Modeling, Methodologies and Tools for Molecular and Nano-scale Communications

Concentration-encoded molecular communication (CEMC) is a technique in molecular communication (MC) paradigm where information is encoded into the amplitude of the transmission rate of molecules at the transmitting nanomachine (TN) and, correspondingly, the transmitted information is decoded by observing the concentration of information molecules at the receiving nanomachine (RN). In this chapter, we particularly focus on the fundamentals, issues, and challenges of CEMC system towards the realization of molecular nanonetworks. CEMC is a simple encoding approach in MC using a single type of information molecules only and without having to alter the internal structure of molecules, or use distinct molecules. Despite its simplicity, CEMC suffers from several challenges that need to be addressed in detail. Although there exists some literature on MC and nanonetworks in general, in this chapter, we particularly focus on CEMC system and provide a comprehensive overview of the principles, prospects, issues, and challenges of CEMC system.

As discussed in the previous chapter, concentration-encoded molecular communication (CEMC) is an information encoding approach to molecular communication (MC) where a transmitting nanomachine (TN) encodes information by varying the transmission rate of molecules, and correspondingly, a receiving nanomachine (RN) decodes the transmitted information by observing the concentration of information molecules available at the RN. While the previous chapter basically dealt with the fundamentals, issues, and challenges of CEMC system, the main objective of this chapter is to particularly focus on performance evaluation of CEMC system in detail. Understanding a single CEMC link completely and accurately is of utmost importance in order to fully understand CEMC-based molecular nanonetworks in the emerging biological information and communication technology (bio-ICT) paradigm. Hence this chapter focuses on the performance evaluation of a single-link CEMC system between a pair of nanomachines.

This chapter comes up with a brief overview of molecular communication models and modulation techniques by reviewing current research works found in the literature. The chapter also provides with an analysis of molecular communication in free diffusion-based molecular communication channel. In this model, the trasmitter nanomachine releases messenger molecules, the molecules diffuse through the channel, and the receiver nanomachine counts the received molecules to decode the information. We consider free diffusion of molecules where no additional force is required. Such a channel is referred to as the diffusion channel and can be modeled by using Ficks law of diffusion. Diffusion coefficient describes the tendency of propagation of the messenger molecules through the medium. Analysis shows that, channel memory offers a significant impact on performance.

Nanonetworking is a recently proposed paradigm that aims to achieve collaboration between nanomachines to carry out complex tasks. Molecular communications has been the most vibrant area of research for nanonetworking, mostly because of its feasibility and existence of communication schemes similar to molecular communications in nature. In molecular communications, two nanomachines communicate with each other via propagation of molecules from the transmitter to the receiver nanomachines through the medium they reside in. How and where to encode the message, i.e. modulation, plays a key role in molecular communications since it greatly affects the communication performance at nanoscale. To this end, in this paper, we examine the landscape of modulation in molecular communications, categorize the modulation schemes in molecular communications by methodology and discuss how convenient they are in terms of synchronization requirements in a nanoscale environment and their biocompatibility for applications inside human body.

Molecular Communication via Diffusion (MCvD) is an effective and energy efficient method for transmitting information in nanonetworks. In this chapter, we focus on the modulation techniques in a diffusion-based communication system. We mainly assume the first hitting process for the reception of the signal and it affects the design of the modulation techniques. As observed in the nature, whenever an information carrying molecule hits to the receiver it is removed from the environment. These information molecules are called messenger molecules and can be of many types of chemical compounds such as DNA fragments, proteins, peptides or specifically formed molecules. Information is modulated on one or more physical properties of these molecules or the release timing. In this chapter, we mention four novel modulation techniques, i.e., concentration, frequency, molecular-type, and timing-based modulations for MCvD in a single transmitter and single receiver environment. We also exemplify a systematic realization for molecular-ratio-based modulation using isomers as messenger molecules for MCvD. Next, we compare the pros and cons of the modulation techniques for an absorbing receiver that are studied in the literature. Knowing the workings and the properties of these modulation techniques enables us to use them in combination whenever it is possible.

This chapter focuses upon the use of coding and protocols within diffusion based molecular communication systems, laying the groundwork for future development in test bed implementations. The chapter starts with an introduction that briefly discusses coding and protocols used in traditional communication systems. Following this, details of the molecular channel are given, including the energy consumption constraints and a relevant mathematical framework. This discussion then leads onto potential encoding and decoding technologies. Next, original results on the use of Hamming codes in molecular communication systems are presented with a quantitative comparison against an uncoded molecular system. The impact of specific design parameters such as the number of molecules, energy, and transmission distance on the bit error rate (BER) is considered. Finally, a protocol, based upon the use of an acknowledgement (ACK) packet is presented as a further advancement to the field that the reader may wish to consider when designing future systems.

