Details

Internet of Things and Data Analytics Handbook


Internet of Things and Data Analytics Handbook


1. Aufl.

von: Hwaiyu Geng

129,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 15.12.2016
ISBN/EAN: 9781119173632
Sprache: englisch
Anzahl Seiten: 816

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Beschreibungen

This book examines the Internet of Things (IoT) and Cyber-Physical Systems (CPS) from a technical, economical and application point of view Examines cloud computing, data analytics, and sustainability and how they relate to IoT/CPS Covers the scope of both consumer IoT and enterprise/government CPS applications Includes best practices, business model and real-world case studies
  List of Contributors xix Foreword xxiii Preface xxvii Acknowledgments xxix Part I INTERNET OF THINGS 1 1 Internet of Things and Data Analytics in the Cloud with Innovation and Sustainability 3Hwaiyu Geng 1.1 Introduction 3 1.2 The IoT and the Fourth Industrial Revolution 4 1.3 Internet of Things Technology 6 1.4 Standards and Protocols 11 1.5 IoT Ecosystem 11 1.6 Definition of Big Data 13 1.7 IoT, Data Analytics, and Cloud Computing 18 1.8 Creativity, Invention, Innovation, and Disruptive Innovation 18 1.9 Polya’s “How to Solve it” 20 1.10 Business Plan and Business Model 20 1.11 Conclusion and Future Perspectives 23 2 Digital Services and Sustainable Solutions 29Rikke Gram-Hansen 2.1 Introduction 29 2.2 Why IoT is not Just “Nice to Have” 30 2.3 Services in a Digital Revolution 32 2.4 Mobile Digital Services and the Human Sensor 32 2.5 Not Just Another App 33 2.6 The Hidden Life of Things 34 2.7 The Umbrellas are not what they Seem 35 2.8 Interacting with the Invisible 36 2.9 Society as Open Source 36 2.10 Learn from your Hackers 37 2.11 Ensuring High-Quality Services to Citizens 37 2.12 Government as a Platform 38 2.13 Conclusion 38 3 The Industrial Internet of Things (Iiot): Applications and Taxonomy 41Stan Schneider 3.1 Introduction to the IioT 41 3.2 Some Examples of Iiot Applications 43 3.3 Toward a Taxonomy of the Iiot 52 3.4 Standards and Protocols for Connectivity 66 3.5 Connectivity Architecture for the Iiot 73 3.6 Data-Centricity Makes Dds Different 79 3.7 The Future of the Iiot 80 4 Strategic Planning for Smarter Cities 83Jonathan Reichental 4.1 Introduction 83 4.2 What is a Smart City? 84 4.3 Smart Cities and the Internet of Things 85 4.4 Why Strategic Planning Matters 86 4.5 Beginning the Journey: First Things First 87 4.6 From Vision to Objectives to Execution 89 4.7 Pulling it all Together 91 5 Next-Generation Learning: Smart Medical Team Training 95Brenda Bannan, Shane Gallagher and Bridget Lewis 5.1 Introduction 95 5.2 Learning, Analytics, and Internet of Things 96 5.3 IoT Learning Design Process 98 5.4 Conclusion 103 6 The Brain–Computer Interface in the Internet of Things 107Jim McKeeth 6.1 Introduction 107 6.2 The Science Behind Reading the Brain 109 6.3 The Science of Writing to the Brain 112 6.4 The Human Connectome Project 113 6.5 Consumer Electroencephalography Devices 113 6.6 Summary 115 7 Iot Innovation Pulse 119John Mattison 7.1 The Convergence of Exponential Technologies as a Driver of Innovation 119 7.2 Six Dimensions of the Plecosystem 119 7.3 Five Principles of the Plecosystem 120 7.4 The Biologic Organism Analogy for the IoT 121 7.5 Components for Innovation with the Organismal Analog 122 7.6 Spinozan Value Trade-Offs 123 7.7 Human IoT Sensor Networks 123 7.8 Role of the IoT in Social Networks 124 7.9 Security and Cyberthreat Resilience 124 7.10 IoT Optimization for Sustainability of our Planet 124 7.11 Maintenance of Complex IoT Networks 125 7.12 The Accordion Model of Learning as a Source of Innovation 126 7.13 Summary 126 Part II