Intelligent Connectivity: AI, IoT, and 5G (IEEE Press)

Description

This book focuses on the fusion between the core technologies of the future – Artificial Intelligence (AI), 5G, and the Internet of Things (IoT), exploring how they can be mutually supportive. AI, IoT, and 5G are the pillars that lead digital transformation, and the combination of super-fast 5th generation networks with AI and IoT will usher in a new age of intelligent connectivity, particularly beneficial to transportation, logistics, education, healthcare, entertainment, public safety/security and industrial and manufacturing operations. The book will offer technical and economic insights about intelligent connectivity as a key part of transformation in the 4th industrial revolution. The reader (technical and non-technical) will benefit from a comprehensive discussion of Artificial Intelligence, 5G, IoT and machine-learning and how they are vital in the period of open-source programming and moderate cloud computing. Cover Title Page Copyright Page Contents Preface Acknowledgement Introduction Chapter 1 Technology Adoption and Emerging Trends 1.1 Introduction 1.2 Trends in Business Technology 1.2.1 Trends that Could Disrupt the Industry 1.2.2 Adopting New Technologies 1.2.3 Best Practices and Risks Associated with Emerging Technologies 1.2.4 Power of Disruptive Technologies 1.2.5 Driving Strategy Around Our Priority 1.2.6 Strategic Partnerships to be Pursued 1.3 AI-Fueled Organizations 1.4 Connectivity of Tomorrow 1.4.1 Intelligent Interfaces 1.5 Moving Beyond Marketing 1.6 Cloud Computing 1.7 Cybersecurity, Privacy, and Risk Management 1.8 Conclusion References Chapter 2 Telecommunication Transformation and Intelligent Connectivity 2.1 Introduction 2.1.1 Learning Algorithm and Its Connections to AI 2.1.2 Machine Learning as a Precursor to AI 2.1.3 Deep Learning and Realization of AI 2.1.4 Consideration of the Next Generation Wireless Technology 2.1.5 Potential of AI and 5G Network Technology Together 2.2 Cybersecurity Concerns in the 5G World 2.2.1 5G’s Potential in Making Security a Priority 2.2.2 Key Features 2.3 Positive Effects of Addressing Cybersecurity Challenges in 5G 2.4 Intelligent Connectivity Use-Cases 2.4.1 Transportation and Logistics 2.4.2 AI-based Driver Assistance and Monitoring 2.4.3 Self-Driving Vehicles 2.4.4 Deliveries with Unmanned Vehicles 2.5 Industrial and Manufacturing Operations 2.5.1 Factory Automation and Remote Control of Industrial Robots 2.5.2 Remote Inspections and Maintenance, and Worker’s Training 2.6 Healthcare 2.6.1 Remote Health Monitoring and Illness Prevention 2.6.2 Remote Diagnosis and Medical Operation 2.7 Public Safety and Security 2.7.1 Intelligent Video-Surveillance and Security Systems 2.7.2 Emergency Services and Border Controls 2.7.3 Other Sectors 2.8 Conclusion References Chapter 3 The Internet of Things (IoT): Potentials and the Future Trends 3.1 Introduction 3.2 Achieving the Future of IoT 3.3 Commercial Opportunities for IoT 3.4 The Industrial Internet of Things 3.4.1 How IIoT Works 3.4.2 Benefits of IIoT 3.4.3 IIoT versus IoT 3.4.4 IIoT Applications and Examples 3.4.5 Vendors in IIoT 3.4.6 The Future of IIoT 3.5 Future Impact of IoT in Our Industry 3.6 Data Sharing in the IoT Environment 3.7 IoT Devices for Environment Operation 3.7.1 Step One: Pick Your Protocol 3.7.2 Step Two: Understand Coexistence 3.7.3 Step Three: Pick Your Technique 3.7.4 Step Four: Create Your Test Plan 3.8 Interoperability Issues of IoT 3.9 IoT-Cloud – Application 3.10 Regulation and Security Issues of IoT 