Fintech Fundamentals E-Book

Len Mei

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 382

Veröffentlichungsjahr: 2024

Beschreibung

This book explores the digital technologies essential for building the new digital economy. It delves into concepts such as cloud and edge computing, 5G telecommunication, blockchain, big data, and AI, explaining how these technologies enable the digital economy. It also examines the impact of financial technology on both traditional and emerging industries, providing a comprehensive introduction for those interested in further research on these subjects.
The course begins with an introduction to the digital economy, followed by detailed discussions on various foundational technologies. Topics such as cloud and edge computing, 5G, blockchain, and AI are covered, highlighting their roles in the digital economy. The book also addresses the transformation of financial services and the impact of fintech on various industries, offering a broad understanding of the current and future landscape.
Designed for professionals and researchers, this book equips readers with the knowledge to understand and engage with the digital economy. It provides a solid foundation for further exploration, making it an essential resource for those looking to navigate and contribute to the evolving digital world.

Leseprobe

FINTECH FUNDAMENTALS

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FINTECH FUNDAMENTALS

Big Data • Cloud Computing • Digital Economy

Len Mei, Ph.D.

MERCURY LEARNING AND INFORMATIONDulles, VirginiaBoston, MassachusettsNew Delhi

Copyright ©2022 by MERCURY LEARNING AND INFORMATION LLC. All rights reserved.

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L. Mei. Fintech Fundamentals.ISBN: 978-1-68392-838-6

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CONTENTS

Preface

Chapter 1: Introduction to the Digital Economy

1.1 Digital Economy

1.2 Infrastructure for the Digital Economy

1.3 Data Center Evolution

References

Chapter 2: Cloud and Edge Computing

2.1 Cloud Computing

2.2 Edge Computing

2.3 High-Performance Computers

2.4 Quantum Computers and Quantum Communication

References

Chapter 3: 5G Telecommunication

3.1 5G Communication Technologies

3.2 Software-Defined Network (SDN)

3.3 Private 5G

3.4 Beyond 5G

References

Chapter 4: Blockchain and Other Digital Economy Infrastructures

4.1 IoT Devices and Sensors

4.2 Semiconductors

4.3 Evolution of Computers Driven by AI

4.4 Blockchain

References

Chapter 5: Big Data and Artificial Intelligence

5.1 What is Artificial Intelligence?

5.2 How is Artificial Intelligence Created?

5.3 Machine Learning

5.4 Big Data

5.5 AI Applications

5.6 The AI Market

5.7 Government’s Role in AI

5.8 European Approach to AI

5.9 Fusion of AI with Biotechnology

5.10 AI and Blockchain

References

Chapter 6: What’s New in the Digital Economy?

6.1 Use of Blockchain to Secure IoT Data

6.2 Blockchain and Credit Card

6.3 Cross-border Retail Business

6.4 Counterparty Platform Dapps

6.5 AML & KYC

6.6 O2O Business

6.7 Third-Party Payment

6.8 Mobile Wallet and Payment Transfer

6.9 European Security Settlement Platform

6.10 Credit Rating System

References

Chapter 7: Financial Services Industries

7.1 Fintech

7.2 Blockchain and Fintech

7.3 Technology-Driven Fintech

7.4 Global Fintech Landscape

7.5 Major Global Fintech Companies

7.6 TechFin

7.7 Blockchain Technology for Banks

7.8 Crowdsale and Crowd Prediction

7.9 Building MDL for Financial Services

7.10 Digital Currency

References

Chapter 8: Trading and Lending

8.1 Security Trading

8.2 Commodity Trading

8.3 Energy Trading

8.4 Alternative Trading System

8.5 Peer-to-Peer Lending

8.6 Online Lending

8.7 Microlending and SME Lending

References

Chapter 9: Renewed Industries

9.1 Wealth Management

9.2 Insurance

9.3 Supply Chain Management

9.4 Healthcare

9.5 Food Industry

9.6 Defense Industry

9.7 Cybersecurity

9.8 Autonomous Vehicles

References

Chapter 10: Industrie 4.0

10.1 The Fourth Industrial Revolution

10.2 Equipment Automation

10.3 Factory Automation

10.4 Data Automation

10.5 From Product Design to Market

References

Chapter 11: Smart City

11.1 What is a Smart City?

11.2 Smart City Projects in the World

11.3 Smart City Project – Transportation

11.4 Smart City Project – Utility Management

11.5 Smart City Project – Crime Prevention

11.6 Smart City Project – Healthcare and Disease Prevention

References

Chapter 12: Governance, Legal Applications, and Regulation

12.1 Governance and Voting

12.2 Regulatory Applications and Issues

12.3 Overcoming Privacy Issues in Data Collection

12.4 Land Title Registration and Real Estate

12.5 Law and Justice

12.6 Protection of Intellectual Property

References

Chapter 13: Conclusion

13.1 Near-Future Positive Impact

13.2 Future Job Market

13.3 Wealth Redistribution

13.4 Technology-Empowered Extremism

13.5 Longer-term Impact

References

Index

PREFACE

The world is propelling into the era of a digital economy. The signs are everywhere: the digital economy is growing at much faster pace than the overall economy. In the U.S. alone, the digital economy is growing at a rate of 10% a year, while the overall economy is at just two percent annually.