INTERNET OF THINGS TECHNOLOGIES 129 8 Internet of Things Open-Source Systems 131Scott Amyx 8.1 Introduction 131 8.2 Background of Open Source 131 8.3 Drivers for Open Source 132 8.4 Benefits of Using Open Source 132 8.5 IoT Open-Source Consortiums and Projects 134 8.6 Finding the Right Open-Source Project for the Job 137 8.7 Conclusion 143 9 MEMS: An Enabling Technology for the Internet of Things (IoT) 147Michael A. Huff 9.1 The Ability to Sense, Actuate, and Control 148 9.2 What are MEMS? 150 9.3 MEMS as an Enabling Technology for the IoT 153 9.4 MEMS Manufacturing Techniques 155 9.5 Examples of MEMS Sensors 158 9.6 Example of MEMS Actuator 163 9.7 The Future of MEMS for the IoT 163 9.8 Conclusion 165 10 Electro-Optical Infrared Sensor Technologies for the Internet of Things 167Venkataraman Sundareswaran, Henry Yuan, Kai Song, Joseph Kimchi and Jih-Fen Lei 10.1 Introduction 167 10.2 Sensor Anatomy and Technologies 169 10.3 Design Considerations 176 10.4 Applications 179 10.5 Conclusion 184 11 Ipv6 for IoT and Gateway 187Geoff Mulligan 11.1 Introduction 187 11.2 Ip: The Internet Protocol 187 11.3 IPv6: The Next Internet Protocol 189 11.4 6LoWPAN: Ip for IoT 191 11.5 Gateways: A Bad Choice 191 11.6 Example IoT Systems 192 11.7 An IoT Data Model 194 11.8 The Problem of Data Ownership 194 11.9 Managing the Life of an IoT Device 195 11.10 Conclusion: Looking forward 195 12 Wireless Sensor Networks 197David Y. Fong 12.1 Introduction 197 12.2 Characteristics of Wireless Sensor Networks 198 12.3 Distributed Computing 201 12.4 Parallel Computing 202 12.5 Self-Organizing Networks 205 12.6 Operating Systems for Sensor Networks 206 12.7 Web of Things (WoT) 207 12.8 Wireless Sensor Network Architecture 208 12.9 Modularizing the Wireless Sensor Nodes 209 12.10 Conclusion 210 13 Networking Protocols and Standards for Internet of Things 215Tara Salman and Raj Jain 13.1 Introduction 215 13.2 IoT Data Link Protocols 218 13.3 Network Layer Routing Protocols 224 13.4 Network Layer Encapsulation Protocols 225 13.5 Session Layer Protocols 227 13.6 IoT Management Protocols 232 13.7 Security in IoT Protocols 233 13.8 IoT Challenges 234 13.9 Summary 235 14 IoT Architecture 239Shyam Varan Nath 14.1 Introduction 239 14.2 Architectural Approaches 239 14.3 Business Markitecture 242 14.4 Functional Architecture 243 14.5 Application Architecture 243 14.6 Data and Analytics Architecture 246 14.7 Technology Architecture 246 14.8 Security and Governance 248 15 A Designer’s Guide to the Internet of Wearable Things 251David Hindman and Peter Burnham 15.1 Introduction 251 15.2 Interface Glanceability 252 15.3 The Right Data at the Right Time 254 15.4 Consistency Across Channels 255 15.5 From Public to Personal 260 15.6 Nonvisual Ui 262 15.7 Emerging Patterns 264 15.8 Conclusion 265 16 Beacon Technology with IoT and Big Data 267Nick Stein and Stephanie Urbanski 16.1 Introduction to Beacons 267 16.2 What is Beacon Technology 269 16.3 Beacon and BLE Interaction 270 16.4 Where Beacon Technology can be Applied/Used 271 16.5 Big Data and Beacons 273 16.6 San Francisco International Airport (Sfo) 274 16.7 Future Trends and Conclusion 280 17 SCADA Fundamentals and Applications in the IoT 283Rich Hunzinger 17.1 Introduction 283 17.2 What Exactly is SCADA? 285 17.3 Why is SCADA the Right Foundation for an IoT Platform? 