3.11 Achieving IoT Innovations While Tackling Security and Regulation Issues 3.12 Future of IoT 3.13 Conclusion References Chapter 4 The Wild Wonders of 5G Wireless Technology 4.1 Introduction 4.1.1 First Generation (1G) 4.1.2 Second Generation (2G) 4.1.3 Third Generation (3G) 4.1.4 Advanced Third Generation (3.5G) 4.1.5 Fourth Generation (4G) 4.1.6 Fifth Generation (5G) 4.2 5G Architecture 4.2.1 Realizing New 5G Possibilities with the Intelligent Edge 4.3 5G Applications 4.3.1 5G and Video Surveillance 4.3.2 5G and Fixed Wireless Access (FWA) 4.4 5G Network Architecture 4.5 Security and Issues of 5G 4.6 IoT Devices in 5G Wireless 4.7 Big Data Analytics in 5G 4.8 AI Empowers a Wide Scope of Use Cases 4.9 Conclusion References Chapter 5 Artificial Intelligence Technology 5.1 Introduction 5.2 Core Concepts of Artificial Intelligence 5.3 Machine Learning and Applications 5.4 Deep Learning 5.5 Neural Networks Follow a Natural Model 5.6 Classifications of Artificial Intelligence 5.7 Trends in Artificial Intelligence 5.7.1 Artificial Intelligence in Energy 5.7.2 Artificial Intelligence in Healthcare 5.7.3 Artificial Intelligence in Education 5.7.4 Artificial Intelligence in Manufacturing 5.7.5 Artificial Intelligence in Financial Services 5.7.6 Artificial Intelligence in Transport 5.8 Challenges of Artificial Intelligence 5.8.1 Data 5.9 Funding Trends in Artificial Intelligence 5.9.1 Artificial Readiness 5.9.2 Foundational Readiness 5.9.3 Operational Readiness 5.9.4 Transformational Readiness 5.10 Conclusion References Chapter 6 AI, 5G, and IoT: Driving Forces Towards the Industry Technology Trends 6.1 Introduction 6.2 Fifth Generation of Network Technology 6.3 Internet of Things (IoT) 6.4 Industrial Internet of Things 6.5 IoT in the Automotive Industry 6.6 IoT in Agriculture 6.7 AI, IoT, and 5G Security 6.8 Conclusion References Chapter 7 Intelligent Connectivity: New Capabilities to Bring Complex Use Cases 7.1 Introduction 7.1.1 Artificial Intelligence 7.1.2 The Fifth Generation Networks 7.1.3 The Internet of Things 7.2 Machine-to-Machine Communication and the Internet of Things 7.3 Convergence of Internet of Things, Artificial Intelligence, and 5G 7.3.1 The Benefits of Intelligent Connectivity 7.4 Intelligent Connectivity Applications 7.4.1 Industry 7.4.2 Transport and Logistics 7.4.3 Healthcare 7.4.4 Security 7.4.5 Smart Homes and Personal Assistant 7.4.6 Wearable Technology 7.4.7 Entertainment 7.4.8 Communication 7.4.9 Resource Management 7.4.10 Agriculture 7.4.11 Education 7.5 Challenges and Risks of Intelligent Connectivity 7.5.1 Economic Risks 7.5.2 Risks to Human Safety and Agency 7.5.3 Social Risk 7.5.4 Secondary Risks 7.5.5 Confidentiality and Scalability 7.6 Recommendations 7.7 Conclusion References Chapter 8 IoT: Laws, Policies, and Regulations 8.1 Introduction 8.2 Recently Published Laws and Regulations 8.2.1 IoT Cybersecurity Improvement Act of 2017 8.3 Developing Innovation and Growing the Internet of Things (DIGIT) Act 8.4 General View 8.5 Relaxation of Laws by the Federal Aviation Administration (FAA) 8.6 Supporting Innovation of Self-Driving Cars by Government Policies 8.6.1 Investment by US Department of Homeland Security 8.6.2 United States Guiding Principles for IoT Security 8.6.3 The United Kingdom on IoT 8.6.4 United States Department of Commerce 8.6.5 Federal Trade Commission and Creating an IoT Security Solution 8.7 Recommendations 8.8 Conclusion References Chapter 9 Artificial Intelligence and Blockchain 9.1 Introduction 9.2 Decentralized Intelligence 9.2.1 Data Protection 9.2.2 Trusting