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Bibliografická citace

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0 (hodnocen0 x )
BK
First published
Hoboken : Wiley, 2017
xxxviii, 869 stran : ilustrace (některé barevné), portréty ; 25 cm

objednat
ISBN 978-1-119-22639-0 (vázáno)
Obsahuje bibliografické odkazy a rejstřík
001462992
Contents // Editors Biographies xxiii List of Contributors xxvii Foreword xxxiii Preface xxxv Acknowledgments xxxvii // 1 Cyber-Physical Systems in Smart Cities - Mastering Technological, Economic, and Social Challenges 1 Martino Fromhold-Eisebith // 1.1 Introduction 1 // 1.2 Setting the Scene: Demarcating the Smart City and Cyber-Physic al Systems 3 // 1.3 Process Fields of CPS-Driven Smart City Development 4 // 1.4 Economic and Social Challenges of Implementing the CPS-Enhanced Smart City 10 // 1.5 Conclusions: Suggestions for Planning the CPS-Driven Smart City 15 // Final Thoughts 17 Questions 18 References 18 // 2 Big Data Analytics Processes and Platforms Facilitating Smart Cities 23 // Pethuru Raj and Sathish A. P Kumar // 2.1 Introduction 24 // 2.2 Why Big Data Analytics (BDA) Is Significant for Smarter Cities 24 // 2.3 Describing the Big Data Paradigm 26 // 2.4 The Prominent Sources of Big Data 27 // 2.4.1 The Salient Implications of Big Data 27 // 2.4.2 Information and Communication Infrastructures for Big Data and Its Platforms 28 // Contents // 2.4.3 Transitioning from Big Data to Big Insights 29 // 2.5 Describing Big Data Analytics (BDA) 29 // 2.6 The Big Trends and Use Cases of Big Data Analytics 31 // 2.6.1 Customer Satisfaction Analysis 32 // 2.6.2 Market Sentiment Analysis 32 // 2.6.3 Epidemic Analysis 32 // 2.6.4 Using Big Data Analytics in Healthcare 33 // 2.6.5 Analytics of Machine Data by Splunk 34 // 2.7 The Open Data for Next-Generation Cities 38 // 2.8 The
Big Data Analytics (BDA) Platforms 39 // 2.8.1 Civitas: The Smart City Middleware 42 // 2.8.2 Hitachi Smart City Platform 43 // 2.8.3 Data Collection 44 // 2.8.4 Data Analysis 44 // 2.8.5 Application Coordination 45 // 2.9 Big Data Analytics Frameworks and Infrastructure 45 // 2.9.1 Apache Hadoop Software Framework 46 // 2.9.2 NoSQL Databases 48 // 2.10 Summary 50 Final Thoughts 51 Questions 51 References 52 // 3 Multi-Scale Computing for a Sustainable Built // Environment 53 Massimiliano Man fren // 3.1 Introduction 53 // 3.2 Modeling and Computing for Sustainability Transitions 55 // 3.2.1 Multilevel Perspective Modeling 56 // 3.2.2 Technological and Social Learning 57 // 3.2.3 Multidisciplinary System Thinking 58 // 3.2.4 Long-Term Thinking for the Built Environment 59 // 3.2.5 Data and Modeling Techniques 63 // 3.3 Multi-Scale Modeling and Computing for the Built Environment 66 // 3.3.1 Virtual Prototyping for Design Optimization 67 // 3.3.2 Performance Optimization Across Building Life Cycle Phases 68 // 3.4 Research in Modeling and Computing for the Built Environment 70 // 3.4.1 Building Energy Balance Analysis 74 // 3.4.2 Forward/Inverse Modeling and Visualization Techniques 76 // 3.4.3 Workflows and Integration of Modeling Strategies 76 // 3.4.4 Research Advances in Modeling and Computing 81 Final Thoughts 82 // Questions 84 References 84 // 4 Autonomous Radios and Open Spectrum in Smart Cities 99 Corey D. Cooke and Adam L Anderson // 4.1 Introduction 99 // 4.2 Candidate