The digital economy is based on data. Data is the crude oil of the digital economy. The volume of data generated is growing exponentially. The digital economy is built upon the foundation of technologies developed in the last six decades – the technology of ICT (Information and Communication Technology). The advance of ICT allows the industry to move into 5G communication, cloud and edge computing, Big Data and artificial intelligence, blockchain technology, Industry 4.0, financial technology (Fintech), digital currency, and many others. These new technologies are altering the landscape of the financial, business, and trade systems today.

Fintech promises to revolutionize the financial industry just as Industry 4.0 revolutionized manufacturing. These new technologies can add trillions of dollars to the global economy. The implications are staggering. On November 9, 2015, the U.S. Department of Commerce unveiled a Digital Economy Agenda to help businesses and consumers realize the potential of the digital economy.1 Since that time, similar measures have been adopted in many other countries.2

These are some aspects of the digital economy. In this book, we will exam the underlying digital technologies required to build the digital economy. We will also discuss basic concepts and elements of the technologies that make a digital economy possible and how they work. We will look at some of the major applications. We will look into the two most important aspects of the economy: the financial industry (Fintech) and the manufacturing industry (Industry 4.0). This subject is vast and is quickly changing. It is impossible to cover the subject entirely in one book. This book serves as a comprehensive introduction and background to anyone who is interested in the subject in order to do further research on the individual subjects included here. Many references are cited in the interest of research and further exploration. And to conclude the book, we will discuss how the economy and society as a whole may be transformed in the next 20 or 30 years.

Len Mei, Ph.D.March 2022

1https://www.commerce.gov/index.php/tags/digital-economy

2https://hbr.org/2017/07/60-countries-digital-competitiveness-indexed

CHAPTER 1

INTRODUCTION TO THE DIGITAL ECONOMY

In this chapter, we will define digital economy and its impact on the world.

1.1 DIGITAL ECONOMY

Since 2010, the world has been propelling into the era of a digital economy. It is evident that the digital sector of the economy in the United States, the world leader in the digital economy, has grown at 10% a year, whereas the rest of the economy grows at two percent a year.1 In addition, job creation in the digital economy sector is growing at a much faster rate than 10%.2 The fastest-growing companies are engaged with the digital economy and according to a report published by the Boston Consulting Group, by 2035 there will be 400 million job openings worldwide in these related industries.3 This number is larger than the current U.S. population. It is estimated that by 2030 the digital economy will increase to $15.7 trillion of the global GDP.

The definition of the digital economy is vague. In the narrow sense, the digital economy includes information and telecommunication services, hardware manufacturing, and the software industry. However, in a broader sense, the digital economy also includes e-commerce, sharing economy, Industry 4.0, and digital services powered by artificial intelligence (AI). The above-mentioned 10% of the U.S. digital economy includes hardware, software, e-commerce, digital media, telecommunication, and supporting services.

The United States is not unique in this aspect; countries around the world are growing the digital economy faster than the general economy. According to one study, the digital economy in Brazil also grows at an annual rate of more than 10% a year.4

Brandz,5 a marketing firm, ranked the ten most recognized brand names in the world: Google, Apple, Amazon, Microsoft, Tencent, Facebook, VISA, McDonald’s, Alibaba, and AT&T. Except for McDonald’s, which is in the food industry, all the other companies are involved in the digital economy. Many other successful companies of the last two decades are also included, such as Salesforce, Adobe, Splunk, Twilio, etc.

It took only a span of five years for Uber to achieve the same market capitalization which General Motors achieved in 107 years. Apple became the first trillion-dollar company in history, followed by Amazon and Google. There are many other examples: on a single day (November 11, 2019) in China, Alibaba made $38 billion, and the value of Zoom Communication increased from $19 billion to $141 billion in 2020; the digital economy has enabled such incredible growth.

The transition to the digital economy is often considered as the 4th Industrial Revolution, following the last three industrial revolutions. The First Industrial Revolution occurred in 1760 when the United Kingdom invented the steam engine, and other steam-powered machines, such as steamboats, locomotives, and textile machines. This made the United Kingdom the first industrialized country in the world, and subsequently, turned it into a global power.