AI Decision Making 9.2.3 AI and Encryption 9.3 Applications 9.3.1 The Coordination of Blockchain into AI 9.3.2 Essential Blockchain Benefits 9.4 How Artificial Intelligence and Blockchain Will Affect Society 9.4.1 Banking and Payments 9.4.2 Cybersecurity 9.4.3 Internet of Things 9.4.4 Unified Communications 9.4.5 Government 9.4.6 Crowdfunding and Donating to Charities 9.4.7 Healthcare 9.4.8 Rentals and Ride-Sharing 9.5 Augmented Reality 9.5.1 Augmented Reality in the Production Context 9.5.2 How Augmented Reality Works 9.5.3 Marker and Marker-Less AR 9.5.4 Layered AR 9.5.5 Projection AR 9.5.6 AR in Education 9.5.7 AR in Navigation 9.5.8 AR in Games 9.6 Mixed Reality 9.7 Virtual Reality 9.7.1 Virtual World 9.7.2 Mental Immersion 9.7.3 Physical Immersion 9.7.4 Tangible Feedback 9.7.5 Intelligence 9.7.6 Types of Virtual Reality 9.7.7 Semi-Immersive 9.7.8 Completely Immersive 9.8 Key Components in a Virtual Reality System 9.8.1 PC (Personal Computer)/Console/Smartphone 9.8.2 Head-Mounted Display 9.8.3 Information Devices 9.8.4 Augmented Reality versus Virtual Reality 9.8.5 Benefits of Augmented Reality 9.9 Augmented Reality Uses 9.9.1 Retail 9.9.2 Real Estate 9.9.3 Interior Design 9.9.4 Tourism and Maps 9.9.5 Training and Education 9.9.6 Healthcare 9.10 Applications of Virtual Reality in Business 9.10.1 Training 9.10.2 Retail 9.10.3 Construction 9.10.4 Data Representation 9.10.5 Manufacture 9.11 The Future of Blockchain 9.12 Blockchain Applications 9.12.1 National Cryptographic Money 9.12.2 Blockchain into Government 9.12.3 Blockchain Specialists 9.13 Blockchain and the Internet of Things 9.14 Law Coordination 9.15 Collaboration for Blockchain Success References Chapter 10 Digital Twin Technology 10.1 Introduction 10.2 The Timeline and History of Digital Twin Technology 10.3 Technologies Employed in Digital Twin Models 10.3.1 Cloud Services 10.3.2 Cyber-Physical Systems 10.4 The Dimension of Digital Twin Models 10.4.1 Digital Twin Data 10.4.2 Services in Digital Twins 10.4.3 Connection in Digital Twins 10.4.4 Physical Assets in Digital Twins 10.4.5 Virtual Entities in Digital Twins 10.5 Digital Twin and Other Technologies 10.5.1 Digital Twins and Internet of Things 10.5.2 Digital Twins and Artificial Intelligence 10.5.3 Digital Twins and Analytics 10.5.4 Digital Twins and Connectivity 10.5.5 Digital Twins and Machine Learning 10.6 Digital Twin Technology Implementation 10.7 Benefits of Digital Twins 10.8 Application of Digital Twins 10.8.1 Manufacturing 10.8.2 Healthcare 10.8.3 Smart Cities 10.8.4 Space Exploration 10.8.5 Business 10.9 Challenges of Digital Twins 10.9.1 Privacy and Data Security 10.9.2 Infrastructure 10.9.3 Data 10.9.4 Trust 10.9.5 Expectations References Chapter 11 Artificial Intelligence, Big Data Analytics, and IoT 11.1 Introduction 11.2 Analytics 11.2.1 Predictive Analytics 11.2.2 Prescriptive Analytics 11.2.3 Descriptive Analytics 11.3 AI Technology in Big Data and IoT 11.4 AI Technology Applications and Use Cases 11.5 AI Technology Impact on the Vertical Market 11.5.1 AI Predictive Analytics in the Vertical Market 11.6 AI in Big Data and IoT Market Analysis and Forecasts 11.7 Conclusion References Chapter 12 Digital Transformation Trends in the Automotive Industry 12.1 Introduction 12.2 Evolution of the Automotive Industry 12.3 Data-Driven Business Model and Data Monetization 12.3.1 Big Data 12.3.2 Product Development 12.4 Services of the Data-Driven Business Model 12.5 Values of New Services in the New Automotive Industry 12.5.1 Consumer Trust 12.6 Conclusion References Chapter 13 