Wireless Technologies 101 // 4.2.1 Open Spectrum 101 // 4.2.2 5G Wireless Technologies 103 // 4.2.3 Internet of Things (loT) 104 // 4.3 PHY and MAC Layer Issues in Cognitive Radio Networks 105 // 4.3.1 Spectrum Sensing 106 // 4.3.1.1 Detection Methods 106 // 4.3.1.2 Cooperative Spectrum Sensing 107 // 4.3.1.3 Other Sensing Issues 107 // 4.3.2 Spectrum Management and Handoff 108 // 4.3.3 Rendezvous Problem 109 // 4.3.4 Coexistence 109 // 4.4 Frequency Envelope Modulation (FEM) 110 // 4.4.1 Network Self-Configuration 112 // 4.4.2 Physical Layer Performance 113 // 4.4.3 Experimental Results 115 // 4.5 Conclusion 116 Final Thoughts 117 Questions 118 References 118 // 5 Mobile Crowd-Sensing for Smart Cities 125 Chandreyee Chowdhury and Sarbani Roy // 5.1 Introduction 125 // 5.2 Overview of Mobile Crowd-Sensing 127 // 5.2.1 Categories of Crowd-sensing 127 // 5.2.2 Architecture of Mobile Crowd-sensing 127 // 5.2.3 Applications of Mobile Crowd-sensing in Smart City 131 // 5.2.3.1 Applications in Infrastructure 131 // 5.2.3.2 Environmental Applications 134 // 5.2.3.3 Social Applications 134 // 5.3 Issues and Challenges of Crowd-sensing in Smart Cities 135 // 5.3.1 Task Assignment Problem 135 // 5.3.2 User Profiling and Trustworthiness 139 // 5.3.3 Incentive Mechanisms 140 // 5.3.4 Localized Analytics 141 // 5.3.5 Security and Privacy 142 // 5.4 Crowd-sensing Frameworks for Smart City 144 // 5.4.1 Here-tf-Now Framework 144 // 5.4.2 Crowd-sensing Framework based on XMPP 146 // viii Contents
// 5.4.3 McSense 146 // 5.4.4 Supporting Framework for Crowd-sensing Apps 148 // 5.5 Conclusion 149 // Final Thoughts 149 Questions 150 References 150 // 6 Wide-Area Monitoring and Control of Smart Energy Cyber-Physical Systems (CPS) 155 // Nilonjon R. Chaudhuri // 6.1 Introduction 155 // 6.2 Challenges and Opportunities 156 // 6.2.1 Wide-Area Monitoring: Damping, Frequency, and Mode-shape Estimation 156 // 6.2.2 Wide-Area Damping Control: Latency Compensation 157 // 6.2.3 Wide-Area Damping Control with Wind Farms 159 // 6.3 Solutions 159 // 6.3.1 Phasor Approach 159 // 6.3.2 Wide-Area Monitoring: Damping, Frequency, and Mode-Shape Estimation 161 // 6.3.3 Test System: 16-Machine, 5-Area System 162 // 6.3.3.1 Simulation Results 163 // 6.3.4 Wide-Area Damping Control: Latency Compensation 166 // 6.3.4.1 Simulation Results 168 // 6.3.5 Wide-Area Damping Control with Wind Farms 169 // 6.3.5.1 DFIG-based Wind Farm Modeling 169 // 6.3.5.2 Rotor Side Converter (RSC) Control 171 // 6.3.5.3 Grid Side Converter (GSC) Control 171 // 6.3.5.4 Control Input 172 // 6.3.5.5 Coordinated Control Design 172 // 6.3.5.6 Simulation Results 172 // 6.4 Conclusions and Future Direction 173 Final Thoughts 175 Questions 175 References 175 // 7 Smart Technologies and Vehicle-to-X (V2X) Infrastructures for Smart Mobility Cities 151 // Bernard Fong, Lixin Situ, and Alvis C. M. Fong // 7.1 Introduction 181 // 7.2 Data Communications in Smart City Infrastructure 182 // 7.2.1 Data Acquisition 183 // 7.2.2 Traffic
