-band excitonic eigenstates. This hybridization is important when L_{B}≤L_{B}^{*} and vanishes almost totally when L_{B}>L_{B}^{*}. These structural rules we draw here could represent a potential guide for the practical design of molecular nanonetwork dedicated to the realization of efficient photoexcitation absorption.

Intra-body Networks and Molecular Communication Networks in Diagnostic Sciences.

Intra-body transmission networks are proposed to be composed of nanostructured or micro-sized detectors placed within the body to monitor health and distribute drugs. Transplanted biosensors are thepotential options for monitoring the bodyfor the detection of particular ailments and determining a diagnosis withthe help of a doctor. Biological systems inside the body remain intricately integrated and interact primarily through biochemical interactions. Thus, the continuous communication performance and intra-body molecular nanonetworks coordinate essential functions within the human body. Spontaneous intra-body molecular nanonetworks, on the other hand, have yet to be investigated using sophisticated tools of information and communication concepts. We intend to understand the exquisite molecular networkingthat exists within us to design and develop pragmatic, effective interaction for evolving nanonetworks and also to lay the groundwork for the progression of groundbreaking diagnosis and therapeutic methodologies inspired by technological tools, which have the potential to be helpful in future nanotechnologies and bioinspired molecular communication (MC) applications.

Evolutionary game theoretic resource allocation simulation for molecular communication

Molecular Communication (MC) is an emerging technology using molecules to transfer information between nanomachines. In this paper, we approach the resource allocation problem in Molecular Nano-networks (MCN) from the perspective of evolutionary game theory. In particular, we consider an MCN as an organism having three types of nodes acting as a sensor, relay, and sink, respectively. The resources are distributed among the nodes according to an evolutionary process, which relies on the selection of the most successful organisms followed by creating their offspring iteratively. In this regard, the success of an organism is measured by the total number of dropped messages during its life cycle. To illustrate the evolution procedure, we design a toy problem, and then solve it analytically and using the evolution approach for comparison. We further simulate the performance of the evolution approach on randomly generated organisms. The results reveal the potential of evolutionary game theory tools to improve the transmission performance of MCNs.

Anomaly Detection in Molecular Communications With Applications to Health Monitoring Networks

Early detection of diseases such as cancer plays a crucial role in their successful treatment. Motivated by this, anomaly detection in molecular nano-networks is studied. The proposed anomaly detection is, in fact, a two-tier network of artificial cells (ACs) in the first tier and a bio-cyber interface (BC) in the second tier. The ACs detect anomaly by variation in concentration of the biomarker molecules released by diseased cells (DCs) to the channel ending to ACs. This channel is modeled by a molecular communication (MC) paradigm. In the second tier, the ACs transmit a molecular message to a BC through the cardiovascular network, which is modeled again by the MC paradigm. A decision is made in the BC, which is implemented on the body skin, based on the received messages from different ACs. Due to the nature of the problem, a suboptimum design of detectors in the first and second tiers are provided. We use a Neyman-Pearson (NP) framework to analyze the detection performance of a health monitoring network. Based on bounding likelihood ratio (LR) a lower bound for the probability of detection and an upper bound for false alarm of each AC is derived. Next, taking into account the effect of ACs-BC channels, the overall performance of anomaly detection is analyzed in terms of probabilities of detection and false alarm.

Computation of decision problems within messages in DNA-tile-based molecular nanonetworks

Akyildiz et al. envisioned the use of nanonetworks as a new paradigm for computation and communication on a very small scale. We present a new approach to implement nanonetworks with molecular communication using tile-based self-assembly systems on the basis of DNA. In this model, the medium of communication is filled with DNA-based molecules. Furthermore, some nanobots are capable of creating or releasing said molecules. Once present, they can be detected by other nanobots and interpreted as messages. Some DNA-based molecule systems are capable of universal computation. We show that it is possible to construct systems, in which the evaluation to true of an arbitrary decision problem is a precondition for the assembly of a message molecule. We relocate computations from nanobots into message molecules, thereby revolutionizing the paradigm for computation in nanonetworks. This approach can be interpreted as computation inside the communication channel. We further present message molecules that only assemble if a marker has been detected at least k times, as a proof of concept.