Wireless Sensors/IoT and Artificial Intelligence for Smart Grid and Smart Home 13.1 Introduction 13.2 Wireless Sensor Networks 13.3 Power Grid Impact 13.4 Benefits of the Smart Grid 13.5 Internet of Things 13.6 Internet of Things on the Smart Grid 13.6.1 Smart Grid Security 13.7 Smart Grid and Artificial Intelligence 13.8 Smart Grid Programming 13.9 Conclusion References Chapter 14 Artificial Intelligence, 5G, and IoT: Security 14.1 Introduction 14.2 Understanding IoT 14.3 Artificial Intelligence 14.4 5G Network 14.5 Emerging Partnership of Artificial Intelligence, IoT, 5G, and Cybersecurity 14.5.1 The Current State of IoT Security 14.6 Conclusion References Chapter 15 Intelligent Connectivity and Agriculture 15.1 Introduction 15.2 The Potential of Wireless Sensors and IoT in Agriculture 15.3 IoT Sensory Technology with Traditional Farming 15.3.1 IoT Sensors Available for Specific Agriculture Applications 15.3.2 Challenges Faced While Implementing Sensor Technologies 15.4 IoT Devices and Communication Techniques 15.5 IoT and all Crop Stages 15.6 Drone in Farming Applications 15.7 Conclusion References Chapter 16 Applications of Artificial Intelligence, ML, and DL 16.1 Introduction 16.2 Building Artificial Intelligence Capabilities 16.3 What is Machine Learning? 16.3.1 Machine Learning Methods 16.4 Deep Learning 16.4.1 Use Cases 16.4.2 The Working Mechanism 16.4.3 Deep Learning Models 16.4.4 Deep Learning and MATLAB® 16.5 Machine Learning vs. Deep Learning Comparison 16.5.1 Data Dependencies 16.5.2 Hardware Dependencies 16.5.3 Problem-Solving Approach 16.6 Feature Engineering 16.6.1 Layerwise Features of Deep Learning 16.6.2 Execution Time in DL 16.6.3 Interpretability 16.7 Applications of Machine Learning 16.7.1 Virtual Personal Assistants 16.7.2 Predictions While Commuting 16.7.3 Video Surveillance 16.7.4 Social Media Services 16.7.5 Spam Email and Malware Filtering 16.7.6 Online Customer Support 16.7.7 Improved Search Engine Results 16.7.8 Product Recommendations 16.7.9 Fraud Detection on the Web 16.8 Applications of Deep Learning 16.8.1 Self-Driving Cars 16.8.2 Healthcare 16.8.3 Voice Assistants and Search 16.8.4 Movies and Sound Effects 16.8.5 Auto Translator 16.8.6 Auto Text Generation 16.8.7 Automatic Handwriting Generation 16.8.8 Image Colorization 16.8.9 Earthquake Prediction 16.8.10 Detection of Brain Cancer 16.8.11 Finance 16.8.12 Energy Price 16.9 Future Trends References Chapter 17 Big Data and Artificial Intelligence: Strategies for Leading Business Transformation 17.1 Introduction 17.2 Big Data 17.2.1 Big Data Need for Organizations 17.2.2 Big Data and Applications 17.3 Machine Learning-Based Medical Systems 17.4 Artificial Intelligence for Stock Market Predictions 17.4.1 Application of Artificial Intelligence by Investors 17.5 Trends in AI and Big Data Technologies Drive Business Innovation 17.5.1 Driving Innovation Through Big Data 17.5.2 The Convergence of AI and Big Data 17.5.3 How AI and Big Data Will Combine to Create Business Innovation 17.5.4 AI and Big Data for Technological Innovation 17.5.5 Disruptive Innovation 17.6 Advancements and New Entries 17.6.1 Recruitment of a Skilled Taskforce 17.6.2 Reliable Performance 17.7 AI and Production 17.7.1 Methodology 17.8 AI and ML Operations Research 17.8.1 Smart Maintenance 17.8.2 Intelligent Manufacturing 17.8.3 IoT-Enabled Manufacturing 17.8.4 Cloud Manufacturing 17.8.5 Suitability of ML with AI 17.9 Collaboration Between Machines and Humans 17.10 Generative Designs 17.11 Adapting to a Changing Market 17.11.1 Conclusion References Index EULA