Digital Twin Technology

Gopal Chaudhary, Manju Khari, Mohamed Elhoseny, Gopal Chaudhary, Manju Khari, Mohamed Elhoseny

• Editore: CRC Press
• Anno: 2021
• ISBN:
  9781000455915
• Edizione: 1
• Argomento: Informatica
• Categoria: Sicurezza informatica

Most of the business sectors consider the Digital Twin concept as the next big thing in the industry. A current state analysis of their digital counterparts helps in the prediction of the future of physical assets. Organizations obtain better insights on their product performance through the implementation of Digital Twins, and the applications of the technology are frequently in sectors such as manufacturing, automobile, retail, health care, smart cities, industrial IoT, etc. This book explores the latest developments and covers the significant challenges, issues, and advances in Digital Twin Technology. It will be an essential resource for anybody involved inrelated industries, as well as anybody interested in learning more about this nascent technology.

This book includes:

• The future, present, and past of Digital Twin Technology.
• Digital twin technologies across the Internet of Drones, which developed various perceptive and autonomous capabilities, towards different control strategies such as object detection, navigation, security, collision avoidance, and backup. These approaches help to deal with the expansive growth of big data solutions.
• The recent digital twin concept in agriculture, which offers the vertical framing by IoT installation development to enhance the problematic food supply situation. It also allows for significant energy savings practices. It is highly required to overcome those challenges in developing advanced imaging methods of disease detection & prediction to achieve more accuracy in large land areas of crops.
• The welfare of upcoming archetypes such as digitalization in forensic analysis. The ideas of digital twin have arisen to style the corporeal entity and associated facts reachable software and customers over digital platforms.
• Wind catchers as earth building: Digital Twins vs. green sustainable architecture.

1 Digital Twin Technology

An Evaluation

Vanita Jain, N. Luthra, and D. Saini

Bharati Vidyapeeth’s College of Engineering

DOI: 10.1201/9781003132868-1

CONTENTS

1. 1.1 Introduction: Background
2. 1.2 Digital Twin Technology
3. 1.3 Digital Twin Technology Using Convolutional Neural Networks with IoT
4. 1.4 Digital Twin Technology Using Recurrent Neural Networks with IoT
5. 1.5 Digital Twin Technology Using Long Short-Term Memory with IoT
6. 1.6 Digital Twin Technology Using Autoencoders and Variational Autoencoders with IoT
7. 1.7 Digital Twin Technology Using Generative Adversarial Networks with IoT
8. 1.8 Digital Twin Technology Using Restricted Boltzmann Machine with IoT
9. 1.9 Deep Learning on IoT Along with Other Algorithms
   1. 1.9.1 Digital Twin Technology with IoT Applications
   2. 1.9.2 Digital Twin Technology with Smart Homes
   3. 1.9.3 Digital Twin Technology with Smart City
   4. 1.9.4 Digital Twin Technology with Energy
   5. 1.9.5 Digital Twin Technology Using Intelligent Transportation System
   6. 1.9.6 Digital Twin Technology in Healthcare and Lifestyle
   7. 1.9.7 Digital Twin Technology in Agriculture
   8. 1.9.8 Digital Twin Technology in Education
   9. 1.9.9 Digital Twin Technology in Industry
   10. 1.9.10 Digital Twin Technology in Government
   11. 1.9.11 Digital Twin Technology in Sports
   12. 1.9.12 Digital Twin Technology in Retail
   13. 1.9.13 Digital Twin Technology in Smart IoT Infrastructure
   14. 1.9.14 Digital Twin Technology in On-Board ML on IoT Devices
   15. 1.9.15 Digital Twin Technology in Network Compression
   16. 1.9.16 Digital Twin Technology in Approximate Computing
   17. 1.9.17 Digital Twin Technology in Hardware Improvement
   18. 1.9.18 Digital Twin Technology with ML for IoT Infrastructure
   19. 1.9.19 Digital Twin Technology in IoT Infrastructure Security
10. 1.10 Digital Twin Technology: Future of IoT
11. References

1.1 INTRODUCTION: BACKGROUND

As technology moves forward at lightning speed, it is really easy to overlook the complete impact of technology in today’s world. As we have seen in automobiles’ invention, it made it possible to travel long distances in hours rather than days. All inventions from the wheel to the compass, clock, steam engine, and several other inventions have made our lives simpler than even kings used to have in the past. It is believed that the invention of wheel made today’s automobile possible. But if we look a couple of centuries back, the automobile was a fantasy. Similarly, the Internet is believed to be a new wheel that brings a radical change in our society. Already technological advancements have changed everything from how we consume information to economic transactions [1,2,3] taking place throughout the world. Millions of transactions are taking place every second, and a zettabyte of data is getting consumed [4,5]. As Steve Jobs said, “You can’t connect the dots looking forward, you can only connect them looking backward.” Thus, today’s fantasy on IoT will be a reality of tomorrow.

Human inventions started from simpler ones like the wheel Archimedes screw, to many complex ones like telephones, cameras, smartphones, automobiles, flying machines, etc. All these inventions have made their position in society with massive changes in all domains from political to social. These inventions have settled in people’s daily lives so profoundly that it makes it impossible for us to appreciate their importance (similar to how we appreciate the eyes or hands). Looking back at the past, we can say, not many people imagined a device like a smartphone which could also be used as a remote to control cars, helps seek answers, and who knows, might be a nuclear detonator also. These inventions have rewired the whole civilization and have reset our way of life.