287 17.4 Case Study: Algae Lab Systems 290 17.5 The Future of SCADA and the Potential of the IoT 290 Part III DATA ANALYTICS TECHNOLOGIES 295 18 Data Analysis and Machine Learning Effort in Healthcare: Organization, Limitations, and Development of an Approach 297Oleg Roderick, Nicholas Marko, David Sanchez and Arun Aryasomajula 18.1 Introduction 297 18.2 Data Science Problems in Healthcare 298 18.3 Qualifications and Personnel in Data Science 306 18.4 Data Acquisition and Transformation 310 18.5 Basic Principles of Machine Learning 316 18.6 Case Study: Prediction of Rare Events on Nonspecific Data 321 18.7 Final Remarks 324 19 Data Analytics and Predictive Analytics in the Era of Big Data 329Amy Shi-Nash and David R. Hardoon 19.1 Data Analytics and Predictive Analytics 329 19.2 Big Data and Impact to Analytics 334 19.3 Conclusion 343 20 Strategy Development and Big Data Analytics 347Neil Fraser 20.1 Introduction 347 20.2 Maximizing the Influence of Internal Inputs for Strategy Development 348 20.3 A Higher Education Case Study 352 20.4 Maximizing the Influence of External Inputs for Strategy Development 356 20.5 Conclusion 363 21 Risk Modeling and Data Science 365Joshua Frank 21.1 Introduction 365 21.2 What is Risk Modeling 365 21.3 The Role of Data Science in Risk Management 366 21.4 How to Prepare and Validate Risk Model 367 21.5 Tips and Lessons Learned 374 21.6 Future Trends and Conclusion 380 22 Hadoop Technology 383Scott Shaw 22.1 Introduction 383 22.2 What is Hadoop Technology and Application? 384 22.3 Why Hadoop? 386 22.4 Hadoop Architecture 388 22.5 HDFS: What and how to use it 391 22.6 YARN: What and how to use it 392 22.7 Mapreduce: What and how to use it 394 22.8 Apache: what and how to use it 395 22.9 Future Trend and Conclusion 396 23 Security of IoT Data: Context, Depth, and Breadth Across Hadoop 399Pratik Verma 23.1 Introduction 399 23.2 IoT Data in Hadoop 402 23.3 Security in IoT Platforms Built on Hadoop 402 23.4 Architectural Considerations for Implementing Security in Hadoop 403 23.5 Breadth of Control 403 23.6 Context for Security 404 23.7 Security Policies and Rules Based on Pxp Architecture 404 23.8 Conclusion 405 Part Iv?SMART EVERYTHING 407 24 Connected Vehicle 409Adrian Pearmine 24.1 Introduction 409 24.2 Connected, Automated, and Autonomous Vehicle Technologies 410 24.3 Connected Vehicles from the Department of Transportation Perspective 413 24.4 Policy Issues Around DSRC 414 24.5 Alternative forms of V2X Communications 414 24.6 DOT Connected Vehicle Applications 415 24.7 Other Connected Vehicle Applications 418 24.8 Migration Path from Connected and Automated to Fully Autonomous Vehicles 419 24.9 Autonomous Vehicle Adoption Predictions 419 24.10 Market Growth for Connected and Autonomous Vehicle Technology 422 24.11 Connected Vehicles in the Smart City 423 24.12 Issues not Discussed in this Chapter 423 24.13 Conclusion 425 25 In-Vehicle Health and Wellness: An Insider Story 427Pramita Mitra, Craig Simonds, Yifan Chen and Gary Strumolo 25.1 Introduction 427 25.2 Health and Wellness Enabler Technologies inside the Car 429 25.3 Health and Wellness as Automotive Features 435 25.4 Top Challenges for Health and Wellness 440 25.5 Summary and Future Directions 444 26 Industrial Internet 447David Bartlett 26.1 Introduction (History, Why, and Benefits) 447 26.2 Definitions of Components and Fundamentals of Industrial Internet 448 26.3 Application in Healthcare 450 26.4 Application in Energy 451 26.5 Application in Transport/Aviation and Others 453 26.6 Conclusion and Future Development 454 27 Smart City Architecture and Planning: Evolving Systems through IoT 457Dominique Davison and Ashley Z. Hand 27.1 Introduction 457 27.2 Cities and the Advent of Open Data 459 27.3 Buildings in Smarter Cities 460 27.4 The Trifecta of Technology 461 27.5 Emerging Solutions: Understanding Systems 462 27.6 Conclusion 464 28 Nonrevenue Water 467Kenneth Thompson, Brian Skeens and Jennifer Liggett 28.1 Introduction and Background 467 28.2 NRW Anatomy 467 28.3 Economy and Conservation 468 28.4 Best Practice Standard Water Balance 469 28.5 NRW Control and Audit 469 28.6 Lessons