The Second Industrial Revolution occurred in 1879, 119 years after the First Industrial Revolution. It happened when the electricity generator was invented, together with many devices using electricity, such as electric lamps and electricity-powered machines. Electricity is a form of energy that can be transported much easier than steam. Therefore, its use was spreading more quickly than a vapor form of energy. It spurred the Second Industrial Revolution. Soon, the United States, the country which first set up large-scale power plants, was industrialized and powered by electricity; subsequently, it became a world power.

The electric lamp was a great invention because it extends the useful hours of a day. Now, suddenly, people could work day and night, without any difference, so productivity increased. Many other electricity-powered machines also proliferated quickly, because of this new availability of power.

Sixty-eight years later, at the Bell Laboratories, also in the United States, the transistor was invented. Before the invention of the transistor, computers were made of vacuum tubes. Transistors are much smaller, work much faster, and consume far less energy than vacuum tubes. Since then, computers made by transistors took a quantum leap in performance and computing power. Transistor technology soon got a big push when integrated circuits were invented by scientists at Texas Instruments in 1957. Techniques of making integrated circuits allow one to build more than one transistor on a single chip of silicon. By making transistors smaller, one can fit more and more transistors onto a single chip.

In 1971, a start-up company, called Intel, launched an integrated circuit product, 4004, which integrated 2,300 transistors in a single chip of silicon. It was a milestone achievement. It was merely 14 years after the invention of the integrated circuit. This chip was more powerful in computing than the large-scale computers made out of vacuum tubes a decade earlier.

The founder of Intel, Gordon Moore, predicted that by making transistors smaller and smaller, one could double the number of transistors on a single silicon chip every two years. Intel 4004 was made with 10 µm technology, that is, the size of the transistor on the 4004 chip is about 10 µm large (1 µm is one-millionth of a meter, i.e., it is the size of small bacteria). Gordon Moore’s prediction is famously known as Moore’s Law. Today, almost 50 years later, this prediction still holds true.

Since then, a new industry was born, the industry of integrated circuits, also known as the semiconductor industry, because the chips are made from semiconductors. Consequently, the industry has been dedicated to making transistors smaller and smaller by improving the techniques of printing circuitry, etching, diffusion, oxidation, deposition, and many other techniques of making transistors. Such progress in the semiconductor industry has made all of the electronics that we are using today.

Eighteen years later, in 1989, Intel again broke the record. It created another milestone product, the Intel 80486, containing 1.18 million transistors using 1 microtechnology. Forty years later, in 2010, Intel’s Xeon 7400 contained 1.9 billion transistors, using 45 nm technologies (1 nm is 1 billionth of a meter). Today, in 2020, the most advanced chip, such as NVIDIA’s GA100, contains 54 billion transistors on a single silicon chip using 7 nm technologies. Soon, we will see chips containing 100 billion transistors or more.

One needs to have some imagination to understand what a chip of 50 billion transistors can do. Today, the cell phone has chips of several billion transistors, and our phones are more than just phones. They are computers more powerful than the large-scale computers of decades ago. However, we need more and more transistors to meet demands of consumers.

Transistors process information in the digital form, 0 and 1. All of the technologies handling data are in digital form. Therefore, we call the economy powered by these technologies the digital economy.

Let’s compare today with 100 years ago. In 1910, mankind started to construct large-scale power plants to generate electricity, and deliver the electricity through transmission lines to factories and homes. Today, we have large-scale data centers, receiving from and sending data to offices, factories, homes, and individuals. Therefore, we say that the digital economy is powered by data, and data are the most valuable commodity in the digital economy. The huge volume of data as this commodity has acquired a name “Big Data.” Big Data is the petroleum of the digital economy.

FIGURE 1.1  Comparison of economies in 1910 and 2010.

The transformation of the digital economy is dramatic. Let us look at one example in our daily life. Not long ago, if you wanted to make an announcement public, you could publish an ad in a newspaper, which has a circulation of 10,000 or more. Unless people search for your ad, it is unlikely it will attract much attention, unless your ad is exceptionally large. Today, to place an ad, you would publish it online. Whoever goes to the page for other information will see your ad. The Internet may circulate millions of copies across the globe. It can be targeted and localized.

Companies like Google receive most of their revenues from advertisements. When you search “vacation to Hawaii,” Google knows that you are interested in taking a vacation to Hawaii. It sells your IP address to travel agencies, hotels, airlines, who immediately send an ad to you to promote their businesses. That is why Google’s revenue is growing 20% a year since its founding nearly 25 years ago. Today, Google is one of the largest companies in the digital economy; the same is true for Meta (including Facebook) and Amazon.