Joe M. Bohlen, along with George M. Beal and Everett M. Rogers in 1957, presented the notion known as the technology adoption curve, as shown in Figure 1.1. They formulated the concept that every invention/solution/product follows a predictable path that can be traced back to the classical normal distribution, also known as the bell curve [6]. The distribution consists of innovators followed by early adopters to laggards. The formulated model still holds, but the period has accelerated at lightning speed, from century to decades, to years, and now into months. Internet of things [7] is hanging between innovators and early adopters. In the future, it will be the backbone of life and businesses. Connected devices have existed since the introduction of computer networks and consumer electronics. But it wasn’t until the Internet was introduced that researchers worldwide started to theorize a well-connected network of devices spread all around the earth with which humans can communicate and interact. A couple of years back, this fantasy started to become a reality in labs that now can be seen in everyday households; we call it the Internet of things.

FIGURE 1.1 Technology adoption curve.

Experts suggest that IoT is the second wave of the digital revolution [8,9] after computers and the Internet in the 1980s and 1990s, respectively. It is really hard to find the exact moment which led to this wave of digital revolution, but trend markers indicate Apple’s iPhone introduction in 2007 starting a domino effect. It gave enormous power in the hands of the user with point-to-point communication and digital applications.

Now, smartphones consist of multiple sensors from microphone, camera, motion sensors, GPS, and many more, along with high computation power. These smartphones can serve as a dashboard [10,11] and/or remote control for IoT devices. Radio-frequency identification (RFID) technology has also come a long way [12,13,14,15], with which we can tag or track objects along with cloud computation which can serve as a door to the future. We generate more than 2.5 Quintillion bytes of data each day, and the rate of data generation is only increasing with time. With IoT in place, data collection will take the next steps which have never been explored before, from tracking animals, insects, birds, or viruses and finding patterns in nature which humans could not understand before due to its complexity. It is like finding a new dimension with all previous physics rules, which makes it open to exploration.

IoT can help us understand intricate patterns [16] or real-time [17] conditions of bridges, roadways, buildings, or super-efficient tracking systems, where the sky is the limit. The generation of new data from sensors has unexplored usage. This will allow big data and machine learning to play an important role in solving complex problems that used to be impossible earlier.

Today machine learning [18] has already settled in our life from Internet search to music recommendations. It has crossed the early adopter’s mark in technological life cycle couple of years back, but with the combination of IoT and machine learning, it is still at the innovator’s stage. However, the next phase won’t be in years but rather months, after breaking the necessary data collection threshold. The race to collect IoT data [19,20,21,22,23] has already begun with smartphones, smartwatches, and smart speakers. New IoT devices are being theorized from old devices every day. The race to make smart electronics for the user has already begun, but the race path is still being explored. Devices such as smart bulbs, smart washing machines, or smart cars are just the tip of the iceberg in the world of IoT. The future of IoT with machine learning will change education [92,94], business, personal life, and politics [26] forever, probably for good. All technologies are dangerous in the wrong hands, and the amalgam of IoT with machine learning is no different [24,25].

The general rule in any revolution is that some people win and some people lose. IoT will make winners and open doors for new opportunities for them. But unfortunately, it will make many jobs of the current-era completely obsolete. Any potential gain of opportunity comes with plenty of challenges in IoT. A severe threat to privacy [27,28] hangs in danger, if the right actions in due time are not taken. Privacy-concerned potential weapons [29,30] for warfare, using the said technology, can be more dangerous than anything before. It won’t be a surprise to know that development of such applications has already started in the background.

In this chapter, we have discussed the future, present, and past of Digital Twin Technology. IoT itself has immense potential to change the world, but the combination of IoT with machine learning has far more potential than anything else. The applications, technologies, and security of IoT products that deal with massive data are discussed thoroughly. All the machine learning models have very specific usage, and hence, their place in IoT systems is also essential. We have discussed all machine models used in IoT and the different methods to transfer data with their appropriate application in the chapter, along with IoT security infrastructure.

1.2 DIGITAL TWIN TECHNOLOGY

Machine learning has started to take its place in IoT systems [31], but due to certain limitations, its progress is slow. IoT data for machine learning is collected using two different methods, namely, stream and accumulated ...

Indice dei contenuti

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Editors
• Contributors
• Chapter 1 Digital Twin Technology: An Evaluation
• Chapter 2 Digital Twin: Towards Internet of Drones
• Chapter 3 Digital Twin in Agriculture Sector: Detection of Disease Using Deep Learning
• Chapter 4 Crop Diseases Detection and Prevention Using AI and Machine Learning Techniques
• Chapter 5 Architecture of Digital Twin for Network Forensic Analysis Using Nmap and Wireshark
• Chapter 6 Wind Catchers as Earth Building: Digital Twins vs Green Sustainable Architecture
• Chapter 7 Digital Twin and the Detection and Location of DoS Attacks to Secure Cyber-Physical UAS
• Chapter 8 Digital Twin Techniques in Recognition of Human Action Using the Fusion of Convolutional Neural Network
• Chapter 9 eVote – A Decentralized Voting Platform
• Chapter 10 Nessus: A Vulnerability Scanner Tool in Network Forensic
• Chapter 11 Case Studies Related to Depression Detection Using Deep Learning Techniques
• Index