Nanonetworks E-Book

Florian-Lennert A. Lau

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 823

Veröffentlichungsjahr: 2024

Beschreibung

Learn the basics—and more—of nanoscale computation and communication in this emerging and interdisciplinary field

The field of nanoscale computation and communications systems is a thriving and interdisciplinary research area which has made enormous strides in recent years. A working knowledge of nanonetworks, their conceptual foundations, and their applications is an essential tool for the next generation of scientists and network engineers. Nanonetworks: The Future of Communication and Computation offers a thorough, accessible overview of this subject rooted in extensive research and teaching experience. Offering a concise and intelligible introduction to the key paradigms of nanoscale computation and communications, it promises to become a cornerstone of education in these fast-growing areas.

Readers will also find:

• Detailed treatment of topics including network paradigms, machine learning, safety and security
• Coverage of the history, applications, and important theories of nanonetworks research
• Examples and use-cases for all formulas and equations

Nanonetworks is ideal for advanced undergraduate and graduate students in engineering and science, as well as practicing professionals looking for an introductory book to help them understand the foundations of nanonetwork systems.

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Thomas Robertazzi

James Duncan

Joydeep Mitra

Diomidis Spinellis

Ekram Hossain

Desineni Subbaram Naidu

Nanonetworks

The Future of Communication and Computation

Florian-Lennert A. Lau

Universität zu LübeckInstitute of TelematicsLübeck, Germany

Copyright © 2024 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data

Names: Lau, Florian-Lennert A., author.

Title: Nanonetworks : the future of communication and computation /

  Florian-Lennert A. Lau.

Description: Hoboken, New Jersey : Wiley, [2024] | Includes bibliographical

  references and index.

Identifiers: LCCN 2024009796 (print) | LCCN 2024009797 (ebook) | ISBN

  9781394213108 (hardback) | ISBN 9781394213115 (adobe pdf) | ISBN

  9781394213122 (epub)

Subjects: LCSH: Nanonetworks.

Classification: LCC TK7874.845 .L38 2024 (print) | LCC TK7874.845 (ebook)

  | DDC 621.39/81–dc23/eng/20240314

LC record available at https://lccn.loc.gov/2024009796

LC ebook record available at https://lccn.loc.gov/2024009797

Cover Design: WileyCover Images: © Andriy Onufriyenko/Getty Images

List of Figures

Figure 1.1

Soccer Ball vs. Earth

Figure 1.2

Size Comparison Between Different Structures

Figure 1.3

Mindmap of Nanotechnologies

Figure 1.4

Electric Nanorobot

Figure 2.2

Early Chopping Tools

Figure 2.2

A 200 000-year-old hand ax (a) and a 30 000-year-old statue (b)

Figure 2.3

Ancient Egypt Potter’s Wheel

Figure 2.4

Damascene steel

Figure 2.5

The Germanic sword “Ulfberth”

Figure 2.6

Small Scale Manufacturing Methods

Figure 2.7

Healthy and Cancerous Cells in Comparison

Figure 2.8

3D-Printed Micro Structure

Figure 2.9

Some Allotropes of Carbon

Figure 2.10

An Array of Microbots

Figure 2.11

A Box From DNA-Origami

Figure 2.12

An Overview of Tile DNA-Nanostructures

Figure 2.13

An Overview of DNA-Nanostructures

Figure 2.14

An Artificial Living Organism Created From Frog Cells

Figure 3.1

Storyboard of a Nanomedicine Scenario

Figure 3.2

Nanoparticles in Drug Delivery

Figure 3.3

Maslow’s Hierarchy of Needs

Figure 4.1

Carbon Atom

Figure 4.2

An Example Molecule

Figure 4.3

c60 and c540 fullerenes. (a) c60 fullerene, also called “Buckminsterfullerene.” (b) c540 fullerene