The digital economy is more than just the Internet. Digital currency is also a disruptive revolution. One of the most well-known digital currencies is Bitcoin. Bitcoin, the first cryptocurrency, is probably the most explosive asset in human history. In 12 years, from 2009 to 2021, its market capitalization has grown from zero to almost $1 trillion. Since Bitcoin appeared, there are many other cryptocurrencies springing to life. At the current count, there are over 6,000 different cryptocurrencies. The top 5 cryptocurrencies have a combined market capitalization of over $1.5 trillion.

Digital currency is possible because of its underlying technology – blockchain. Blockchain can transform the Internet of information as we know it today, into the Internet of value. It is “money over IP” as coined by Cathie Woods, the founder of Ark Invest. Money can be transferred online, just like data, without the worry that it can be stolen or duplicated. Suddenly, the Internet finds itself capable of performing many financial functions with the security and speed unmatched by the traditional financial systems.

The birth of blockchain technology is due to the maturity of Internet technology itself, the advances in computer science, the spread of cheap computing power, the high speed, high bandwidth communication, and many other factors such as e-commerce and trade globalization.

Blockchain-based smart contracts and distributed apps open up a wide frontier for transaction applications, in addition to the immutable digital identity, which provides a chain of custody and proof of asset ownership.

Another important innovation in the digital economy is AI. Artificial intelligence, machine learning, and Big Data are different aspects of the same thing. Artificial intelligence promises to augment human intelligence and to help humans to accelerate innovations. Applications of AI in healthcare, governance, legal systems, smart cities, factory automation, etc., can greatly improve efficiency.

Recent growth in AI is promoted by the computing power, increase in data volume, as well as investments. Large companies in the digital economy, such as Amazon, Google, IBM, Alibaba, are all offering AI algorithms in their cloud computing services so their customers can build AI-based applications.

As machines become more intelligent, they can perform more human-like tasks, such as strategic games, self-driving cars, medical diagnoses, facial recognition, and many more.

Today’s AI capability already includes reasoning, planning, learning, natural language processing, and the ability to move and manipulate objects, and its capability is increasing each day.

Artificial intelligence machines consist of three elements: hardware, software, and Big Data. Hardware is the computer system that mimics a human neuron network. Software is the machine learning algorithm, also known as deep learning, including search, mathematical optimization, methods based on statistics and probability, etc. Big Data is fed into the AI machine to train it. For example, the AI machine that can perform a medical diagnosis learns its technique by studying tens of thousands of X-ray images and their associated diagnoses. In facial recognition, AI learns to recognize people by examining millions of faces. Once they are recorded, AI can perform the related tasks better and faster than humans. After all, an experienced doctor can only learn from tens of thousands of his patient’s diagnoses, and an AI machine can learn from millions of diagnoses from a worldwide database. A machine can also detect smaller patterns which are imperceptible by human eyes. In autonomous cars, the machine can react in fractions of a millionth second instead of in seconds by a human. The technology of CV-2X6 (cell, vehicle to everything) constantly connects cars to other devices. AI is also used in the financial industry to detect and flag activity in banking and finance such as unusual debit card usage and large account deposits.

Likewise, the application of digital technologies in the manufacturing industry has revolutionized the industry. Germany calls it “industrie 4.0” and we refer to it as Industry 4.0. In a factory of Industry 4.0, manufacturing is entirely automated by robots. These robots have extraordinary sensors: they can see and hear. In addition to sensors, they can also perform certain cognitive services, that is, the services which require pre-acquired knowledge. They can work 24 hours a day and in environments which are dangerous to humans.

At the factory level, the data collected from the entire factory and supply chain will allow the manufacturing execution system to make decisions about production and flow of materials to make the most efficient use of its resources, such as equipment. It can replace much of the managerial work. When the factory is linked to its upstream and downstream supply chain, or even markets, certain tasks can be guided by the requirements of the supply chain. For example, the use of data will allow participants of the delivery, such as shipping and courier companies, to globally track their freight in real time. End customers can even specify how they want their products made. The factory is said to be “personalized.” In a personalized manufacturing environment, factories can cater quickly and creatively to meet customer demands. They are flexible and specialized to produce personalized products without hiring and training additional skilled laborers.

When there is no labor required in the factory, there is no need to turn on the lights. Therefore, these types of factories are also known as “lights-out” factories. They can turn out products 24 hours a day without workers.

These are some aspects of the digital economy. In this book, we will examine the underlying digital technologies required to build the digital economy. We will look at some of the major applications. And in the end, we will speculate on how the economy and society will be transformed in 20 or 30 years.

1.2 INFRASTRUCTURE FOR THE DIGITAL ECONOMY

As the name implies, the digital economy is the economy driven by digital data. The digital economy involves three aspects of data: the generation-collection, transmission, and processing of data.