Surveillance 185 // 7.3 Deployment: An Economic Point of View 186 // 7.3.1 Detecting Abnormal Events 187 // 732 Network Failure 188 // 7.3.3 Micromobility Data Communications 190 // 7.3.4 V2X Network Integration and Interoperability 194 // 7.4 Connected Cars 195 // 7.4.1 Multi-Hop Communication in V2X 195 // 7.4.2 Green V2X Communications in Smart Cities 198 // 7.4.3 Vehicular Communications Infrastructure Reliability 200 // 7.4.4 Business Intelligence in Connected Cars 201 // 7.5 Concluding Remarks 202 // Final Thoughts 203 Questions 203 References 204 // 8 Smart Ecology of Cities: Integrating Development Impacts on Ecosystem Services for Land Parcels 209 // More Morrison, Rovi S. Srinivoson, and Cynnomon Dobbs // 8.1 Introduction 209 // 8.2 Need for Smart Ecology of Cities 212 // 8.3 Ecosystem Service Modeling (C02 Sequestration, PM10 Filtration, Drainage) 214 // 8.3.1 Overview of Ecosystem Services in Urban Contexts 214 // 8.3.2 C02 Sequestration 215 // 8.3.3 PM 10 Filtration 217 // 8.3.4 Drainage 218 // 8.4 Methodology 219 // 8.4.1 Carbon Sequestration 219 // 8.4.2 Drainage 224 // 8.4.3 PM 10 Filtration 228 // 8.5 Implementation of Development Impacts in Dynamic-SIM Platform 231 // 8.6 Discussion (Assumptions, Limitations, and Future Work) 234 // 8.7 Conclusion 235 Final Thoughts 236 Questions 236 References 236 // 9 Data-Driven Modeling, Control, and Tools for Smart Cities 243 // Modhur Beh! ond Rohu I Monghorom // 9.1 Introduction 245 // 9.1.1 Contributions 247 // Contents
// 9.1.2 Experimental Validation and Evaluation 248 // 9.2 Related Work 248 // 9.3 Problem Definition 250 // 9.3.1 DR Baseline Prediction 250 // 9.3.2 DR Strategy Evaluation 251 // 9.3.3 DR Strategy Synthesis 251 // 9.4 Data-Driven Demand Response 252 // 9.4.1 Data Description 252 // 9.4.2 Data-Driven DR Baseline 253 // 9.4.3 Data-Driven DR Evaluation 253 // 9.5 DR Synthesis with Regression Trees 254 // 9.5.1 Model-Based Control with Regression Trees 254 // 9.5.2 DR Synthesis Optimization 256 // 9.6 The Case for Using Regression Trees for Demand Response 259 // 9.7 DR-Advisor: Toolbox Design 261 // 9.8 Case Study 263 // 9.8.1 Building Description 263 // 9.8.2 Model Validation 265 // 9.8.3 Energy Prediction Benchmarking 265 // 9.8.4 DR Evaluation 266 // 9.8.5 DR Synthesis 268 // 9.8.5.1 Revenue from Demand Response 271 // 9.9 Final Thoughts 271 Questions 272 References 272 // 10 Bringing Named Data Networks into Smart Cities 275 // Syed Hassan Ahmed, Saldar Hussain Bouk, Dongkyun Kim, and Mahasweta Sarkar // 10.1 Introduction 275 // 10.2 Future Internet Architectures 278 // 10.2.1 Data-Oriented Network Architecture (DONA) 278 // 10.2.2 Network of Information (Netlnf) 279 // 10.2.3 Publish Subscribe Internet Technology (PURSUIT) 281 // 10.3 Named Data Networking (NDN) 282 // 10.4 NDN-based Application Scenarios for Smart Cities 285 // 10.4.1 NDN in loT for Smart Cities 285 // 10.4.2 NDN in Smart Grid for Smart Cities 287 // 10.4.3 NDN in WSN for Smart Cities 288 // 10.4.4 NDN in
MANETs for Smart Cities 290 // 10.4.5 NDN in VANETs for Smart Cities 293 // 10.4.6 NDN in Climate Data Communications 296 // 10.5 Future Aspects of NDN in Smart Cities 297 // 10.5.1 NDN Content/Data 297 // 10.5.2 Naming Content/Data in NDN 298 // 10.5.3 NDN Data Structures 299 // 10.5.4 NDN Message Forwarding 299 // 10.5.5 Content Discovery in NDN 300 // 10.5.6 NDN in Dynamic Network Topology 301 // 10.5.7 Content Caching in NDN 301 // 10.5.8 Security and Privacy 302 // 10.5.9 Evaluation Methods 303 // 10.6 Conclusion 303 // Final Thoughts 304 Questions 304 References 304 // 11 Human Context Sensing in Smart Cities 311 Juhi Ranjon, Erin Griffiths, ond Komin Whitehouse // 11.1 Introduction 311 // 11.2 Human Context Types 312 // 11.2.1 Physiological Context 313 // 11.2.2 Emotive Context 314 // 11.2.3 Functional Context 315 // 11.2.4 Location Context 316 // 11.3 Sensing Technologies 317 // 11.3.1 Video and Audio 