The Nine Pillars of Technologies for Industry 4.0 (Telecommunications)

Description

Industry 4.0 refers to automation and data exchange in manufacturing technologies. From innovative research, challenges, solutions and strategies to real-world case studies, the aim of this edited book is to focus on the nine pillars of technology that are supporting the transition to Industry 4.0 and smart manufacturing. The nine pillars include the internet of things, cloud computing, autonomous and robotics systems, big data analytics, augmented reality, cyber security, simulation, system integration, and additive manufacturing. A key role is played by the industrial IoTs and state-of-the-art technologies such as fog and edge computing, advanced data analytics, innovative data exchange models, artificial intelligence, machine learning, mobile and network technologies, robotics and sensors. This book is a useful resource for an audience of academic and industry researchers and engineers, as well as advanced students in the fields of information and communication technologies, robotics and automation, big data analytics and data mining, machine learning, artificial intelligence, AR/VR/ER, cybersecurity, cyber physical systems, sensing and robotics with a focus on Industry 4.0, and smart manufacturing. Cover Contents About the editors Foreword 1 The Nine Pillars of technology for Industry 4.0 1.1 Introduction 1.2 The Nine Pillars 1.2.1 Autonomous robots 1.2.2 Simulation 1.2.3 Horizontal and vertical system integration 1.2.4 Industrial internet of things 1.2.5 Cybersecurity 1.2.6 Cloud computing 1.2.7 Additive manufacturing 1.2.8 Augmented reality 1.2.9 Big data and data analytics 1.3 Conclusions References 2 Industry 4.0: the next frontier and its technological impacts, the role of global standardisation and sustainable development 2.1 Global standardisation in Industry 4.0 2.1.1 Technological impacts of Industry 4.0 2.2 Industry 4.0 and sustainable development References 3 Industrial revolution 4.0 – big data and big data analytics for smart manufacturing 3.1 Smart manufacturing and cyber-physical system 3.2 Overview of big data 3.2.1 Data-driven smart manufacturing 3.2.2 Data lifecycle 3.3 Big data analytics 3.3.1 Text analytics 3.3.2 Audio analytics 3.3.3 Video analytics 3.3.4 Social analytics 3.3.5 Predictive analytics 3.3.6 Big data advanced analytics for smart manufacturing References 4 Virtual and augmented reality in Industry 4.0 4.1 Industry 4.0 4.1.1 Augmented reality and virtual reality 4.1.1.1 Programming libraries 4.2 AR and VR in Industry 4.0 4.3 Conclusion References 5 Cyber security: trends and challenges toward Industry 4.0 5.1 Introduction 5.2 Recent trends 5.2.1 Web servers 5.2.2 Cloud computing 5.2.3 Advanced persistent threat 5.2.4 Smart mobile phones 5.2.5 New internet protocol 5.2.6 Code encryption 5.2.7 Social engineering 5.2.8 Social media exploits 5.2.9 Bad universal serial bus (USB) attack 5.2.10 Air-gapped system attack 5.3 Cyber security solution technologies 5.3.1 Vulnerability scanners 5.3.2 Intrusion prevention system 5.3.3 Intrusion detection system 5.4 Challenges 5.4.1 User privacy 5.4.2 Cyber security risk management 5.4.3 Digital forensics 5.5 Conclusions References 6 The role of IIoT in smart Industries 4.0 6.1 Introduction 6.1.1 Internet of Things means what? 6.1.2 Journey of IoT to IIoT to Industry 4.0 6.1.3 Difference between IoT and IIoT? 6.2 Overview of IoT in smart Industrial 4.0 6.2.1 State-of-the-art industrial Internet of Things 6.2.2 Applications of IIoT for smart Industries 4.0 6.2.2.1 Smart factory 6.2.2.2 Requirements of a smart factory 6.2.2.3 Modularity 6.2.2.4 Interoperability 6.2.2.5 Decentralisation 6.2.2.6 Driverless cars 6.2.2.7 Smart hotel rooms 6.2.2.8 Automation and adaptive with maximum efficiency 6.2.2.9 Smarter manufacturing and production with machine learning and data mining 6.2.2.10 Optimising industry processes with cloud computing and big data 6.3 Use cases of IIoT for smart Industries 4.0 6.3.1 Automotive navigation system 6.3.2 Supply-chain management and optimisation 6.3.3 Asset tracking and optimisation 6.3.4 Driving enterprise transformation 6.3.5 Connecting the form to the Cloud 6.4 The current uses and limitations of IIoT in