The modern economy requires infrastructures like power plants, highways, railways, and public utilities; the digital economy requires different kinds of infrastructures, such as 5G, data centers, etc. These infrastructures all involve high technologies, including semiconductors. The countries which have the capability to quickly build these infrastructures will be able to rapidly develop a digital economy. Since the digital economy ensures the growth of the future economy, building the digital economy infrastructure becomes the number one priority for many countries.

All aspects of data handling require an infrastructure. These infrastructures are the foundation of the digital economy. To grow the digital economy, a country has to grow the digital infrastructure in proportion, both in capacity and capability. The digital infrastructure is to the digital economy as power plants, highways, railroads, airports, and utilities are to the traditional economy.

In the past, the data generation and collection process involved human effort. But increasingly, such a process is automated to a point that human involvement and supervision are not necessary. Data transmission speed is also increasing dramatically with the advancement of 5G telecommunication technology. 5G allows data to be transferred to a large-scale data center, or a cloud computing center, to be processed.

Cloud computing centers have data analysis capabilities, such as AI, blockchain, and many others. As computer power advances, local computer centers also acquire some of the computing capabilities of the large-scale cloud computing centers. These local computing centers are closer to the source of the data. Such a localized computing center is known as edge computing, in differentiation from centralized cloud computing.

A large-scale digital infrastructure involves many technologies. We can roughly divide these technologies into three layers:

• application,

• infrastructure, and

• fundamental layers.

The application layer is the interface with human beings or users. It allows us to perform the functions we want to achieve in manipulating data. It is very much like the apps we use in our cell phones. The infrastructure layer is the physical layer or the hardware that allows us to collect, transmit data, and produce the results we want. The fundamental technology layer constructs the infrastructure hardware. For example, the high-performance computer is the building block of data centers. Sensors and the Internet of Things (IoT) are the components used to collect data.

The digital infrastructure is huge. It is composed of millions of components. Any system capable of manipulating digital data has a computer at its core. Each component is composed of both hardware and software. The software includes operating systems and applications. The whole digital infrastructure consists of billions of interconnected computers, cables, transmission towers, IoTs, sensors, antennas, etc., performing different functions coherently based on predefined protocols. These components of the gigantic digital infrastructure belong to millions of independent owners – companies, government organizations, and individuals. And yet, amazingly, they all work seamlessly to perform desired functions.

All the hardware components are made of semiconductor chips. Today, the most advanced chip is manufactured using 5 nm technology. A chip, the size of a nail, contains 60 billion transistors. It is indeed very powerful.

The applications are the technologies that interface with us. For example, in the cell phone, we have many apps, which are built to interface with us. Using these apps, we can send messages, read news, watch videos, listen to music, consult bank accounts, do shopping, and much more. Likewise, the application level of the digital economy allows us to perform certain tasks. A few examples are fintech, artificial intelligence, Industry 4.0, and smart city.

Beneath the applications, there is the layer of infrastructure. For example, the apps in a cell phone are the “application” and the cell phone itself is the “infrastructure.” Infrastructure technologies are the platforms that allow us to build applications. Some of the important ones are blockchain, smart factory, Big Data, IoT, etc. For example, you need to use blockchain to make digital currency. You need the Internet of Things (IoT) to make intelligent robots and devices. And you need a smart factory to build Industry 4.0, etc.

In turn, these infrastructure technologies are made from more fundamental technologies, such as HPC (High-Performance Computers), 5G, the Internet, cloud computing, and semiconductors. For example, a data center consists of millions of interconnected computers. Further down the chain, everything is made of semiconductor chips, which collect, transmit, and process digital data.

Most of the applications reside in the cloud. A cloud is a data center which offers application software and data storage. This application software can include built-in AI, or blockchain concepts. Today, many companies are offering cloud services: Amazon, Google, Meta, Microsoft, Alibaba, etc.

Companies use a cloud platform when their customer-related services are dedicated applications as opposed to a website. A cloud platform can build and run an application that can leverage the power of hyper-scale data centers: in order to reach users worldwide, to use sophisticated analytics and AI functions, to utilize massive data storage, or to take advantage of cost efficiencies. For example, if you operate a small e-commerce business, you can use a website. But when it increases in size and volume, you may want to have a dedicated application. By using a cloud service, you don’t have to buy/lease your own servers, develop your own software applications, and have access to the latest hardware and software.

For example, Vodafone uses Google Cloud services to develop a cloud-based data platform7 to find new opportunities and improve relationships with customers. It helps Vodafone to create and deploy new digital services in many countries, as well as to gain new insights from customer data to improve relationships and boost customer retention.

Likewise, IBM developed an AI-powered digital assistant chatbot app called TOBi.8 When consumers ask TOBi questions, it instantly accesses relevant data and answers the questions. It also pulls up customer records for further help.