317 // 11.3.2 Wearables 320 // 11.3.3 Smartphones 324 // 11.3.4 Environment 328 // 11.4 Conclusion 331 Final Thoughts 332 Questions 332 References 333 // 12 Smart Cities and the Symbiotic Relationship between Smart Governance and Citizen Engagement 343 // Tori Okner ond Russell Preston // 12.1 Smart Governance 344 // 12.1.1 Smart Governance and Smart Cities 347 // 12.1.2 The Role of Planning & Design 347 // 12.2 Case Study - Somerville, Massachusetts 348 // 12.2.1 Slumerville to Somerville 348 // 12.2.1.1 Professionalizing City Hall 349 // 12.2.2 Planning Somerville 352
Contents // 12.2.2.1 SomerVision 352 // 12.2.2.2 Somerville by Design 352 // 12.2.2.3 Smart Cities and Planning in Somerville 363 // 12.3 Looking Ahead 365 // 12.3.1 Lessons from Somerville 365 // 12.3.2 Recommendations 367 Final Thoughts 368 Questions 370 References 370 // 13 Smart Economic Development 373 Modhovi Venkoteson // 13.1 Introduction 373 // 13.1.1 Significance of Educating for Sustainability 374 // 13.1.2 Economics in Cultural Context 375 // 13.1.3 Reconciling Economic Theory and Historical Context 376 // 13.1.4 Significance of Context 377 // 13.2 Perception of Resource Value, Market Outcomes, and Price 378 // 13.2.1 Market Distortions 379 // 13.2.2 Externalities 380 // 13.2.3 Common Goods 382 // 13.2.4 Market Prices 383 // 13.3 Conscious Consumption and the Sustainability Foundation of Smart Cities 384 // 13.3.1 Smart Economic Development 386 // 13.3.2 Next Steps Theory and Practice 387 Final Thoughts 388 // Questions 388 References 388 // 14 Managing the Cyber Security Life-Cycle of Smart Cities 391 // Mridul S. Borik, Anirbon Sengupta, and Chandan Mazumdar // 14.1 Introduction 391 // 14.2 Smart City Services 393 // 14.3 Smart Services Technologies 394 // 14.4 Smart Services Security Issues 396 // 14.5 Management of Cyber Security of Smart Cities 397 // 14.5.1 Scope and Cyber Security Policy Formulation 398 // 14.5.2 Cyber Security Requirements Identification 400 // 14.5.3 Risk Management 400 // 14.5.4 Detailed Security Policy Formulation 401 // 14.5.5 Security
Measures Implementation 401 // 14.5.6 Cyber Security Incident Management 401 // 14.5.7 Service Continuity Management and Disaster Recovery 402 // 14.5.8 Cyber Security Metrics Generation 403 // 14.5.9 Audit and Compliance Checking 403 // 14.6 Discussion 403 // 14.7 Conclusion 404 // Questions 404 References 40S // 15 Mobility as a Service 409 // Christopher Expósito-lzquierdo, Ai ram Expósito-Mŕrquez, and Julio Brito-Santana // 15.1 Introduction 409 // 15.2 Mobility as a Service 413 // 15.2.1 Millennials 413 // 15.2.2 Concept of Mobility as a Service 415 // 15.2.3 Transportation Infrastructures 418 // 15.2.4 Information and Communications Technologies 420 // 15.2.5 Interoperability 422 // 15.2.6 Autonomous Car 423 // 15.2.7 Connected Vehicle 424 // 15.2.8 Sharing Mobility 425 // 15.3 Case Studies on Mobility as a Service 427 // 15.3.1 UbiGo 427 // 15.3.2 car2Go 428 // 15.3.3 Uber 430 // 15.3.4 RideScout 431 // 15.4 Conclusions and Further Research 432 Acknowledgments 433 // Final Thoughts 433 Questions 433 References 434 // 16 Clustering and Fuzzy Reasoning as Data Mining Methods for the Development of Retrofit Strategies for Building Stocks 437 // Philipp Geyer and Arno Schlueter // 16.1 Introduction 438 // 16.1.1 Problem Description 438 // 16.1.2 Smart Cities and Data Mining 438 // 16.1.3 Approach 438 // 16.1.3.1 Clustering 438 // 16.1.3.2 Conditions of Data Mining in Building Stock 439 // 16.1.3.3 Contents of the Chapter 439 // xiv Contents // 16.2 Method 440 // 16.3 Application