smart Industry 4.0 6.4.1 Connectivity 6.4.2 Autonomous power 6.4.3 IoT hardware 6.4.4 Security 6.5 Open research issues and challenges of smart Industries 4.0 6.5.1 Interoperability 6.5.2 Big IIoT data analytics 6.5.3 Data security 6.5.4 Data quality 6.5.5 Visualisation 6.5.6 Privacy issues 6.5.7 Investment issues 6.5.8 Servitised business models 6.6 Future directions of IIoT in smart Industries 4.0 6.7 Conclusion Acknowledgement References 7 Simulation in the 4th Industrial Revolution 7.1 Introduction 7.2 Types of simulation 7.2.1 Simulation of a physical system 7.2.1.1 Controller design 7.2.1.2 Mechanical systems 7.2.1.3 Manufacturing systems 7.2.1.4 Transport systems 7.2.2 Interactive simulation 7.2.2.1 Physically responding simulations 7.2.2.2 Virtual reality simulation 7.3 Benefits of simulation 7.3.1 Predictive maintenance 7.3.2 Prediction 7.3.3 Design, development, and training References 8 The role of artificial intelligence in development of smart cities 8.1 Industry 4.0 and smart cities 8.2 Artificial intelligence 8.2.1 Machine vision and object recognition 8.2.2 Natural language processing 8.2.3 Cognitive computing 8.3 Role of AI in smart cities 8.3.1 Safety and surveillance 8.3.1.1 Crime detection 8.3.1.2 Crime prevention 8.3.1.3 Cybersecurity 8.3.2 Healthcare 8.3.2.1 Wellbeing 8.3.2.2 Early detection 8.3.2.3 Diagnosis 8.3.2.4 Decision-making 8.3.2.5 Treatment 8.3.2.6 End of life care 8.3.2.7 Research 8.3.2.8 Training 8.3.3 Big data 8.3.4 Transportation and infrastructure 8.3.5 Energy planning and management 8.4 Opportunities and risks References – Industry 4.0 and Smart Cities References – Artificial Intelligence References – Safety and Surveillance References – Healthcare References – Big Data References – Transportation and Infrastructure References – Energy Planning and Management Reference – Opportunities and Risks 9 How industrial robots form smart factories 9.1 Industry 4.0 and industrial robots 9.2 Smart factories 9.2.1 Efficiency and productivity 9.2.2 Safety and security 9.2.3 Flexibility 9.2.4 Connectivity 9.3 Internet of Things 9.4 Artificial intelligence 9.5 Smart materials and 3D printing References 10 Integration revolution: building trust with technology 10.1 Introduction 10.2 Objectives and methodology 10.3 Evolution of the blockchain 10.3.1 Closed and open systems 10.4 Trust economies 10.4.1 Production 10.4.2 Organisation 10.4.2.1 Smart contracts 10.4.2.2 Shareholder voting 10.4.2.3 Firm registrations 10.4.3 Services 10.4.3.1 IoT and healthcare 10.4.3.2 Smart grids/energy 10.4.4 Finance 10.4.4.1 Banking and finance 10.4.4.2 Insurance 10.4.5 Governance 10.4.5.1 Land records 10.4.5.2 Elections 10.4.5.3 Tracking tax payments 10.4.5.4 Subsidies and distribution system 10.5 Utilising the blockchain 10.6 Conclusion References 11 System integration for Industry 4.0 11.1 Introduction 11.2 Application of system integration in VLS database replication design 11.3 Database replication 11.3.1 Root of replication 11.3.1.1 Replication in a distributed system 11.3.1.2 Replication in databases 11.3.1.3 Replication in MySQL 11.3.1.4 Replication algorithm (server–server replication) 11.3.1.5 Algorithm description 11.3.1.6 MySQL algorithm policies 11.4 Virtual learning system replication policy 11.4.1 Testing of experiment II (transition and analysis) replication testing 11.4.2 Configuration on master at command prompt 11.4.3 Configuration on slave at command prompt 11.4.4 Hardware and software requirement for replication 11.5 Conclusion References 12 Additive manufacturing toward Industry 4.0 12.1 AM in various industries 12.1.1 Automotive industries and suppliers 12.1.1.1 Car components: interior and exterior 12.1.2 Aerospace industries 12.1.3 Toy industry 12.1.4 Consumer goods 12.1.5 Foundry and casting 12.1.6 Medical 12.1.7 Architecture and landscaping 12.2 Different materials used in AM 12.2.1 Plastics 12.2.2 Metals 12.2.3 Ceramics 12.2.4 Composites 12.3 Global evolution of AM 12.3.1 Early stages 12.3.2 Growth stages 12.3.3 Maturity stages 12.4 Future direction of AM 12.5 Conclusion References 13 Cloud computing in Industrial Revolution 4.0 13.1 Cloud computing 13.1.1 Benefits of cloud computing 13.1.2 Types of cloud computing 13.1.3 Types of cloud services 13.2 