Understanding Communication via Diffusion (CvD) is key to molecular communications research since it dominates the movement at the nano-scale. The researcher needs to properly understand the random diffusion of the molecules for the analysis of a molecular communication system. This chapter aims explaining the dynamics of diffusion from a communication engineer’s perspective as well as providing useful hints for an effective simulation design by discussing some key intricacies. The chapter starts with a brief survey of simulators for molecular communications, followed by the basics of the simulation of Brownian motion and CvD. Several intricacies are addressed to help the researcher in simulation design, such as the number of replications required in terms of movement and bit sequence. We utilize this information further by discussing the design of more complex CvD systems such as tunnel-based approach that utilizes destroyer molecules and distributed simulator design based on HLA. Introduction of more complex CvD systems provides significant improvements in data rate and communications in general, bridging the gap between human-scale and nano-scale systems and enabling nanonetworking as a viable technology.

The Nobel laureate physicist Richard Feynman, in his famous speech in 1959 entitled “There’s Plenty of Room at the Bottom”, has pointed out the concepts in nanotechnology and described how the manipulation of individual atoms and molecules would give rise to more functional and powerful man-made devices.

In this chapter strategies for controlling neuronal communication and behavior from a theoretical neuroscience perspective are discussed. The main motivation for controlling neuronal networks is to slow down, halt or reverse mental diseases like senile dementia and Alzheimer’s disease (AD (a list of acronyms most used in this chapter is provided in Table 1)), as well as other mental diseases that reduce quality of life. The concept of neuronal networks used in this chapter denotes a group of interconnected biological neurons and must be distinguished from the concepts of neural networks (group of interconnected nerves) and artificial neural networks (group of interconnected “neurons” used in computer science).

The development and advancement of nanomedicine has opened up many exciting, new applications of nanoparticles such as sensing, imaging, delivery, and therapy. However, their ability to readily enter cells and organelles that allow these nanomedical applications also opens up the possibility of unintended adverse nanotoxicity. The interaction between nanoparticles and biomolecules results in biocorona formation on the nanoparticle surface that is very different from adsorption of biomolecules on a flat surface. It remains a great challenge to understand the applications and risks associated with nanoparticles being in contact with biological systems beyond experimental methods that have limited resolution of the interactions and conformational changes involved. Recently, biomolecule-nanoparticle molecular dynamics (MD) simulations are becoming a viable approach for a detailed view of biocorona formation. In this review, we present the advantages and challenges of several MD simulation approaches for the study of biomolecule-nanoparticle interactions. In particular, we argue for the development of GPU-optimized MD simulations as a critical step in the study of biocorona formation. We discuss recent successes on how integrated computational and experimental studies are important to establish how the structure and functions of biomolecules are affected by nanoparticle interactions with the biomolecules.

In molecular biology of the cell, cell communication is defined as the process carried out by chemical signals within and among cells. The informatics issue of cell communication in this book chapter is to uncover the principles of the bioinformatics of cell communication by means of communication engineering, e.g., the statistical tool for performance analysis of communication processes. As we know well by now, the state of the art of molecular science has been reshaped by advanced technologies since the genome sequencing became a reality. In accordance with nowadays available nanotechnology for molecular signal detection, we apply communication engineering technology in the theoretical analysis of cell communication whose goal is to discover the mechanism of cell communication that determines the cellular functions connected with applications in medicine. Though intensive research has been devoted to the biochemistry of signaling pathways, laying a strong scientific foundation for the informatics study of communication processes of the cell in the form of signaling pathways, the study of the communication mechanism of signaling pathway networks in the cell—cell communication—by means of communication engineering is still a relatively new field, where supporting technologies from multiple disciplines are needed. In this book chapter, the formulation of the cell communication mechanism of signaling pathway networks using martingale measures for random processes is proposed and the performance of the cell communication system constructed by the signaling pathways in simulation studies is evaluated from the viewpoint of communication engineering. From the computational analysis result of the above cell communication process, it is concluded that the modeling method in this study not only is efficient for bioinformatics analysis of biological cell communication processes but also provides a reference framework for brain communication towards its application in molecular biomedical engineering.