Amazon, the world’s largest cloud service provider, offers its analytics and forecasting services, called Amazon Web Service (AWS). AWS expands in multiple directions. Amazon Web Services (AWS) had an estimated 2018 annual revenue of $25.65 billion. AWS excels in AI, augmented reality, and analytics.

Alibaba is the largest cloud-computing company in China and operates data centers in 23 regions and 63 zones globally, with revenue of $72 billion in 2020. Recently, it is moving into the health, global fashion, and the electric car markets.

Big Data is a term used to describe the technology used to handle large volumes of data. Big Data has three characteristics: mega volumes, speed, and it is unstructured. This makes Big Data different from the traditional database. Because of the cost reduction of collection, transmission, storage, and processing of data, an increased volume of Big Data is now cheaper and more accessible than traditional database technologies.

Big Data requires a huge capacity of data handling along with the data path – collection, transmission, storage, and processing. The capacity of data handling must grow in sync with the volume of data. This puts a huge demand on the digital economy infrastructure.

Until recently, most data was generated by humans. With the advent of the Internet of Things (IoT), more objects and devices are connected to the Internet, gathering data on customer usage patterns and product performance. All of these issues prompt the quantity of data to grow exponentially. More data generation puts the demand on data transmission. The transmission technology has moved from 3G to 4G, and now 5G in the last decade.

Finally, data need to be processed: validated, sorted, classified, aggregated, analyzed, and reported. In addition to constructing a Big Data center to increase capacity, the capability of data handling also needs to be improved. To alleviate the congestion, software libraries such as Hadoop and Spark have been developed to allow for the distributed processing of large data sets across clusters of computers, using simple programming models. They allow the clustering of multiple computers to analyze massive datasets in parallel. Each of these multiple computers offers local computation and storage. The library can detect and handle failures at the application layer, thus delivering a highly available and reliable service on top of a cluster of computers.

1.3 DATA CENTER EVOLUTION

In the digital economy, all economic activities are evolving around data. Therefore, data centers play a key role in the digital economy. This is similar to how the power plants played a key role in the Second Industrial Revolution.

Fundamental technologies are the foundation of building the infrastructure of the digital economy. All applications and infrastructure layers are constructed on the fundamental technologies. They include HPC (High-Performance Computers), 5G telecommunication, the Internet, cloud computing, and most important of all – semiconductor technology.

Cloud computing resides in the data centers. Data centers evolved from large computing centers in the early 1970s. When inexpensive networking equipment was available in the 1980s, it became possible to put many servers in a specific room inside the company to serve the entire company. When the Internet became prevalent, data centers started to appear to serve the public instead of private data centers belonging to a single company.

The original data center, in the 1980s, was used mostly for data storage purposes. They were called “Infrastructure as a Service,” in short, “IaaS.” In the 1990s, data center companies started to offer operating systems to their customers. With a dumb terminal, one can log into a data center computer and use it as a remote computer. This type of service is called “Platform as a Service,” or “PaaS.” Beginning in 2000, data centers offered not only operating systems but also application software on data centers. This type of data center is called “Softwareas a Service,” or “SaaS.” For example, Microsoft 365 is an office application offered by Microsoft data center.

Since 2010, data center companies have offered much more sophisticated application software, including blockchain, AI, etc. The data center has acquired many different names, such as “Blockchain as a Service,” “AI as a Service,” etc. Suddenly, customers without the know-how of blockchain or AI can ask the service providers to develop their own blockchain-based or AI-based applications. All they need to know is how they want their applications to be used.

With such a capability, the market for cloud computing grows explosively. The global cloud computing market grew to $266 billion in 2019 and is forecasted to grow by 15% a year until 2027. The United States currently has almost half of the global market.

The deployment of these services can save operating costs of companies by leveraging the established hardware and software. In addition, the added capabilities will help boost their business performance. Therefore, companies are proactively outsourcing cloud computing instead of building their own IT infrastructures. For example, Stelco Holdings, a Canadian steel company, uses the AI-enabled cloud platform Canvas Analytics9 to digest its operational data to obtain real-time analytics solutions. Similarly, in the healthcare industry, Nuance Communications Inc., acquired by Microsoft in 2021, offers a cloud-based AI platform to healthcare providers.

Such cloud-based systems are a boom for small and medium enterprises (SMEs). Suddenly, SMEs find that the highly sophisticated and expensive tools which were previously inaccessible to them, are at their fingertips for reasonable costs. This increases their competitive advantages to the same levels as larger competitors. According to some estimates, up to 60% of companies are currently using cloud services rather than their in-house computer systems.