Error Performance Optimization Using Logarithmic Barrier Function in Molecular Nanonetworks

The performance of a molecular signal detector relies on selected value of the detection threshold. In fact, arbitrary choice of detection threshold would increase either the probability of miss detection or the probability of false alarm. Thereby, an appropriate value of detection threshold is required in order to optimize the error performance of a realistic diffusion-based molecular communication (DbMC) system. For this, we come up with a new approach that yields optimal value for convex optimization problem in DbMC using logarithmic barrier followed by modified Karush–Kuhn–Tucker conditions, Newton Raphson method, and finally rounding the solution to the nearest integer value. Previous works on threshold optimization in DbMC either give suboptimal solution or optimal one whereof the convergence depends on choosing the smallest interval that contains the optimal value. Numerical and simulation results reveal that our proposed methodology can provide optimal solution with utmost accuracy in few iterations.

Monte Carlo Analysis of Molecule Absorption Probabilities in Diffusion-Based Nanoscale Communication Systems with Multiple Receivers.

For biomedical applications of nanonetworks, employing molecular communication for information transport is advantageous over nano-electromagnetic communication: molecular communication is potentially biocompatible and inherently energy-efficient. Recently, several studies have modeled receivers in diffusion-based molecular communication systems as "perfectly monitoring" or "perfectly absorbing" spheres based on idealized descriptions of chemoreception. In this paper, we focus on perfectly absorbing receivers and present methods to improve the accuracy of simulation procedures that are used to analyze these receivers. We employ schemes available from the chemical physics and biophysics literature and outline a Monte Carlo simulation algorithm that accounts for the possibility of molecule absorption during discrete time steps, leading to a more accurate analysis of absorption probabilities. Unlike most existing studies that consider a single receiver, this paper analyzes absorption probabilities for multiple receivers deterministically or randomly deployed in a region. For random deployments, the ultimate absorption probabilities as a function of transmitter-receiver distance are shown to fit well to power laws; the exponents derived become more negative as the number of receivers increases up to a limit beyond which no additional receivers can be "packed" in the deployment region. This paper is expected to impact the design of molecular nanonetworks with multiple absorbing receivers.

Nature inspired node density estimation for molecular nanonetworks

The problem of estimating the node density in ad hoc networks is a significant one for protocol design. In molecular nanonetworks, the node density estimation problem poses additional challenges due to the limited processing and communication capabilities of the network nodes which necessitate the design of simple to implement distributed solutions, and the diffusion based communication channel which is different from traditional electromagnetic networks. In this work, inspired by the quorum sensing process, we propose and analyze a new node density estimation scheme based on synchronous transmission of all network nodes and measurement of the received molecular concentration. We show that when the synchronous transmission is performed in infinite space, a linear parametric model of the node density can be derived which can be used for estimation purposes. When, however, the transmission is performed over a finite space the model becomes time varying. To overcome the difficulties associated with the time varying nature we propose the use of periodic transmission which for large enough values of the period transforms the linear model into a static one. An online parameter identification technique is then introduced to estimate the node density using the derived linear static parametric models. The utilization of the node density estimates to adaptively regulate probabilistic flooding in network structures relevant to nanonetworks is then considered. The random geometric graph model and uniform grid structures are used to demonstrate how the node estimates can be used to dictate the desired rebroadcast probabilities, through analysis and simulations.

Abnormality Detection in Correlated Gaussian Molecular Nano-Networks: Design and Analysis.