Biological networks are known to be robust despite signal disruptions such as gene failures and perturbations. Extensive research is currently under way to explore biological networks and identify the underlying principles of their robustness. Structural properties such as power-law degree distribution and motif abundance have been attributed for robust performance of biological networks. Yet, little has been done so far to quantify such biological robustness. We propose a platform to quantify biological robustness using network simulator (NS-2) by careful mapping of biological properties at the gene level to that of wireless sensor networks derived using the topology of gene regulatory networks found in different organisms. A Support Vector Machine (SVM) learning model is used to measure the correlation of packet transmission rates in such sensor networks. These sensor networks contain important topological features of the underlying biological network, such as motif abundance, node/gene coverage, and transcription-factor network density, which we use to map the SVM features. Finally, a case study is presented to evaluate the NS-2 performance of two gene regulatory networks, obtained from the bacterium Escherichia coli and the baker’s yeast Sachharomyces cerevisiae.

In the broad sense of the term, nanonetworks may refer not just to networks composed of nanosized devices, but also to communication networks enabled by nanotechnology. Nanoscale communication techniques can be suitable to interconnect elements far larger than a few square micrometers in applications subject to strong size constraints or bandwidth requirements. Here, the concept Graphene-enabled Wireless Network-on-Chip (GWNoC) is introduced as a clear example of this category. In GWNoC, graphene plasmonic antennas are used to wirelessly communicate the components of a multicore processor, which are located in the same chip. This shared medium approach is opposed to current chip communication trends and aims to reduce many of the issues that hamper the development of scalable multiprocessor architectures. In this chapter, we describe the scenario and the communication requirements that justify the employment of nanonetworking techniques, as well as the main challenges that still need to be overcome in this new research avenue.

The goal of this chapter is to review the process, issues, and challenges of energy harvesting in nanonetworks, composed of nanonodes that are nano to micro meters in size. A nanonode consisting of nan-memory, a nano-processor, nano-harvesters, ultra nano-capacitor, and a nano-transceiver harvests the energy required for its operations, such as processing and communication. The energy harvesting process in nanonetworks differs from traditional networks (e.g. wireless sensor networks, RFID) due to their unique characteristics such as nanoscale, communication model, and molecular operating environment. After reviewing the energy harvesting process and sources, we introduce the communication model, which is the main source of energy consumption for nanonodes. This is followed by a discussion on the models for joint energy harvesting and consumption processes. Finally, we describe approaches for optimizing the energy consumption process, which includes optimum data packet design, optimal energy utilization, energy consumption scheduling, and energy-harvesting-aware protocols.

Nanoscale communication is a novel and quite interdisciplinary research area which aims to design and develop communication networks among nano-size machines to extend their limited capabilities for groundbreaking biomedical, industrial and environmental applications [1].

Nanofluids are smart colloidal suspensions of fine nanomaterials in the size range of 1–100 nm in base fluids. For the last few years, nanofluids have been an important focus of research, due to their superior thermo physical properties and promising heat transfer applications. Regardless of various experimental studies, it is still unclear whether the thermal conductivity enhancement in nanofluids is anomalous, or lies within the predictions of theoretical models. Moreover, most of the reported values on their thermo physical properties are inconsistent, due to the complexity associated with the surface chemistry of nanofluids. In this chapter, the versatility of ultrasonics, as an effective non-invasive tool in characterizing nanofluids, is discussed. The chapter encompasses the significance and measurement methods of various ultrasonic parameters. The ultrasonic investigations, being non-invasive in nature, highly efficient and relatively cheap, can provide a powerful means to explore complex colloidal systems, like nanofluids and ferrofluids.

This chapter gives an intensive overview of some recent micro‐ and nanostructured Radio Frequency (RF) security issues. It identifies the challenges of tomorrow’s security problems and why this has been a big relevance not only to nano-communications but also to other applications. A short overview on the traditional Physical Unclonable Functions (PUFs) introduces the reader into the concept of applied electromagnetic waves interacting with nanomaterials. Major security and fingerprinting contributions, which are newly‐proposed and implemented by the authors, are concluded in this chapter. These security techniques are based on artificially‐synthesized disordered micro and nano materials. A potential on‐chip realization and integration scenario of such approach is also discussed. Novel material synthesis technologies and functional prototype production processes are illustrated. Extraction process of RF fingerprints, based on near‐field scattering measurements, is included as well. Finally, statistical analysis and distance measures of similarity, uniqueness and orthogonality of the extracted fingerprints are carefully investigated at the end of this chapter.