Learned 472 28.7 Case Studies 473 28.8 The Future of Nonrevenue Water Reduction 479 28.9 Conclusion 479 29 IoT and Smart Infrastructure 481George Lu and Y.J. Yang 29.1 Introduction 481 29.2 Engineering Decisions 482 29.3 Conclusion 492 30 Internet of Things and Smart Grid Standardization 495Girish Ghatikar 30.1 Introduction and Background 495 30.2 Digital Energy Accelerated by the Internet of Things 497 30.3 Smart Grid Power Systems and Standards 500 30.4 Leveraging IoTs and Smart Grid Standards 503 30.5 Conclusions and Recommendations 510 31 IoT Revolution in Oil and Gas Industry 513Satyam Priyadarshy 31.1 Introduction 513 31.2 What is IoT Revolution in Oil and Gas Industry? 515 31.3 Case Study 516 31.4 Conclusion 519 32 Modernizing the Mining Industry with the Internet of Things 521Rafael Laskier 32.1 Introduction 521 32.2 How IoT will Impact the Mining Industry 523 32.3 Case Study 535 32.4 Conclusion 541 33 Internet of Things (IoT)-Based Cyber–Physical Frameworks for Advanced Manufacturing and Medicine 545J. Cecil 33.1 Introduction 545 33.2 Manufacturing and Medical Application Contexts 546 33.3 Overview of IoT-Based Cyber–Physical Framework 548 33.4 Case Studies in Manufacturing and Medicine 548 33.5 Conclusion: Challenges, Road Map for the Future 556 Part V?IoT/DATA ANALYTICS CASE STUDIES 563 34 Defragmenting Intelligent Transportation: A Practical Case Study 565Alan Carlton, Rafael Cepeda and Tim Gammons 34.1 Introduction 565 34.2 The Transport Industry and Some Lessons from the Past 566 34.3 The Transport Industry: a Long Road Traveled 567 34.4 The Transpoprt Industry: Current Status and Outlook 570 34.5 Use Case: oneTRANSPORT—a Solution to Today’s Transport Fragmentation 572 34.6 oneTRANSPORT: Business Model 575 34.7 Conclusion 578 35 Connected and Autonomous Vehicles 581Levent Guvenc, Bilin Aksun Guvenc and Mumin Tolga Emirler 35.1 Brief History of Automated and Connected Driving 581 35.2 Automated Driving Technology 583 35.3 Connected Vehicle Technology and the Cv Pilots 587 35.4 Automated Truck Convoys 589 35.5 On-Demand Automated Shuttles for a Smart City 590 35.6 A Unified Design Approach 591 35.7 Acronym and Description 592 36 Transit Hub: A Smart Decision Support System for Public Transit Operations 597Shashank Shekhar, Fangzhou Sun, Abhishek Dubey, Aniruddha Gokhale, Himanshu Neema, Martin Lehofer and Dan Freudberg 36.1 Introduction 597 36.2 Challenges 600 36.3 Integrated Sensors 600 36.4 Transit Hub System with Mobile Apps and Smart Kiosks 601 36.5 Conclusion 610 37 Smart Home Services Using the Internet of Things 613Gene Wang and Danielle Song 37.1 Introduction 613 37.2 What Matters? 613 37.3 IoT for the Masses 614 37.4 Lifestyle Security Examples 615 37.5 Market Size 617 37.6 Characteristics of an Ideal System 619 37.7 IoT Technology 624 37.8 Conclusion 630 38 Emotional Insights via Wearables 631Gawain Morrison 38.1 Introduction 631 38.2 Measuring Emotions: What are they? 632 38.3 Measuring Emotions: How does it Work? 632 38.4 Leaders in Emotional Understanding 633 38.5 The Physiology of Emotion 635 38.6 Why Bother Measuring Emotions? 636 38.7 Use Case 1 636 38.8 Use Case 2 637 38.9 Use Case 3 640 38.10 Conclusion 640 39 A Single Platform Approach for the Management of Emergency in Complex Environments such as Large Events, Digital Cities, and Networked Regions 643Francesco Valdevies 39.1 Introduction 643 39.2 Resilient City: Selex Es Safety and Security Approach 645 39.3 City Operating System: People, Place, and Organization Protection 646 39.4 Cyber Security: Knowledge Protection 650 39.5 Intelligence 651 39.6 A Scalable Solution for Large Events, Digital Cities, and Networked Regions 652 39.7 Selex ES Relevant Experiences in Security and Safety