A nano-abnormality detection scheme (NADS) in molecular nano-networks is studied. This is motivated by the fact that early detection of diseases such as cancer plays a crucial role in their successful treatment. The proposed NADS is in fact a two-tier network of sensor nano-machines (SNMs) in the first tier and a data-gathering node (DGN) at the sink. The SNMs detect the presence of competitor cells (abnormality) by variations in input and/or parameters of a nano-communications channel. The noise of SNMs as their nature suggest is considered correlated in time and space and herein assumed additive Gaussian. In the second step, the SNMs transmit micro-scale messages over a noisy micro-communications channel (MCC) to the DGN, where a decision is made upon fusing the received signals. We find an optimum design of detectors for each of the NADS tiers based on the end-to-end NADS performance. The detection performance of each SNM is analyzed by setting up a generalized likelihood ratio test. Next, taking into account the effect of the MCC, the overall performance of the NADS is analyzed in terms of probabilities of misdetection and false alarm. In addition, computationally efficient expressions to quantify the NADS performance are derived by providing, respectively, an approximation and an upper bound for the probabilities of misdetection and false alarm. This in turn enables formulating a design problem, where the optimized concentration of SNMs in a sample is obtained for a high probability of detection and a limited probability of false alarm. The results indicate that otherwise ignoring the spatial and temporal correlation of SNM noise in the analysis, leads to an NADS that noticeably underperforms in operations.The results indicate how effective fusion of the noisy observations collected from a number of SNMs with limited capabilities could provide an acceptable detection performance.

A nanocommunication system for endocrine diseases

Nanotechnology is a new and very promising area of research which will allow several new applications to be created in different fields, such as, biological, medical, environmental, military, agricultural, industrial and consumer goods. This paper focuses specifically on nanocommunications, which will allow interconnected devices, at the nano-scale, to achieve collaborative tasks, greatly changing the paradigm in the fields described. Molecular communication is a new communication paradigm which allows nanomachines to exchange information using molecules as carrier. This is the most promising nanocommunication method within nanonetworks, since it can use bio-inspired techniques, inherit from studied biological systems, which makes the connection of biologic and man-made systems a easier process. At this point, the biggest challenges in these type of nanocommunication are to establish feasible and reliable techniques that will allow information to be encoded, and mechanisms that ensure a molecular communication between different nodes. This paper focus on creating concepts and techniques to tackle these challenges, and establishing new foundations on which future work can be developed. The created concepts and techniques are then applied in an envisioned medical application, which is based on a molecular nanonetwork deployed inside the Human body. The goal of this medical application is to automatously monitor endocrine diseases using the benefits of nanonetworks, which in turn connects with the internet, thus creating a Internet of NanoThings system. The concepts and techniques developed are evaluated by performing several simulations and comparing with other researches, and the results and discussions are presented on the later sections of this paper.

Novel network coding approaches for diffusion‐based molecular nanonetworks

AbstractThis paper proposes 2 novel network coding approaches for diffusion‐based nanonetworks. In these 2 methods, messages from nanomachines are jointly decoded at the receiver, and in one of the methods multiple molecule types are simultaneously employed to provide simultaneous communications over both nanomachine‐to‐relay and relay‐to‐nanomachine channels. As a result of the simultaneous use of the channel, each communication channel is allowed to use longer symbol intervals. This, in turn, results in a drastically reduced intersymbol interference component in all channels and enables communicating over longer ranges with higher data rates. The performance of the proposed communication system is investigated both analytically and via computer simulations.

Security Vulnerabilities and Countermeasures for Target Localization in Bio-NanoThings Communication Networks

The emergence of molecular communication has provided an avenue for developing biological nanonetworks. Synthetic biology is a platform that enables reprogramming cells, which we refer to as Bio-NanoThings, that can be assembled to create nanonetworks. In this paper, we focus on specific Bio-NanoThings, i.e, bacteria, where engineering their ability to emit or sense molecules can result in functionalities, such as cooperative target localization. Although this opens opportunities, e.g., for novel healthcare applications of the future, this can also lead to new problems, such as a new form of bioterrorism. In this paper, we investigate the disruptions that malicious Bio-NanoThings (M-BNTs) can create for molecular nanonetworks. In particular, we introduce two types of attacks: 1) blackhole and 2) sentry attacks. In blackhole attack M-BNTs emit attractant chemicals to draw-in the legitimate Bio-NanoThings (L-BNTs) from searching for their target, while in the sentry attack, the M-BNTs emit repellents to disperse the L-BNTs from reaching their target. We also present a countermeasure that L-BNTs can take to be resilient to the attacks, where we consider two forms of decision processes that includes Bayes’ rule as well as a simple threshold approach. We run a thorough set of simulations to assess the effectiveness of the proposed attacks as well as the proposed countermeasure. Our results show that the attacks can significantly hinder the regular behavior of Bio-NanoThings, while the countermeasures are effective for protecting against such attacks.