Fog computing 13.3 Edge computing 13.4 Security and privacy issues 13.5 Cloud computing in Industrial Revolution 4.0 13.6 Cloud computing in the communication sector 13.7 Cloud computing in healthcare sector 13.8 Scholarly articles in cloud computing 13.9 Conclusion References 14 Cybersecurity in Industry 4.0 context: background, issues, and future directions 14.1 Introduction: background and motivation 14.2 I4.0 cybersecurity characterizations 14.2.1 Cybersecurity vulnerabilities 14.2.2 Cybersecurity threats 14.2.3 Cybersecurity risks 14.2.3.1 Exposure 14.2.3.2 Attacks 14.2.4 Cybersecurity countermeasures 14.3 I4.0 security principles 14.3.1 Confidentiality 14.3.2 Integrity 14.3.3 Availability 14.3.4 Authenticity 14.3.5 Nonrepudiation 14.3.6 Privacy 14.4 I4.0 system components 14.4.1 Cloud computing 14.4.1.1 Public cloud 14.4.1.2 Private cloud 14.4.1.3 Community cloud 14.4.1.4 Fog computing 14.4.2 Big data 14.4.3 Interoperability and transparency 14.4.3.1 Application program interface 14.4.3.2 GraphQL 14.4.4 Blockchain (distributed ledgers) 14.4.5 Software-defined network 14.4.6 Multi-factor authentication 14.4.6.1 Kerberos 14.4.6.2 Two-factor authentication 14.4.6.3 Three-factor authentication 14.5 Open issues 14.5.1 Fog computing issues 14.5.2 Big data issues 14.5.3 Interoperability issues 14.5.4 GraphQL issues 14.5.5 Blockchain issues 14.5.6 SDN issues 14.5.7 Kerberos issues 14.5.8 Two-factor authentication issues 14.5.9 Three-factor authentication issues 14.6 Future directions 14.6.1 Directions to the developer/designer 14.6.2 Directions to researchers 14.6.3 Directions to industries/factories 14.7 Conclusion References 15 IoT-based data acquisition monitoring system for solar photovoltaic panel 15.1 System design and development 15.1.1 Conceptual TC sensors placement, system design, integration and installation 15.1.2 Hardware: sensors system design, integration and installation 15.2 Software design and development 15.2.1 Embedded software design integration – Raspberry Pi Zero Wireless hardware system and website 15.2.2 Embedded software design 15.3 Results and discussion 15.3.1 Hardware system design and development 15.3.2 Embedded software design and development 15.3.3 Cloud/database monitoring system 15.3.4 IoT-based data acquisition monitoring system webpage –localhost 15.4 Conclusion Acknowledgement 16 Internet of Things (IoT) application for the development of building intelligent energy management system 16.1 Introduction 16.2 Building indoor environment 16.3 Building energy management 16.4 IoT approach for data and information collection 16.4.1 Indoor environment monitoring 16.4.1.1 IoT workflow 16.4.1.2 Importance of indoor environment monitoring 16.4.2 Energy performance assessment 16.5 Conclusion References 17 Expert fault diagnosis system for building air conditioning mechanical ventilation 17.1 Introduction 17.2 Overall system description 17.2.1 Literature review 17.2.2 Knowledge acquisition 17.2.2.1 Meeting with experts 17.2.2.2 Manufacturer operating manuals 17.2.3 Design 17.2.3.1 Selection of a prototype development tool 17.2.3.2 Selection of knowledge representation technique 17.2.3.3 Selection of control technique 17.2.3.4 Development of the prototype 17.2.3.5 Development of interface 17.2.3.6 Development of the product 17.2.4 Testing and validation 17.2.5 Maintenance 17.3 System development 17.3.1 Development of the KBS 17.3.1.1 Inference engine and knowledge base 17.3.1.2 Making classes, subclasses and instances 17.3.1.3 Slots and slot value for classes and instances 17.3.1.4 Functions writing 17.3.1.5 Main function 17.3.1.6 Conclude function 17.3.1.7 Display function 17.3.1.8 Reset function 17.3.2 Create a rule 17.3.3 Goal 17.3.4 User interface 17.3.5 KAL view debugger window 17.3.6 Find and replace window 17.3.7 Rule window 17.4 Summary References 18 Lean green integration in manufacturing Industry 4.0 18.1 Green 18.2 Lean green 18.3 Integration of lean and green 18.4 Lean green needs 18.5 Lean green benefits 18.6 Lean green disadvantages 18.7 Elements integrate lean green 18.8 Lean green tools 18.8.1 Production Preparation Process (3P) 18.9 Lean enterprise supplier networks 18.10 Lean green application 18.11 Lean green standards 18.12 Chapter summary