Management in Complex Situations 652 39.8 Conclusion 657 40 Structural Health Monitoring 665George Lu and Y.j. Yang 40.1 Introduction 665 40.2 Requirement 666 40.3 Engineering Decisions 667 40.4 Implementation 669 40.5 Conclusion 671 41 Home Healthcare and Remote Patient Monitoring 675Karthi Jeyabalan 41.1 Introduction 675 41.2 What the Case Study is About 676 41.3 Who are the Parties in the Case Study 677 41.4 Limitation, Business Case, and Technology Approach 678 41.5 Setup and Workflow Plan 678 41.6 What are the Success Stories in the Case Study 679 41.7 What Lessons Learned to be Improved 681 Part Vi? Cloud, Legal, Innovation, and Business Models 683 42 Internet of Things and Cloud Computing 685James Osborne 42.1 Introduction 685 42.2 What is Cloud Computing? 687 42.3 Cloud Computing and IoT 688 42.4 Common IoT Application Scenarios 690 42.5 Cloud Security and IoT 693 42.6 Cloud Computing and Makers 695 42.7 An Example Scenario 696 42.8 Conclusion 697 43 Privacy and Security Legal Issues 699Francoise Gilbert 43.1 Unique Characteristics 699 43.2 Privacy Issues 701 43.3 Data Minimization 704 43.4 Deidentification 708 43.5 Data Security 710 43.6 Profiling Issues 714 43.7 Research and Analytics 715 43.8 IoT and DA Abroad 716 44 IoT and Innovation 719William Kao 44.1 Introduction 719 44.2 What is Innovation? 719 44.3 Why is Innovation Important? Drivers and Benefits 724 44.4 How: the Innovation Process 725 44.5 Who does the Innovation? Good Innovator Skills 727 44.6 When: in a Product Cycle when does Innovation Takes Part? 729 44.7 Where: Innovation Areas in IoT 730 44.8 Conclusion 732 45 Internet of Things Business Models 735Hubert C.Y. Chan 45.1 Introduction 735 45.2 IoT Business Model Framework Review 736 45.3 Framework Development 740 45.4 Case Studies 743 45.5 Discussion and Summary 755 45.6 Limitations and Future Research 756 Index 759
Hwaiyu Geng, P.E., is a consultant with Amica Research (www.AmicaResearch.org, Palo Alto, California), promoting green planning, design, and construction projects. He has had over 40 years of manufacturing and management experience, working with Westinghouse, Applied Materials, Hewlett Packard, and Intel on multi-million high-tech projects. He has written and presented numerous technical papers at international conferences.  Mr. Geng, a patent holder, is also the editor/author of Data Center Handbook (Wiley, 2015).
This book examines the Internet of Things (IoT) and Data Analytics from a technical, application, and business point of view. Internet of Things and Data Analytics Handbook describes essential technical knowledge, building blocks, processes, design principles, implementation, and marketing for IoT projects. It provides readers with knowledge in planning, designing, and implementing IoT projects. The book is written by experts on the subject matter, including international experts from nine countries in the consumer and enterprise fields of IoT. The text starts with an overview and anatomy of IoT, ecosystem of IoT, communication protocols, networking, and available hardware, both present and future applications and transformations, and business models. The text also addresses big data analytics, machine learning, cloud computing, and consideration of sustainability that are essential to be both socially responsible and successful. Design and implementation processes are illustrated with best practices and case studies in action. In addition, the book: Examines cloud computing, data analytics, and sustainability and how they relate to IoT Covers the scope of consumer, government, and enterprise applications Includes best practices, business model, and real-world case studies

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