Influence of inter-nanoparticle interaction on nanonetworks-based molecular communications

With the rapid penetration of nanonetworks-based molecular communication (MC), many studies potentially investigated the influence of nanoparticles on the performance of transmission/delivering nanoscale information. In this study, we analytically investigate a new collision/adhesion-based MC framework by considering the inter-nanoparticles near field interaction such as van der Waals forces and the electronic structure of the nanoparticles in terms of hardness coefficient. In this context, we derive a closed-form expression of the required velocity for two nanoparticles to physically contact in terms of near field forces and hardness coefficient of the nanoparticles. In a consequence, we study the performance of the proposed MC framework in intra-body nervous system nanonetwork. Then, we evaluate the performance of the proposed nanonetwork-based mobile ad hoc molecular nanonetwork metrics of interest such as average packet delay, network throughput and sustained traffic rate. The numerical results reveal that when the mutual interaction between molecules is attractive force, it generally provides a better performance; more specifically, it can deliver information with lower delay than repulsive force although the hardness coefficient of nanoparticles is small.

Framework for single input single output nanonetwork-based realistic molecular communication.

Mobile ad hoc molecular nanonetwork (MAMNET) is a new paradigm for the realisation of future nanonetworks. In MAMNET, transmission of nanoscale information from nanomachine to infostation is based on collision and adhesion. In this study, the authors develop a realistic framework for encompassing the electronic structure of the neurotransmitter in the process of transmitting nanoscale information at a single input single output nanonetwork. Nanonetwork performance is evaluated in terms of average packet delay, throughput and incurred traffic rate. Numerical results demonstrate the influence of the neurotransmitter's electronic structure over the performance of nanonetworks.

A Communication Theoretic Analysis of Synaptic Channels Under Axonal Noise

Molecular communication is an emerging communication technology for applications requiring nanoscale networks. Transferring vital information about external and internal conditions of the body through the nervous system is an important type of intra-body molecular nanonetworks. Thus, investigating the performance of such systems from the communication theoretic perspective gives us insight on the limitation of neuro-spike communication and ways to design artificial neural systems. In this letter, we study the performance of the neuro-spike communication under different stochastic impairments such as axonal shot noise, synaptic noise, and random vesicle release. The objective is to optimally detect the spikes at the receiving neuron. Since several uncertainties occur under each hypothesis, composite hypothesis is employed to find the optimum detection policy. Furthermore, we obtain closed-form solutions for the optimal detector and derive the binary decision error at the postsynaptic terminal.

A queueing-theoretical delay analysis for intra-body nervous nanonetwork

Nanonetworks is an emerging field of study where nanomachines communicate to work beyond their individual limited processing capabilities and perform complicated tasks. The human body is an example of a very large nanoscale communication network, where individual constituents communicate by means of molecular nanonetworks. Amongst the various intra-body networks, the nervous system forms the largest and the most complex network. In this paper, we introduce a queueing theory based delay analysis model for neuro-spike communication between two neurons. Using standard queueing model blocks such as servers, queues and fork-join networks, impulse reception and processing through the nervous system is modeled as arrival and service processes in queues. Simulations show that the response time characteristics of the model are comparable to those of the biological neurons.

On Error Performance of Network Coding in Diffusion-Based Molecular Nanonetworks

This paper investigates the error performance of network coding in diffusion-based molecular nanonetworks. Closed-form expressions for error probability have been derived by adopting pulse-based modulation with energy detection, and modeling diffusion noise and interference effects. The effect of different parameters on error performance has also been investigated.