References 19 Lean government in improving public sector performance toward Industry 4.0 19.1 People development system 19.2 Lean implementation in public sector 19.3 Performance measurement in improving public sector 19.4 Problems of lean implementation in public sector 19.5 Conclusion References 20 Lean dominancy in service Industry 4.0 20.1 Definition 20.2 Transformation in lean services 20.3 Eight wastes of lean 20.4 Lean tools in service industry 20.5 Critical elements in lean service 20.5.1 Leadership and management 20.5.2 Customer focus 20.5.3 Empowering employment 20.5.4 Quality 20.5.5 Challenges in lean services 20.6 Application of lean in services 20.6.1 Lean hotel 20.6.1.1 Implementation and tool 20.6.1.2 Waste in hotel 20.6.1.3 Benefits of lean hotel 20.6.2 Lean in hospital 20.6.2.1 Tools and implementation 20.6.2.2 Waste in hospital 20.6.2.3 Benefits of lean hospital 20.6.3 Lean construction 20.6.3.1 Implementation 20.6.3.2 Tools 20.6.3.3 Wastes in construction 20.6.3.4 Benefits of lean construction 20.6.4 Lean office 20.6.4.1 Implementation 20.6.4.2 Tools 20.6.4.3 Waste 20.6.4.4 Benefits of lean office 20.7 Lean in service versus lean in manufacturing 20.7.1 Lean manufacturing 20.8 Chapter summary References 21 Case study: security system for solar panel theft based on system integration of GPS tracking and face recognition using deep le 21.1 Introduction 21.2 Method 21.2.1 Face recognition using deep learning 21.2.2 GPS tracking 21.3 Results and discussion 21.3.1 Deep learning model for face recognition system 21.3.2 Offline test 21.3.3 Online test 21.3.4 GPS tracking test 21.3.5 GPS tracking: communication system 21.3.6 GPS tracking: real-time system test 21.4 Conclusion References 22 Project Dragonfly 22.1 Introduction 22.1.1 Overview 22.1.2 Air quality 22.1.3 Water quality 22.2 Related research 22.2.1 IoT concept 22.2.2 Air quality 22.2.2.1 Effects of industrial activities on air quality 22.2.2.2 Air pollutants 22.2.2.3 Existing air quality monitoring methods 22.2.2.4 Water quality index and pollutants 22.2.2.5 Existing water quality monitoring methods 22.3 Methodology 22.3.1 Overview 22.3.2 System setup 22.3.3 Quadcopter 22.3.4 Hardware modules 22.3.4.1 Air monitoring unit 22.3.4.2 Water monitoring unit 22.3.4.3 Communication unit 22.3.4.4 Azure Cloud Service 22.3.4.5 Mobile application 22.4 Results 22.4.1 Dragonfly 22.4.2 Project Dragonfly Software 22.4.2.1 User verification 22.4.2.2 Cockpit 22.4.2.3 Monitoring results 22.5 Conclusion References 23 Improving round-robin through load adjusted– load informed algorithm in parallel database server application 23.1 Introduction 23.2 Application of LBAM 4.0 23.3 Cluster database system 23.3.1 Methodology 23.4 Experimentation 23.4.1 Hardware and software requirement for loadbalance approach model 23.5 Algorithm description 23.5.1 Round-robin algorithm 23.5.2 Translation of round-robin algorithm into PHP algorithm 23.5.2.1 Implementation (PHP which is a web server base application—program extract) 23.6 Load adjusted–load informed algorithm 23.6.1 Translation of load adjusted–load informed algorithm into PHP algorithm (adopted from Job Informed) 23.6.2 Implementation (program extract PHP) 23.7 Recommendations 23.8 Conclusion References 24 5G network review and challenges 24.1 5G network overview 24.1.1 5G network use cases 24.1.2 5G network architecture 24.2 5G network deployment 24.2.1 Deployment options for NSA and SA 24.2.2 Spectrum of 5G network 24.2.3 5G technology and technical regulations 24.2.3.1 Massive MIMO 24.2.3.2 Coverage enhancement mechanisms 24.3 Key challenges of 5G network 24.3.1 Small cell deployment challenges 24.3.2 Fibre backhaul network deployment challenges 24.3.3 5G user privacy challenges 24.4 Summary References 25 Industry 4.0 and SMEs 25.1 Introduction 25.2 Opportunities and challenges of adopting Industry 4.0 by SMEs 25.2.1 Opportunities 25.2.2 Challenges 25.3 Readiness of SMEs to adopt Industry 4.0 25.3.1 PwC Industry 4.0 self-assessment tool 25.3.2 RoSF Smart Factory assessment tool 25.3.3 WMG Industry 4.0 assessment tool 25.4 Helping SMEs to adopt Industry 4.0 25.4.1 GrowIn 4.0 25.4.2 Industry 4.0 readiness/awareness tool 25.4.3 Benefits identification 25.5 Summary References Index Back Cover