Figure 4.4

Different Types of Carbon Nanotubes

Figure 4.5

A rope made out of carbon nanotubes

Figure 4.6

An Example DNA-Helix Segment

Figure 4.7

Timeline of DNA Discoveries

Figure 4.8

Venn-Diagramm of Nanostructures

Figure 4.9

The State Space for a MDP

Figure 4.10

An MDP With Sand Pit and Charging Station

Figure 4.11

An Example Policy for a Nanodevice

Figure 4.12

A POMDP Example Scenario

Figure 4.13

ADecPOMDP Example

Figure 4.14

An Example DecPOMDPcom

Figure 4.15

Biological Nanorobot

Figure 4.16

Bacterial Nanorobot

Figure 4.17

Liposomes and Micelles

Figure 4.18

Example Circuit

Figure 4.19

Self-Assembled Snowflakes

Figure 4.20

DNA-Origami

Figure 4.21

Wang-Tiles

Figure 4.22

DX and TX-Tile

Figure 4.23

Holliday Junction

Figure 4.24

Tiletype examples

Figure 4.25

DNA-Tile in the Process of Binding

Figure 4.26

(a) 2D-Tileset (b) Assembly Sequence of a TAS

Figure 4.27

Growth- and Facet-Errors

Figure 4.28

(a)

k

×

k

Proofreading Tiles. (b) Snaked Proofreading Tiles

Figure 4.29

Odd/even Snaked-Block

Figure 4.30

3D-Snaked Proofreading

Figure 4.31

HollowCube of Edge Length 5

Figure 4.32

Linear Runtime Hollow Cube

Figure 4.33

Constant Size Square Tileset

Figure 4.34

Square of Logarithmically Many Tile Types

Figure 5.1

Inclusion Diagram of Complexity Classes

Figure 5.2

Reduction Scheme

Figure 5.3

QCA Neighborhoods

Figure 5.4

Quantum Dot Cell With Tunnel Junctions

Figure 5.5

Binary Interpretation of QCA States

Figure 5.6

Majority Gate

Figure 5.7

Invertergatter

Figure 5.8

(a) Tileset that assembles into a 4-bit AND at temperature 2. (b) Resulting message molecule