The conventional CMOS technology faces various challenges in the continues down-scaling. Therefore, different emerging technologies based on bottom-up and self-assembly nanofabrications are being explored to overcome these challenges. These technologies exploit different nano-materials in the regular structures such as the crossbar nano-architecture, which is a two-dimensional grid with configurable switches at the crosspoints. Exploiting nano-materials in crossbar nano-architectures offers the possibility of significantly denser circuits at reduced fabrication costs compared to the existing lithography-based manufacturing. However, in these nano-architectures atomic device sizes and poor control on the fabrication processes impairs the reliability of these circuits. In this chapter, we investigate reliability issues in crossbar nano-architectures in terms of variation and defect tolerance. We study two approaches, namely logic mapping and self-timed architecture design, to provide variation and defect tolerance. In the logic mapping approach, different configurations, a.k.a mappings, of a logic function on a crossbar nano-architecture are explored to find the configuration with the required variation and defect tolerance. Simulation results, on a set of benchmark circuits, show that the proposed logic mapping approach achieves variation tolerance more than 98% of the cases, while in 100% of the cases all defects are tolerated. The efficiency of these algorithms is independent of crossbar size. At the architecture-level, a self-timed nano-architecture is introduced to reduce the circuit vulnerability to delay variations. Compared to the synchronous counterparts, with around 50% overhead in the number of activated switches, the proposed architecture provides 100% tolerance of delay variations.

This chapter illustrates the analysis of a nano-communication system, implemented in blood vessels, designed for detecting tumor cells. This system may be used for diagnostic purposes in the early stage of a disease or to check any relapse of a previous disease already treated.The tumor detection happens through revealing tumor biomarkers, such as the CD47 protein, on the cell surface. Once a biomarker has been revealed, a molecular communication system distributes the information to a number of nano-machines having a size allowing them to flow through the vessel at the maximum speed of the bloodstream. The final information detection is extra-body, and based on a smart probe, which triggers a decision tree computing which aims to find if any tumor is present and the most likely location. Effects of aging and serious disease, such as the diabetes, are highlighted.

The human body is a massive nanoscale molecular communications network composed of billions of interacting cells Drug delivery is an important application of molecular communication. Nanogels are aqueous dispersions of nanoscale size formed by cross-linking of hydrophilic polymers, capable of retaining large amounts of water yet remaining insoluble and maintaining a three-dimensional structure. Nanogel structure enables easy attachment of vector groups for effective communication with cells to reach the desired targeted site. This chapter highlights communication of drug loaded nanogels for targeting cancer through receptors. The chapter critically discusses receptors like- integrin αvβ3, EphA2, folate, Hyaluronan and monoclonal antibody for communication with nanogels.

In this chapter, we present a cyber-physical approach towards the design of bio-hybrid micro-robotic swarms that can achieve various complex tasks at micro-level in a minimal invasive manner, such as abnormal tissue detection and drug delivery in hardly accessible regions of the human body. To this end, we cease to view micro-robots as passive point-like particles, but rather as interactive Turing machines performing complex biochemical processing, as well as physical interactions in a realistic 3D environment. Our theoretical framework is based on a non-equilibrium statistical physics approach capable of accounting for attraction and repulsion interactions among micro-robots, as well as the volume exclusion effects. To account for biological sensing, interacting, actuation dynamics, and the 3D complex tumor microenvironment, we also use an open-source 3D multiscale simulator specifically developed for this research. Taken together, the theoretical framework and computational platform we develop can enable various design trade-offs of interacting bio-hybrid micro-robotic swarms for future medical applications.

Advanced nucleic acid-based sensor-applications require computationally intelligent biosensors that are able to concurrently perform complex detection and classification of samples within an in vitro platform. Realization of these cutting-edge computational biosensor systems necessitates innovation and integration of three key technologies: molecular probes with computational capabilities, algorithmic methods to enable in vitro computational post processing and classification, and immobilization and detection approaches that enable the realization of deployable computational biosensor platforms. We provide an overview of current technologies, including our contributions towards the development of computational biosensor systems.

Nano-technology is rapidly maturing to help realize bio-chemical sensors that can be implanted in human body with integrated transceivers to share symptoms’ data with each other internally to the body, and with outside world.