With the huge amounts of data being transferred, data security becomes a concern. There are two ways to protect data: (1) to prevent hacking of existing data and (2) to create the data so it is free from hackers. That is where blockchain technology becomes valuable. The encrypted and distributed nature of the blockchain database provides high security to sensitive data. Blockchain methods provide a solution for such security issues.

REFERENCES

1.https://www.bea.gov/data/special-topics/digital-economy.

2.https://www.researchgate.net/figure/2010-2015-Percent-Change-in-Digital-Economy-Jobs-and-Establishments_fig4_323701244.

3.https://image-src.bcg.com/Images/BCG_Year-2035_400-Million-Job-Opportunities-Digital%20Age_ENG_Mar2017_tcm52-153963.pdf.

4.http://www.csstoday.com/Item/6662.aspx.

5.https://www.kantar.com/campaigns/brandz-downloads/brandz-top-100-most-valuable-global-brands-2020.

6.https://www.zdnet.com/article/what-is-c-v2x-and-how-it-changes-the-driving-smart-cities/

7.https://cloud.google.com/press-releases/2019/1120/vodafone-chooses-google-cloud-as-strategic-cloud-platform/.

8.https://newsroom.ibm.com/Boosting-Digital-Contact-in-a-Contactless-World-with-AI-and-Hybrid-Cloud.

9.https://www.plant.ca/general/stelco-supports-its-intelligent-operations-with-ai-deal-181902/.

CHAPTER 2

CLOUD AND EDGE COMPUTING

The modern digital system consists of hardware and software. The hardware consists of a data processing unit, a data storage unit, and an interface. The software consists of an operating system and application software. The operating system is the interface layer which performs the translation between machine language that the computer understands and the programming languages that humans understand. The application software performs a specific task that the user commands the computer to do.

Any large-scale computing system is composed of many smaller systems that communicate with each other. The basic computing system can be compared to a brick, and the large computing system is like a building.

The infrastructure of a digital economy is comprised of data collection, data transmission, and data processing. Data are collected from a human or from an object, using sensors and IoT.

When you send a message or compose an email, you generate data. Sometimes, you generate data unconsciously: for example, when you make a credit card transaction, take a ride on Uber, or purchase online, you are also generating data.

Currently, however, data are increasingly being gathered from objects. When we attach a sensor such as a camera to the object, it acquires vision. It can take pictures. An object is connected to the Internet through IoT. It can then send the data, such as a video or a picture, over the Internet to a destination. For example, a surveillance camera sends video online.

The second component of digital infrastructure is data transmission. The data are transmitted through the telecommunication network. In 2020, the telecommunication network is entering 5G or the 5th generation. Compared to 4G, 5G has a bandwidth that is 1000× as large. It can connect 100x more devices simultaneously. Its download speed is 100× faster than that of 4G, and the response time is 5× faster. The 5G telecommunication network will allow more data to be transmitted and therefore processed.

Creating AI requires data, and more data can provide AI with more intelligence. Therefore, 5G serves not only human-operated but also AI-controlled machines.

We have mentioned that data from objects are collected by sensors and transmitted to the internet by IoT. IoT installation is witnessing exponential growth globally. Currently, in 2021, there are 36 billion IoT systems installed. It is forecast that by 2025, the number will double.

Many companies are already using IoT to monitor their equipment. For example, Caterpillar, the world’s largest manufacturer of agriculture machinery, installs IoT on its tractors.1 No matter how far out the tractors are in the field, Caterpillar can monitor their performance in real time. It knows the exact location, the working condition, and when and how a tractor breaks down, eliminating the need for fault diagnosis.2 In this way, Caterpillar can provide assistance much faster.

Samsung’s smart refrigerator has sensors and IoT.3 It can tell you exactly what food you have in the refrigerator, without you having to open it. You can check it using Samsung’s app, in your phone. When you are at the supermarket wondering whether or not you have eggs at home, just open the app.

Doctors can monitor a patient’s vital signs, such as blood pressure or erratic heart beats, remotely and in real time, which helps to prevent serious mishaps.

In the supply chain, IoT can track valuable assets along their supply route. Any deviation can be detected and corrected more quickly. Warehouse inventory can be tracked using IoT.

In the utility industry, with the help of IoT technology in the electrical grid, engineers can collect necessary data to monitor the performance and power consumption. This information can help in channelizing the flow of electricity to the homes. So, the pressure to create more electric power during peak consumption hours can be distributed. With IoT-driven electric smart meters, the utility company can guide households about their pattern of power consumption and ways in which they can reduce their dependence on electricity.

IoT used in the industry is called IIoT. It can gather data from machines and materials around the factory to track any deviation in performance parameters, and promptly eliminate the glitches so that the production process is not hampered.