Figure 5.9

Ligand of a Message Molecule

Figure 5.10

Receptor for Message Molecules

Figure 5.11

4 bitAND-Nanonetwork

Figure 5.12

Messages Molecule Without Nucleation Errors

Figure 5.13

Message Combination With Ligand

Figure 5.14

General Boolean Tileset Construction

Figure 5.15

Message Molecule for the Decision Problem THRES

Figure 5.16

Message Molecule for the function problem ADD

Figure 5.17

Message Molecule for the Function Problem MULT

Figure 5.18

Message Molecule for the Function Problem XOR

Figure 5.19

Message Molecule for the Counting Problem

Figure 5.20

Complexity of Different MDP Variations

Figure 5.21

A Lifted DecPOMDPcom

Figure 6.1

Average Data Rates Over Time

Figure 6.2

Connecting in-Body and out-Body

Figure 6.3

Molecular Communication Channel Model

Figure 6.4

Receptor Ligand Interaction

Figure 6.5

Ligand of a Message Molecule

Figure 6.6

Receptor for Message Molecules

Figure 6.7

The Architecture of a FCNN Network

Figure 6.8

Example Nanonode Distributions

Figure 6.9

Hop Count Network After a Reset

Figure 6.10

An MST After the propagation Phase

Figure 6.11

The Retrieval-PhaseWorst-Case

Figure 6.12

Number of propagation messages sent

Figure 6.13

Destructive Retrieval Message Number

Figure 7.1

The Process of Chemotaxis

Figure 7.2

A Motor Protein Moving Cargo Along a Track

Figure 7.3

Bubble Propulsion

Figure 7.4

Overlapping Hop-Count Zones

Figure 7.5

The Initial 3D Hop Count State

Figure 7.6

The 3D Hop Count State After Propagation

Figure 7.7

The 3D Hop Count State With Real Distances

Figure 7.8

3D Hop Counts in a Human Model

Figure 7.9

Proteom Fingerprint Strengths

Figure 7.10

BloodvoyagerS Circulatory System Model

Figure 7.11

Model of Individual Blood Vessels in Nanonetworks

Figure 7.12

Different Modules of MEHLISSA

Figure 8.1

Diagnostic Procedures Overview

Figure 8.2

Overview of Quantitative Procedures of Laboratory Analytical Methods

Figure 8.3

A CNT Sensors

Figure 8.4

Molecule Counter

Figure 8.5

DNA-Box Dispenser

Figure 9.1

A Generic Fuel Cell

Figure 9.2

The Broadcast Storm

Figure 9.3

Harvesting vs. Sending Duration

Figure 9.4

Compariosn of Different Message Retrieval Schemes

Figure 9.5

Conical Signal Propagation in Hop Count Network

Figure 9.6

Obstacles in Hop-Count Routing

Figure 9.7

SLR Routing With Hindrances

Figure 9.8

Ring-Saving in Hop-Count Nanonetworks

Figure 9.9

Naive Flooding vs. Ring Saving

Figure 9.10

SLR vs. Ring Saving + SLR

Figure 10.1

Several Example Quartz

Figure 10.2

Clock Drift

Figure 10.3

NTPv4 Architecture

Figure 10.4

QCA Clock

Figure 10.5

Dysfunctional QCA Majority Gate

Figure 10.6

Lanmport Clock Example

Figure 10.7

The Chandy-Lamport Snapshot Algorithm

Figure 10.8

Sequential Consistency

Figure 10.9

Transitivity And Consistency

Figure 10.10

Causal Consistency

Figure 10.11

Langton’s Ant Simulation

Figure 11.1

Nanonetwork Safety Architecture

Figure 12.1

IoNT Reference Architecture

Figure 12.2

A Body Area Network

Figure 12.3

The SwarmNetwork Rules

Figure 12.4

An Example Acoustic Nanonetwork

Figure 12.5

An Example Electromagnetic Nanonetwork

Figure 12.6

Nanonetwork on Chip Architecture

Figure 12.7

An Example Bacterial Nanonetwork

Figure 12.8

An Example Molecular Nanonetwork

Figure 12.9

DNA-Based Nanonetwork Reference Architecture

Figure 12.10

4-bit in 2HAM

Figure 12.11

Result of 100 Simulations of a 4 Bit-AND

Figure 12.12

3 Bit-THRES-Tileset

Figure 12.13

THRES as a Nanonetwork

Figure 12.14

Result of 50 kTAM Simulations for a 3 Bit-THRES

Figure 12.15

4 bitADD Tileset

Figure 12.16

ADD as a Nanonetwork

Figure 12.17

Result of 50 kTAM -Simulations for ADD

Figure 12.18

General Boolean Tileset Construction

Figure 12.19

A DNA-Based Nanonetwork for Boolean Formulas

Figure 12.20

Turing Machine to Tiles Reduction

Figure 12.21

Reference Architecture for a Tile-Based Turing Machine

Figure 12.22

Phase 1 Explanation

Figure 12.23

Phase 2 Explanation

Figure 12.24

Message Molecule for the Counting Problem

Figure 12.25

Unary Counting Molecule Approximation in NetTAS

Figure 12.26

Sierpinski Triangle

Figure 13.1

Reasons to use Nanonetworks

Figure 13.2

Readiness to Nanotechnology on Recommendation

Figure 13.3

Future Directions

List of Tables

Table 3.1

2021 US BIP

Table 4.1

CNT Properties

Table 4.2

Comparision of Molecular Communiccation Types

Table 4.3

Example Assembly Process in the ktHAM

Table 5.1

Formal Definition of Operations for Nanonetworks

Table 5.2

List of Operations Sorted Into Complexity Classes

Table 5.3

Example Truth Table

Table 6.1

Communication Types on Different Scales

Table 6.2

Comparison Between Communication Types and Their Parameters

Table 6.3

Comparision of Molecular Communiccation Types

Table 7.1

Example Proteome Fingerprint

Table 9.1

Ring Saving Simulation Parameters

Table 12.1

Acoustic Nanonetwork Specification & Parameters

Table 12.2

EMC Nanonetwork Specification & Parameters

Table 12.3

Bacterial Nanonetwork Specification & Parameters

Table 12.4

Molecular Nanonetwork Specification & Parameters

Table 12.5

DNA-Based Nanonetwork Specification & Parameters

Table 12.6

AND Message Molecule Sub-Assemblies

Table 12.7

THRES Message Molecule Sub-Assemblies

Table 12.8

Tile Complexity of Operations

About the Author

Florian-Lennert A. Lau attained his bachelor’s degree in computer science in 2013, followed by his master’s degree with a focus on computational complexity theory in 2016. Since 2016, he has been actively engaged as a PhD student at the German University of Lübeck. Distinguished as the head of the pioneering Nano Group Lübeck, established in 2016, he has significantly influenced the field of nanonetworks by introducing DNA as a multi-purpose material.

