Recent Trends and Best Practices in Industry 4.0 1st Edition by Abhinav Sharma, Arpit Jain, Paawan Sharma, Mohendra Roy 8770228051 9788770228053

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ISBN 10: 8770228051

ISBN 13: 9788770228053

Author: Abhinav Sharma, Arpit Jain, Paawan Sharma, Mohendra Roy

Industry 4.0 is used interchangeably with the fourth industrial revolution and represents a new stage in the organization asnd control of the industrial value chain. Cyber-physical systems form the basis of industry 4.0 (e.g., ‘smart machines’). They use modern control systems, have embedded software systems, can be addressed via IoT (the Internet of Things), and may use extensive data analytics and/or articifical inteligence systems to operate autonomously. The aim of this book is to provide detailed insights into the state of art techniques in AI, IoT, Blockchain technology and associated technologies which play a vital role in the implementation of a successful project for upcoming and practicing engineers. Owing to its multidisciplinary nature, Industry 4.0 is not a single topic but a combination of a multitude of technologies from different domains. Keeping this in mind the book includes the following topics: Artificial intelligence Internet of things Blockchain technology Digital manufacturing Robotics Cybersecurity The book will be a comprehensive guide to academicians and engineers who want to align with recent trends of fourth industrial revolution.

Recent Trends and Best Practices in Industry 4.0 1st Table of contents:

1 Artificial Intelligence in the Digital Chemical Industry, its Application and Sustainability

1.1 Introduction

1.2 AI in the Chemical Industry and Applications

1.2.1 AI in chemical science

1.2.2 AI in research and development

1.2.3 AI in catalyst design spaces

1.3 Sustainability of AI Applications

1.3.1 Environmental aspect

1.3.2 Energy aspect

1.3.3 Economic aspect

1.3.4 Time aspect

1.3.5 Safety and human factor aspect

1.4 Digital Transformation of the Chemical Industry

1.4.1 State of digital transformation

1.4.2 Key trends in digitalization

1.4.3 Optimizing production

1.4.4 Supporting remote operations

1.4.5 Reducing waste

1.4.6 Unlocking new growth opportunities

1.4.7 Increasing supply chain visibility

1.4.8 Safety, compliance, and sustainability

1.5 Digital Chemical Industry 4.0

1.6 Challenges of AI in the Chemical Industry

1.7 Industry 4.0 Impact Technologies

1.8 Conclusion

References

2 Managing the Transition Toward Industry 4.0: A Study on the Implementation of Digital Manufacturing Processes

2.1 Introduction

2.2 Main Text

2.2.1 Problem formulation

2.2.2 Purpose and question

2.2.3 Related research

2.2.4 Industry 4.0

2.2.5 Strategic guidance for IIoT

2.2.6 Strategy

2.2.7 Resources and competencies

2.2.8 IT maturity

2.2.9 Smart manufacturing

2.2.10 Method

2.2.11 Delimitation

2.2.12 Method selection

2.2.13 Data collection

2.2.14 Literature review

2.2.15 Interview

2.2.16 Selection and respondents

2.2.17 Ethics

2.2.18 Data analysis

2.2.19 Transcription

2.2.20 Coding

2.2.21 Method discussion

2.3 Outcomes of the Study

2.3.1 Industry 4.0

2.3.2 Strategy

2.3.3. Resources and competencies

2.3.4. IT maturity

2.3.5 Smart manufacturing

2.4 Conclusion

References

3 Container as a Service in the Cloud: An Approach to Secure Hybrid Virtualization

3.1 Introduction

3.2 Virtualization in Cloud Computing

3.2.1 System level virtualization

3.2.2 OS level virtualization

3.2.3 Container vs. virtual machine

3.2.4 Architectural difference between VM and container

3.3 Container as a Service Model

3.3.1 CaaS architecture

3.4 Containerization Techniques

3.4.1 Docker

3.4.2 Singularity

3.4.3 uDocker

3.5 Research Challenges

3.6 Conclusion

References

4 Automated Framework for Detecting Unknown Activity in a Vehicular ad hoc Network

4.1 Introduction

4.1.1 VANET deployment challenges

4.1.2 VANET applications

4.1.3 Possible attacks on vehicular network

4.1.4 ML for vehicular networking

4.1.5 Current challenges and opportunities

4.1.6 Research gap

4.1.7 Problem statement

4.1.8 Motivation

4.2 Literature Work

4.2.1. An overview

4.2.2 Mobility-based routing in VANET with security

4.2.3 Encryption and authentication

4.3 Proposed Approach

4.3.1 Methodology

4.3.2 Improve hybrid cooperative malicious node detection approach (IHCMNDA)

4.4 Result and Discussion

4.4.1 Simulation

4.4.2 Improved cooperative bait detection (ICBDS)

4.4.3 Improve hybrid cooperative malicious node detection approach (IHCMNDA)

4.5 Conclusion and Future Work

References

5 Control of Mobile Manipulator with Object Detection for EOD Applications

5.1 Introduction

5.1.1 Explosive ordnance disposal robots

5.2 Object Detection

5.2.1 Computer-aided design of the proposed design

5.2.2 System architecture

5.3 Hardware Architecture

5.3.1 Robot-arm contol and actuation

5.4 Object Detection

5.4.1 TensorFlow

5.4.2 TensorFlow object detection API

5.5 Results and Discussion

5.5.1 Hardware implementation

5.5.2 Robotic arm simulation using MATLAB GUI

5.5.3 Simulation in gazebo using ROS

5.6 Conclusion

Acknowledgments

References

6 Smart Agriculture: Emerging and Future Farming Technologies

6.1 Introduction

6.2 Role of Smart Agriculture in Yield Enhancement

6.2.1 Soil samples and its mapping

6.2.2 Smart irrigation systems

6.2.3 Smart fertilizer system

6.2.4 Smart disease control and pest management

6.2.5 Smart monitoring of crop yields and climatic conditions

6.3 Technologies Involved in Precision Agriculture

6.3.1 Wireless sensing technology in agriculture

6.3.2 Communication methods and latest technologies in smart agriculture

6.3.3 Latest technologies for large data processing

6.4 Autonomous Vehicles in Smart Agriculture

6.4.1 IoT-based tractors

6.4.2 Harvesting robots

6.4.3 UAVs in agriculture

6.5 Challenges in Smart Agriculture

6.6 Conclusions and Future Directions

References

7 Plant Feature Extraction for Disease Classification

7.1 Introduction

7.2 Literature Review

7.3 Features Representation and Characteristics

7.4 Texture Features

7.4.1 Statistical methods for texture classification

7.4.2 Gray-level run-length matrix

7.4.3 Advantages and limitations

7.4.4 Histogram of gradient magnitudes

7.5 Color Features Extraction

7.5.1 Advantage and disadvantages

7.5.2 Color co-occurrence matrix (CCM)

7.6 Shape-based Feature Extraction

a. Distances (Object length)

b Center of gravity

c Area (A):

d Hole:

e Perimeter (p):