Sensors are important components in the digital infrastructure. A sensor is an interface between the digital world and the real world. There are many types of sensors: the camera is a vision sensor; the microphone is a sound sensor. There are sensors for pressure, temperature, chemical concentration, speed, magnetic field, electrical field, gyroscope, moisture, biological parameters, and many others. Sensors collect ambient data and send these data through IoT to remote data centers through the Internet.

There are also robots, which can act on objects as instructed. Robots are mechanical arms. In many automated factories, robots replace human labor. The global market for robots is growing exponentially. In 2020, it was worth about $45 billion. It is expected to grow to $67 billion by 2025.4

2.1 CLOUD COMPUTING

Cloud computing is the central component of digital infrastructure. It evolves from the data center. It refers to computing services over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale. Cloud computing infrastructure, composed of both hardware and software, includes HPC servers, data storage, databases, networking, software, analytics, and other new technologies such as AI and blockchain.

Cloud computing allows many customers to share the same computing resources so that the cost of IT is reduced, and the development and deployment of applications are much faster. There is no need to procure expensive computer systems to install the operating system and application software, which also reduces all the associated maintenance costs.

In addition, the cloud environment has better cyber-security. Cloud service can also be scaled easily as needed. Cloud service providers are increasingly offering new technologies such as AI, blockchain, and many others so that even a company that does not have expertise in the area can develop applications together with the cloud service provider. It also has the added advantage of the automatic update—you are always using the latest version.

From the point of view of cloud resource ownership, there are three types of cloud—public cloud, private cloud, and hybrid cloud.

Public cloud makes computing and data storage resources available to many users over the public Internet. The public cloud provider owns and manages the data centers, hardware, and infrastructure. It is a multi-tenet environment—the data center infrastructure is shared by all cloud customers, sometimes numbering in millions.

Examples of public clouds are Amazon Web Services (AWS), Google Cloud, IBM Cloud, Microsoft Azure, and Oracle Cloud. The global public cloud market is growing rapidly. Marketing firm Gartner5 estimates that the worldwide public cloud market will exceed $330 billion by 2022.

Due to its more open platform, public cloud is vulnerable to privacy invasion and data hacking. The alternative is to use a private cloud service. Private cloud is dedicated for a single customer only. Its infrastructure is on-premises. Private cloud retains many of the benefits of cloud computing—including elasticity, scalability, and ease of service delivery—with the access control, security, and resource customization of on-premises infrastructure.

Because of the data security concerns, regulatory compliance requires certain types of sensitive information to be stored on-premises, such as confidential documents, intellectual property, or any other sensitive data. Private cloud can meet such requirements better than public cloud. Customers with such sensitive data usually choose private cloud. If public cloud is like an apartment building, private cloud is like a single-family house.

The hybrid cloud is the combination of public cloud and private cloud. The user of a hybrid cloud has an on-premises private cloud; yet it can redirect the excess demand to a public cloud.

A hybrid cloud integrates a company’s private cloud and public clouds into a single, flexible infrastructure. The company must decide what data can be stored in its private cloud and what can be stored in its public cloud. By mixing the public and private cloud resources, the company can choose which cloud to use for each workload and/or to move workloads freely between the two clouds according to changing circumstances. This enables the company to meet its IT objectives more effectively and cost-efficiently.

2.2 EDGE COMPUTING

In recent years, edge computing has attracted substantial attention because its proximity to the source of data can reduce the overheads of data transmission. This allows faster response time and better bandwidth availability. It is complementary to cloud computing. In many situations, edge computing is even preferred when there is a need for low latency and quick actuation, as in self-driving cars or privacy concerns over the transfer of sensitive data or massive amounts of data to the remote cloud. A reduction in the transmission of data to external locations also means fewer open connections and fewer opportunities for cyber attacks.

The edge computing infrastructure is expanding even faster than cloud computing facilities. By 2025, more data will be generated and processed at edge computing facilities than by using cloud computing.

Edge computing is like a localized cloud. It requires the same kind of support as cloud computing. In some aspects, the requirements of edge computing are even more demanding than those of cloud computing. This is because edge computing must support hundreds or even thousands of edge devices, and its workflows must be more flexible. At the same time, it is more vulnerable than cloud computing because of its greater exposure to the outside environment.

A new method of constructing edge computing infrastructure is emerging: composable infrastructure. Infrastructure composing is a technique to abstract hardware resources from a physical location, and use software to apply those resources where needed. The 5G Virtual Network is an example.

A composable infrastructure allows the speedy operational deployment, enabling the business to quickly deliver new products and services to the market. It also eliminates the need for workload‐specific environments and configures the resources as desired to meet the unique needs of an application. It improves performance and flexibility, reduces under-utilization, and creates a more agile, cost-effective data center.