In 2020, Florianwas awarded the title of best PhD thesis by the special interest group “Communication and Distributed Systems” (KuVS). Subsequently, he transitioned to the role of postdoctoral researcher at the University of Lübeck.

Next to several private publications, Florian authored his inaugural scientific book, Nanonetworks – The Future of Communication and Computation, in 2024. His research is intricately focused on DNA-based self-assembly systems and their pioneering applications in nanorobots and nanonetworks, marking him as a leading authority in this field.

Preface

At the time of writing this book, I was unknown in the world of nanonetworks. However, people usually judge the credibility of new information by the reputation of the person giving it to them. Since I have no reputation, I would encourage you to come and see for yourself if the information in this book is any good. I have tried my very best to explain everything as pragmatically and directly applicable as possible. As Richard Feynman once said, “If you cannot explain something in simple terms, you don’t understand it.”

In order to enable the largest possible audience to read a scientific work, it should be written as simply and unconditionally as possible. The understanding of a text can be accelerated, for example, by using repetitions in appropriate places. This may sometimes irritate an expert, but the additional explanations benefit inexperienced but interested readers. For a thorough understanding of the following work, however, it is assumed that the reader has the basics of complexity theory, as well as logic and general computer science, or is willing to acquire them using the referenced secondary literature. The repetition of all the basics is far beyond the scope of a single book. Mathematical procedures are always chosen in the course of the work in such a way that they provide practical use. For example, theoretical proofs of concepts are nice but often of little practical relevance. By means of a constructive approach or proof, however, it is possible to use results effectively or to derive procedures that offer real added value.

Why This Book

Before we begin with anything else, it is good to understand the reasons and driving forces behind this book. The field of nanonetworks is, in several regards, special and cannot be easily compared to other areas of research.

First and foremost, nanonetworks is a young discipline that has only been introduced as recently as 2008. That said, that is only true for the area itself – other, much older sciences have been and are still contributing to this area for a very long time. Like any other discipline, nanonetworks are also standing on the shoulders of giants.

Due to the interdisciplinary nature of the field, nanonetworks are standing on a surprising number of giants. Unlike other areas, nanonetworks incorporate results from biology, chemistry, physics, medicine, computer science, mathematics, and engineering, to name just the major scientific fields. As a result, researchers have to be well-educated in several of those domains. Concerning all other domains, contributors should at least be aware of all the major ideas and problems. Consequently, especially new researchers can have a difficult time entering this vast area of research, and it can be quite intimidating to even start.

Additionally, the many different backgrounds also lead to a number of problems concerning wording and definitions. It is not rare that identical words mean very different things to the contributors. As a result, communication can be surprisingly difficult, and it is necessary to find a common ground and vocabulary to effectively communicate and avoid doing research twice.

Further, nanonetworks still sound extremely futuristic to the average person, and it is surprisingly difficult to tell science fiction and actual science apart from each other. This is only aggravated by the general tendency of the media to unnecessarily blow up headlines. Headlines are so commonly exaggerated that it is more and more difficult to tell the hype and the actual science apart from each other.

All of those aspects and many more led me to the conclusion that it is time for a somewhat general book that summarizes all the major ideas and problems in this area. We need a common language and a good entry point for new researchers if we want to achieve lasting progress as a community. Interdisciplinary research can be challenging, but given the right prior education, it can be done effectively and efficiently.

Important Remarks

Before we start with any tutorial on reading order or anything else, a few words on the peculiarities of the book.

While I avoid opinions in a book such as this whenever possible, there will be some occasions where I make predictions about the future. In those and a few other cases, the use of opinions is signified by the use of “I,” “me,” or “mine.” Thus, it should be clear at any point what is an opinion and what is following the scientific publications.

If you are completely new to the topic of nanonetworks, I would advise you to read all the presented content in the displayed order. If you are just interested in a single topic, you can jump to the respective chapter by clicking on the elements in the table of contents. All of the chapters can be read on their own and possess the necessary introductions and connecting elements that allow for an enjoyable and informative read. It could be beneficial to also read the preface and the introduction in any case, as that helps convey the idea behind this book.

