Data center handbook : plan, design, build, and operations of a smart data center / / Hwaiyu Geng, P.E |
Autore | Geng Hwaiyu |
Edizione | [Second edition.] |
Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] |
Descrizione fisica | 1 online resource (755 pages) |
Disciplina | 004.0684 |
Soggetto topico | Electronic data processing departments - Design and construction |
ISBN |
1-119-59755-2
1-119-59754-4 1-119-59753-6 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Sustainable data center strategic planning, design, construction, and operations with emerging technologies -- Global data center energy demand and strategies to conserve energy -- Nergy and sustainability in data centers -- Data center architecture and infrastructure -- Loud and edge computing -- Financial analysis, ROI and TCO -- Managing data center risk -- Software defined environment -- Computing, storage, networking resource management in data centers -- Wireless sensor networks to improve energy efficiency in data centers -- ASHRAE standards & practices for data centers -- Data center telecommunications cabling and TIA standards -- Air side economizer technologies -- Rack-level cooling and server-level cooling -- Corrosion (contamination) control for mission critical facilities -- Rack PDU for green data centers -- Fiber cabling fundamentals, installation and maintenance -- Design of energy efficiency IT equipment -- Energy saving technologies of servers in data centers -- Cyber-security and data centers -- Consideration of microgrids for data centers -- Data center site search and selection -- Architecture : data center rack floor plan and facility layout design -- Mechanical design in data centers -- Data center electrical design -- Electrical : uninterruptible power supply system -- Structural design in data centers : natural disaster resilience -- Fire protection and life safety design in data centers -- Reliability engineering for data centers infrastructures -- Computational fluid dynamics for data centers -- Data center project management -- Data center benchmark metrics -- Data center infrastructure management -- Data center air management -- Energy efficiency assessment of data centers using measurement and management technology -- Drive data center management and build better AI with IT devices as sensors -- Preparing data centers for natural disasters and pandemics. |
Record Nr. | UNINA-9910677883703321 |
Geng Hwaiyu
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Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] | ||
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Lo trovi qui: Univ. Federico II | ||
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Data center handbook / / [edited by] Hwaiyu Geng |
Edizione | [1st edition] |
Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons Inc., , 2014 |
Descrizione fisica | 1 recurso en línea (717 p.) |
Disciplina | 004.068/4 |
Soggetto topico |
Electronic data processing departments - Design and construction
Electronic data processing departments - Security measures |
ISBN |
1-118-93757-0
1-118-93756-2 1-118-93758-9 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Data Center Handbook; Copyright; Brief Contents; Contents; Contributors; Preface; Acknowledgments; Technical Advisory Board; Chapter Organization; Part I Data Center Overview and Strategic Planning; Chapter 1 Data Centers-Strategic Planning, Design, Construction, and Operations; 1.1 Introduction; 1.2 Data Center Vision and Roadmap; 1.3 Strategic Location Plan; 1.4 Sustainable Design; 1.5 Best Practices and Emerging Technologies; 1.6 Operations Management and Disaster Management; 1.7 Business Continuity and Disaster Recovery; 1.8 Conclusion; References; Further Reading
Chapter 2 Energy and Sustainability in Data Centers2.1 Introduction; 2.2 Flexible Facilities-Modularity in Data Centers; 2.3 Water Use; 2.4 Proper Operating Temperature and Humidity; 2.5 Avoiding Common Planning Errors; 2.6 Cooling System Concepts; 2.7 Building Envelope and Energy Use; 2.8 Air Management and Containment Strategies; 2.9 Electrical System Efficiency; 2.10 Energy Use of IT Equipment; 2.11 Leveraging IT and Facilities; 2.12 Determining Data Center Energy Use Effectiveness; 2.13 Private Industry and Government Energy Efficiency Programs 2.14 USGBC-LEED Adaptations for Data Centers2.15 Harmonizing Global Metrics for Data Center Energy Efficiency; 2.16 Industry Consortium-Recommendations for Measuring and Reporting Overall Data Center Efficiency; 2.17 Strategies for Operations Optimization; References; Further Reading; Chapter 3 Hosting or Colocation Data Centers:; 3.1 Introduction; 3.2 Hosting; 3.3 Colocation (Wholesale); 3.4 Types of Data Centers; 3.5 Scaling Data Centers; 3.6 Selecting and Evaluating DC Hosting and Wholesale Providers; 3.7 Build versus Buy; 3.8 Future Trends; 3.9 Conclusion; Further Reading Sources for Data Center Industry News and TrendsChapter 4 Modular Data Centers: Design, Deployment, and Other Considerations; 4.1 Modular Data Center Definition; 4.2 MDC Benefits and Applications; 4.3 Modularity Scalability Planning; 4.4 MDC Anatomy; 4.5 Site Preparation, Installation, Commissioning; 4.6 How to Select an MDC Vendor; 4.7 External Factors; 4.8 Future Trend and Conclusion; Further Reading; Chapter 5 Data Center Site Search and Selection; 5.1 Introduction; 5.2 Site Searches Versus Facility Searches; 5.3 Globalization and the Speed of Light; 5.4 The Site Selection Process 5.5 Industry Trends Affecting Site SelectionFurther Reading; Chapter 6 Data Center Financial Analysis, ROI and TCO; 6.1 Introduction to Financial Analysis, Return on Investment, and Total Cost of Ownership; 6.2 Financial Measures of Cost and Return; 6.3 Complications and Common Problems; 6.4 A Realistic Example; 6.5 Choosing to Build, Reinvest, Lease, or Rent; Further Reading; Chapter 7 Overview of Data Centers in China; 7.1 Introduction; 7.2 Policies, Laws, Regulations, and Standards; 7.3 Standards; 7.4 Development Status of China's Data Centers; 7.5 Energy Efficiency Status 7.6 Development Tendency |
Record Nr. | UNINA-9910140482603321 |
Hoboken, New Jersey : , : John Wiley & Sons Inc., , 2014 | ||
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Lo trovi qui: Univ. Federico II | ||
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The internet of things and data analytics handbook / / edited by Hwaiyu Geng |
Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2017 |
Descrizione fisica | 1 online resource (811 pages) : illustrations |
Disciplina | 004.67/8 |
Soggetto topico |
Internet of things
Data mining Cooperating objects (Computer systems) Big data |
ISBN |
1-119-17362-0
1-119-17363-9 1-119-17360-4 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
-- -- 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 3 /Hwaiyu 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 29 /Rikke 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 41 /Stan 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 83 /Jonathan 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 95 /Brenda 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 107 /Jim 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 119 /John 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 131 /Scott 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) 147 /Michael 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 167 /Venkataraman 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 187 /Geoff 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 197 /David 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 215 /Tara 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 239 /Shyam 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 251 /David 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 267 /Nick 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 283 /Rich 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 297 /Oleg 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 329 /Amy 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 347 /Neil 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 365 /Joshua 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 383 /Scott 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 399 /Pratik 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 409 /Adrian 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 427 /Pramita 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 447 /David 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 457 /Dominique 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 467 /Kenneth 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 481 /George 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 495 /Girish 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 513 /Satyam 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 521 /Rafael 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 545 /J. 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 565 /Alan 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 581 /Levent 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 597 /Shashank 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 613 /Gene 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 631 /Gawain 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 643 /Francesco 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 665 /George 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 675 /Karthi 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 685 /James 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 699 /Francoise 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 719 /William 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 735 /Hubert 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. |
Record Nr. | UNINA-9910155255703321 |
Hoboken, New Jersey : , : John Wiley & Sons, , 2017 | ||
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Lo trovi qui: Univ. Federico II | ||
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