f. Major-axis Angle

g. Roundness

h Euler number

i Eccentricity

j Boundary rectangle area

7.6.1 Advantage and disadvantages

7.7 Conclusion and Future Scope

References

8 Development Methodologies for the Internet of Things: For all Commercial and Industrial Needs

8.1 Introduction

8.2 Research Questions

8.3 Development Methodologies

8.3.1 Development methodologies for IoT-based smart cities

8.3.2 Development methodologies for IoT-based automatic driving

8.3.3 Development methodologies for IoT-based agriculture field monitoring

8.3.4 Development methodologies for IoT-based railway monitoring

8.3.5 Development methodologies for IoT-based forest monitoring

8.4 Conclusion

References

9 Bio-inspired Multilevel ICHB-HEED Clustering Protocol for Heterogeneous WSNs

9.1 Introduction

9.2 Review of Literature

9.3 Proposed Work

9.3.1 Network model for multilevel energy heterogeneity

9.3.2 MLICHBHEED protocol

9.4 Results and Discussions

9.4.1 Network lifetime

9.4.2 Total energy consumption

9.4.3 Number of packets sent to the base station

9.5 Conclusion

References

10 IoT Enabled by Edge Computing for Telecomms and Industry

10.1 Introduction to IoT and Edge Computing

10.1.1 IoT and edge-computing scenarios

10.1.2 IoT versus edge computing

10.1.3 Industry 4.0

10.1.4 Cloud computing and edge computing

10.1.5 Cloud computing and edge computing: use cases

10.2 IoT and Edge Computing Framework

10.2.1 IoT and edge computing integrated framework

10.2.2 Pros and cons

10.2.3 Opportunities and challenges

10.3 Edge Computing Devices

10.4 Telcos and Edge Computing

10.4.1 Telco network modernizations challenges

10.4.2 Multi-access edge computing [16], [17]

10.5 Task Scheduling in Edge Computing

10.5.1 Scheduler-based edge computing

10.5.2 Computing task analysis

10.5.3 Computing task scheduling scheme

10.6 Security and Privacy Issues of Edge Computing

10.6.1 Attacks classification

10.7 Conclusion

References

11 Low-energy Network Protocols

11.1 Introduction

11.2 IIoT

11.2.1 Network architecture

11.2.2 Protocols

11.2.3 Testbed

11.2.4 Criteria for implementation

11.3 Low-energy Technologies

11.4 Self-powered Network

11.4.1 Comparison between energy harvesting WSNs and battery-operated WSNs

11.4.2 Energy-harvesting sources for sensors

11.4.3 Energy stored by a supercapacitor

11.4.4 Proposed circuit

11.4.5 BLE–RSL10 from ON Semiconductor

11.4.6 Key features of BLE-RSL10 [17]

11.4.7 Lifetime improvement of WSN

11.4.8 Energy model for WSN

11.4.9 Lifetime optimization in a mobile WSN with solar energy harvesting

11.4.10 Time-slotted approach

11.4.11 Optimization algorithm

11.5 Simulation of WSN Network and Results

11.5.1 Simulation parameters

11.5.2 Simulation results

11.6 Future Scope

11.7 Conclusion

References

12 Consensus Algorithms in Blockchain for an Efficient and Secure Network

12.1 Introduction

12.1.1 Motivation

12.2 Literature Review

12.3 Emergence of Cryptocurrencies

12.4 Role of Consensus Algorithms

12.4.1 Proof of work

12.4.2 Proof of stake

12.4.3 Delegated proof of stake (DPoS)

12.4.4 Byzantine fault tolerance (BFT)

12.4.5 Proof of burn (PoB)

12.4.6 Proof of capacity (PoC)

12.4.7 Proof of elapsed time (PoET)

12.5 Limitation of Consensus Algorithms

12.5.1 Failure of consensus

12.5.2 Ledger forks

12.5.3 Substandard performance

12.6 Conclusion

References

13 Blockchain Protocols and Algorithms

13.1 Introduction

13.2 Blockchain Technology and Industry 4.0

13.2.1 Basic concepts

13.2.2 Related works

13.2.3 Algorithm or protocol?

13.3 Known Blockchain Consensus Algorithms

13.3.1 Popular algorithms

13.3.2 Derivative algorithms

13.4 Less-known Blockchain Consensus Algorithms

13.4.1 Algorithms based on PoW

13.4.2 Algorithms based on PoS

13.4.3 Algorithms based on FBA

13.4.4 Hybrid algorithms

13.4.5 PoX algorithms

13.5 Blockchain Protocols

13.5.1 Bitcoin

13.5.2 Ethereum

13.5.3 Corda

13.5.4 Quorum

13.5.5 Hyperledger

13.5.6 Eosio

13.5.7 NEM (New Economy Movement)

13.5.8 Multichain

13.5.9 Consensus algorithms and protocols classification

13.6 Blockchain Applications

13.6.1 How to develop a blockchain application?

13.6.2 Example of blockchain application in industry 4.0

13.6.3 How to choose a blockchain consensus algorithm and protocol?

13.7 Conclusion

References

14 Cybersecurity in Autonomous Driving Vehicles

14.1 Introduction

14.1.1 Industry 4.0

14.1.2 Autonomous vehicles and industry 4.0

14.1.3 Criteria to use autonomous vehicles in industrial operation

14.2 IoT and Cybersecurity

14.2.1 Cybersecurity—risk assessment

14.3 Autonomous Driving Applications

14.3.1 Autonomous vehicle road behavior

14.4 Autonomous Driving Vehicle: Enabling Technologies

14.4.1 Functional framework of autonomous vehicle

14.5 Autonomous Vehicle Security

14.5.1 Protection methods

14.5.2 Potential security risks of AV crowdsourcing

14.5.3 Attacks on V2X communications technologies

14.5.4 Autonomous vehicle defense

14.5.5 Security by design

14.5.6 Autonomous vehicle security management

14.6 Cyberattacks on Autonomous Vehicle

14.6.1 Security and privacy threats: case of autonomous automated vehicles

14.7 Conclusion

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