As this is an interdisciplinary book, some topics will not be covered in the depth they might deserve. This book is mainly written for computer scientists and engineers; it should offer a good entry point for scientists from other areas too. Computer science is a tool science where it is often necessary to learn a new field in a short period of time. During that time, it is often necessary to isolate and understand the big questions and problems in the field. Thus all the relevant aspects of a field will likely be covered, but some details will likely be missing.

Acknowledgments

At this point, I would like to thank a few people who have actively or passively contributed to the success of this work – some of the people mentioned might not even be aware of it. My thanks go to these people:

I thank my parents, who always gave me the necessary freedom to do what I wanted. They taught me as much autonomy as needed to allow me to make advanced progress and develop a certain pride in my own achievements – this is the basis of my ambition and drive.

I thank my grandmother for being a kind-hearted person.

I would like to thank my former high school teachers, Peter Koerting and Wolfgang Malm, who taught me a deep need for formal correctness and an affection for the natural sciences.

I would like to thank Rüdiger Reischuk for taking the time and patience to put the knowledge I gained during my studies to the test.

I would like to thank Bennet Gerlach for the fact that he is available to me almost every day for discussion and reflection and thus closed many gaps in my school knowledge that would otherwise still exist today. Without him, my model of the world would be fundamentally different – and, I think, worse.

I would like to thank my girlfriend, Christin Grill, for always being by my side with discussions, advice, and action.

I thank my brothers Finn and Frederic for the free time activities when the pressure of study and work became overwhelming.

And last but not least, I would like to thank my doctoral supervisor Stefan Fischer for the many years of supervision and the introduction to the academic world with all its subtleties, as well as the financial support that I benefited from at his institute.

The rest of my thanks go to all members of the Nano Group and the Institute for Telematics, who have walked part of my life with me over the last few years and have always supported me when I asked for it.

1Introduction

This chapter introduces the new reader to the topic of nanonetworks and nanotechnologies in general. As this is a scientific book that goes into some level of depth, some scientific knowledge is presupposed. We first have a look at the designation “nano” and explain where the entire idea came from. Then, we have a look at science fiction, which often serves as a basis for future developments. Afterward, we try to establish an intuitive understanding of nanotechnologies and present several example applications that can either be conceptualized or even realized in wet-lab experiments. Then we discuss those parameters and environmental constraints that distinguish nanotechnologies from the early days of computer science, where resources have been very constrained as well. This includes various challenges and “new” laws of nature that system engineers have to deal with. Lastly, we summarize the chapter and give a chapter-wise overview of the entire book.

1.1 Etymology

Before we begin with anything else, it is beneficial to understand the meaning of the word “nano” and its etymology. The word nano was derived from the Greek word “nanos,” meaning “dwarf.” Although the Greek concept of atoms might be much closer in meaning to what we call today as “nano,” the word was already overloaded with meaning in physics.

Technically, the prefix “nano-” is used to designate anything that is a billion times smaller than a meter (1–4000 m). In numerical terms, everything between 1 and 999 nm lies in the nanorange. While that definition suits the physical perspective well, the nanonetworks community decided that anything between 1 and 4000 nm in size could be considered “nanoscale.” The smallest human blood vessels, also called capillaries, are about 4000 nm in diameter. Thus, any “nanobot” that is designed to operate in the human body must be smaller than that.

A more relatable example can be seen in Figure 1.1. On the left, a soccer ball is compared to the size of the planet Earth. The same ratio applies to the same soccer ball compared to a carbon nanotube.

To put things into perspective, a single hair is between 20 000 and 200 000 nm in diameter. A typical human cell has a diameter of about 10 000 nm, while DNA only has a diameter of about 2 nm. Hence, the nanoscale is extremely small.

Figure 1.1 A comparison between a soccer ball and the planet Earth made by Marc Stelzner at a university seminar.

Source: ITM (Stelzner).

1.2 Science Fiction

While the entire idea of “nano” already surfaced more than 2500 years ago in ancient Greece and India, it only picked up speed in the last 200 years. While the majority of the historical development is discussed in Chapter 2, the popular science fiction part is discussed here. Science fiction is technically not part of history, yet many ideas in modern physics have been inspired by even older science fiction literature. One could even go as far as claiming that such early literature supplies modern science with visions and ideas to strive for. While this has been true for many decades, there seems to be a current shortage of new ideas that might motivate future research.

The science fiction history that explicitly names nanotechnologies began in the 1950s with the famous author Arthur C. Clarke. In 1956, he wrote the story “The Next Tenants,” where he writes about machines that are only micrometers in size (Clarke, 1957). It is a story about crazy scientists where biological and technological elements have been combined. The protagonist of the story discovers this mad scientist on a Pacific island, where he trains termites in the use of technology as he is convinced that humanity is doomed to self-destruct. This overarching theme describes the Cold War mentality quite well and combines it with the first taste of nanotechnologies as we envision them today.

While many of the elements of the story seem foreign to us, nanoscale technology is by no means limited to science fiction literature.

Another famous author who picked up the idea of nanotechnology is Stanislaw Lem, who is well-known for his “Star Diaries.” In 1964, he wrote the book The Invincible, about a spacecraft from which the name of the book derived (Lem, 1964). The crew of the ship is sent out to investigate the fate of its sister ship the “Condor”. During the investigations, the crew of the spacecraft discovers traces of quasi-life based on an evolutionary selection process of self-replicating machines. This might be one of the, if not the first, dystopian stories in which nanotechnology has “gone wrong.”

In 1984, Stanislaw Lem wrote another book titled Peace on Earth (Lem, 1984). This book combines visionary aspects of artificial intelligence and nanorobotics. Lem envisions a peaceful society where most of the arm production is outsourced to factories on the moon. In the process, the nations on Earth agree to demilitarize the Earth completely. While a lot of autonomy is programmed into the stationary technology on the moon, many mysterious events begin to happen. A similar development as in The Invincible starts, and the result is a strange “metallic moon dust” that turns out to be a swarm of nanobots. During an expedition to the moon, some of that dust is brought to the earth, where the nanobots cause a near complete devastation of technology infrastructure.

Possibly motivated by this story, the famous TV show Futurama adopted a similar plot. Here, tech genius Professor Farnsworth is dissatisfied with the political conditions on Earth and decides to relocate to another location. To make the comet he chose more habitable, he deploys a swarm of nanorobots to purify the water. Due to unknown circumstances, the nanorobots get caught in a process of rapidly accelerated robotic evolution, which ultimately leads to the emergence of intelligent robots.

In the 1990s, even Mickey Mouse comic books contained ideas about nanorobots and the potentially devastating consequences they might bring. All of this might create a rather bleak outlook on the potential of nanotechnology to improve living conditions for humans. We will analyze the immense medical potential in Chapter 3 and have a look at the potential dangers in Chapter 13.

1.3 Nanotechnology Intuition

While technology might not have reached the stage described in science fiction, there is still an ever-decreasing trend in size. There are manifold driving forces behind this development. For example, the transistor density on memory chips is roughly doubling every 12–18 months (Moore, 1965). Gordon Moore observed this while working at Intel in 1965. A similar rule also existed for the computational power of CPUs but that law has come to a halt. Since 2010, the number of basic operations performed by a single commercial processor has remained more or less stagnant.

That said, improvements are happening, just not in the simple number of operations. For example, instead of limiting ourselves to a single processor, many devices have been introduced with multiple cores. Thus, a new dimension of parallel/concurrent computing is technically keeping the trend of accelerated computation alive. Yet, the “new” natural laws that apply at the nanoscale create a host of other challenges for system designers.

To give a better intuition of the different size scales, a comparison between different objects is shown in Figure 1.2. While transistors of the current generation might not be at the very left end of the scale, they are somewhere between the size of DNA and viruses.

Five years before Moore discovered this trend, famous scientist Feynman gave a revolutionary talk on the topic “There is plenty of room at the bottom.” In this trend, he predicted the massive trend of miniaturization we have experienced over the last 60 years. While this might not seem like much to some readers, it is surprisingly difficult to predict future developments like that. Feynman more or less started the current “race to the bottom” when it comes to the limits of miniaturization.

While Feynman mainly limited himself to the areas that were available at the time, nanotechnologies turned out to be an interdisciplinary field. Over the last 50–100 years, an enormous body of knowledge has been gathered by humanity as a whole. For example, a big industrial interest stems from the automotive industry, medicine, and chip manufacturers. The automotive industry is mainly interested in new technologies like terahertz antennas to prevent collisions by detecting close objects in advance. Chip designers want to increase the efficiency of their products to be ahead of the competition. Medicine offers a variety of possible applications for nanotechnology, like cancer treatments on the basis of metallic nanoparticles. We analyze many of these medical applications in Chapter 3. The area draws a lot of interest as health might be the greatest concern for humans. Even the Buddha said 2600 years ago that “health is the greatest worldly good” (Gotama, 600–520 BCE approximately 500 BCE, n.d.).

Figure 1.2 A comparison between different structures and their size. The interval between 1 and 100 nm ( to ) contains many interesting, naturally occurring protein-based structures.

Source: Florian-Lennert A. Lau.

The general interest in nanotechnology exceeds the industrial aspects by far. Vast areas of basic science in STEM concern themselves with aspects of nanotechnologies. Examples are physics, chemistry, biology, computer science, and many other areas. Among other things, phenomena, tools, or other technologies that either measure or manipulate matter at the nanoscale are researched.

A small overview of the relevant areas to the explicit area of nanonetworks that is discussed in this book is shown in Figure 1.3. As nanonetworks are a subdiscipline of computer science, the hierarchical mind map starts in the middle with communication and computation at the nanoscale. The map can also serve as an overview of the table of contents of the entire book.

It covers the major areas of computation, communication, and construction at the nanoscale in great detail as computer science and computer network science have most expertise to offer in those areas. It can be surprisingly difficult to construct simple structures at the nanoscale as we have few available tools and many external influences hinder precise construction processes. Computation at the nanoscale faces similar challenges. While transistors and circuits themselves are only a few nanometers in size, the voltage supply to power them is not. Overall, the constraints in memory, energy, and computational power are comparable to the early days of computer science.

Communication faces similar challenges. Energy is a scarce resource at the nanoscale, especially when trying to use the same principles as in modern computers. Wireless sensor networks use about 95% of their energy for communication, and the trend will likely only be worse in nanonetworks. As a result, it might be necessary to switch to alternative means of communication based on molecules. These have been tested by nature and are proven to work – maybe it is possible to adapt them for nanonetworking purposes.

In addition, the book progresses along the topics of locomotion and localization. Both problems can be surprisingly difficult to solve using artificial, nanoscale devices. Locomotion requires energy and is, at least in the human body, always subject to several other influences. Every small enough structure is subject to Brownian motion, diffusion, and blood flow. Finding the exact location of a structure or device on this basis can be surprisingly challenging. It is even more difficult as the memory of devices might be so limited that they cannot even store a unique identifier.

Other very important areas are actuators and sensors. Both are necessary to either capture the state of the environment or manipulate it. Actuators and sensors form the basis for communication and locomotion. While nanoscale sensors and actuators exist at least as concepts, it is surprisingly difficult to combine them with other devices. For example, the process of creating carbon nanotubes involves many stochastic processes, and the final position of a finished carbon nanotube is more or less random. Precise placement of components in 3D is a huge unsolved problem.

For networking purposes and many algorithms, it is necessary to have at least a primitive understanding of time and randomness. Without time, it is impossible to gather data in a meaningful way for medical applications. A diagnosis is only relevant for a time, and many algorithms rely on random numbers. This is especially true for heuristic methods that might save energy while not producing optimal results.

Figure 1.3 A mind map of nanonetworks. The different branches represent different areas of research and each node is related to its neighbors.

Source: ITM (Florian-Lennert A. Lau).

The last analyzed area in the book is ethical, legal, and social issues (ELSI). ELSI is concerned itself with the ethical, legal, and social aspects of other disciplines. While it might be possible to create certain systems, it might not be possible to get a permit to use them or they might be ill-received by the society as a whole. For example, DNA research and gene manipulation have a pretty bad reputation that can be avoided given better management beforehand.

Arguably the biggest challenge is the combination of the individual solutions into a fully functional nanonetwork. As single devices are too constrained to solve difficult problems, nanodevices are expected to collaborate (Akyildiz et al., 2008). Yet, it is challenging to even assemble the different components of nanorobots into one fully functional device. The challenge of creating an entire network of such devices is even greater. In Chapter 12, we analyze the possible architecture in detail.

Yet, one especially promising approach for the described problem of assembly and the component combination is self-assembly. The general idea of the work is to grow nanoscale devices and networks of devices like crystals. Crystals are to be understood as a lattice structure made up of simple building blocks. At the nanoscale, there are few other ways to manipulate matter on a large scale.

Crystal formation is based on the principle of self-assembly. Tiny and often simply structured components assemble themselves into larger and often complex structures according to local rules. These complex structures could be nanodevices, nanosensors, packets of messages, entire nanonetworks, or even computing machines.

Although most known crystals follow the paradigm of self-assembly, the process can hardly be influenced. An exception is certain building blocks made of DNA. These also follow the self-assembly paradigm and can be treated like crystals. However, these building blocks have the property that some DNA strands can only meaningfully form a bond with their inverse.

It is possible to create arbitrary strands of DNA in the laboratory. Consequently, one can create building blocks of DNA that behave like pieces of a puzzle or even arbitrary shapes from a single DNA strand. As will be shown later, basically all the problems can be solved using just DNA as a building block. Unlike artificial approaches, DNA already works at the nanoscale and can assemble with the necessary degree of precision/accuracy. Thus, the biggest problems of precise placement and the component combination might come “for free” when focusing on DNA as an atomic building block.

In addition to DNA as a possible building block, there are other ideas about how nanodevices can be created. For example, there are many approaches that are based on naturally occurring cells or bacteria. They suggest using existing resources in nature and, if necessary, adapting them to one’s own needs. For example, biological nanodevices.

These are explained in detail in Chapter 4.

Artificial approaches are also possible. An example of a nanorobot based on classic electronic components is shown in Figure 1.4. The approach is based on wireless sensor networks as used in research and parts of the economy. Novel materials, such as carbon nanotubes, are used, which are a promising building block for sensors, actuators, and storage technologies at the nanoscale (Baughman et al., 2002).

1.4 Example Applications

While the state of the art in nanotechnologies has not yet reached what science fiction literature envisioned, there are still hosts of innovations available. Today, nanoparticles are used regularly (Yao et al., 2017; Wang et al., 2015; Li et al., 2018), for example, in cancer research (Singhal et al., 2010). Some nanoparticles are structured in such a way that they only bind to cancerous tissue and are heated there by external influences. This can be done, for example, by laser. The affected tissue is destroyed and the surrounding, healthy cells can recover. Visionary approaches envision the use of nanodevices in the human body to better label or directly destroy cancer cells.

Figure 1.4 Schematic representation of an electric nanorobot. The individual components of a nanorobot are implemented using electronic components (Büther et al., 2017).

Source: Buther et al. (2017)/with permission of Springer Nature.

This approach can easily be generalized. In general, nanodevices can be a useful, supportive measure in the detection and treatment of diseases. This includes, among other things, the treatment of diabetes (Gu et al., 2013), the detection and local treatment of inflammatory diseases (Staples et al., 2006; Amato et al., 2010; Stelzner et al., [2016b]), and many other diagnostic and therapeutic options (Benenson et al., 2004). It is often suggested that nanodevices may have to collaborate because of their size limitations (Akyildiz et al., 2008).

Complex scenarios describe the use of molecular automata, which are already able to recognize several markers for disease in the laboratory and start producing specific RNA sequences as soon as all markers are present (Anderson et al., 2006). The sequences produced do not serve any purpose at the moment, but show in principle that one can react to environmental parameters – also constructively.

In Ghavami et al. (2012), the detection of diseases is further abstracted. Among other things, applications such as intracellular operations are proposed. Such technologies require a high degree of precision (Freitas, 2005).

Another popular use case for nanodevices is to support the human immune system. It often happens that people’s self-healing powers are no longer sufficient for certain clinical pictures. If a patient has reached old age, regeneration processes often take significantly longer than that in young people. A general improvement in the immune system is also conceivable, regardless of the initial situation (Freitas, 2005; Akyildiz et al., 2008; Gu et al., 2013).

1.5 Unique Problems and Challenges