Case 442 // 16.4 Data Sources and Preprocessing 443 // 16.4.1 Data Sources and Modeling 443 // 16.4.1.1 Public Databases and Datasets 444 // 16.4.1.2 Protocols and Surveys 444 // 16.4.1.3 Sensor Measurements and Bottom-Up Modeling 445 // 16.4.2 Construction of the Feature Space 446 // 16.4.2.1 Metamodeling 446 // 16.4.2.2 The Feature Space 447 // 16.5 Clustering 448 // 16.5.1 Nonspatial Clustering 448 // 16.5.2 Spatial Clustering 451 // 16.5.2.1 Managing Nonuniform Effect Dimensions 452 // 16.5.2.2 Results of One-Step Spatial Clustering 452 // 16.5.2.3 Two-Step Spatial Clustering Results 454 // 16.6 Fuzzy Reasoning 456 // 16.6.1 Nonspatial Fuzzy Reasoning 456 // 16.6.1.1 Ramp Membership Functions 456 // 16.6.2 Spatial Fuzzy Reasoning 458 // 16.7 Mixed Fuzzy Reasoning and Clustering 459 // 16.8 Postprocessing: Interpretation and Strategy Identification 459 // 16.8.1 Data Visualization 459 // 16.8.2 Data Mining Postprocessing 461 // 16.8.3 Transformation Strategy Development 463 // 16.8.4 Strategy Development 463 // 16.8.5 Policy Development and Retrofit Programs 463 // 16.9 Comparison and Discussion of Methods 464 // 16.9.1 Information 465 // 16.9.2 Scaling 465 // 16.9.3 Robustness 466 // 16.10 Conclusion 467 Final Thoughts 468 Questions 468 Acknowledgments 469 References 469 // 17 A Framework to Achieve Large Scale Energy Savings for Building Stocks through Targeted Occupancy Interventions 473 // Aslihon Koratas, Allisondro Stoiko, ond Carol C Menasso // 17.1 Introduction 474
// 17.2 Objectives 475 // 17.3 Review of Occupancy-Focused Energy Efficiency Interventions 476 // 17.3.1 Knowledge-Based Interventions 477 // 17.3.2 Persuasion Interventions 478 // 17.3.3 Penalty Interventions 479 // 17.3.4 Technology Interventions 480 // 17.3.5 Building Energy Use Interventions in Energy Policy Design 480 // 17.4 Role of Occupants’ Characteristics in Building Energy Use 481 // 17.5 A Conceptual Framework for Delivering Targeted // Occupancy-Focused Interventions 483 // 17.5.1 Measuring the Impact of Occupancy Characteristics on Building Energy Use 483 // 17.5.2 Clustering Occupants’ MOA Levels and Energy Use Profiles 486 // 17.5.3 Identifying Multilevel Building Energy Use Intervention Strategies 487 // 17.6 Case Study Example 490 // 17.7 Discussion 493 // 17.8 Conclusions and Policy Implications 494 // Questions 496 Acknowledgment 496 // References 496 // 18 Sustainability in Smart Cities: Balancing Social, Economic, Environmental, and Institutional Aspects of Urban Life 503 All Komeily ond Rovi Srinivoson // 18.1 Introduction 503 // 18.2 Sustainability Assessment in Our Cities 506 // 18.3 Sustainability in Smart Cities 508 // 18.4 Achieving Balanced Sustainability 512 // 18.4.1 Improving Procedural Balance 512 // 18.4.2 Improving Contextual and Temporal Balance 512 // 18.4.2.1 City Blocks as a Contextual Variable 513 // 18.4.3 Improving Integrational Balance 518 // xvi Contents // 18.4.3.1 Institutional and Governing Aspect 520 // 18.4.4 Current Developments:
Sustainability Information Modeling Platforms 520 Final Thoughts 521 Questions 522 Appendix 1 522 // Appendix 2 523 References 531 // 19 Toward Resilience of the Electric Grid 535 Jiankang Wang // 19.1 Electric Grids in Smart Cities 536 // 19.1.1 Structure of Power Systems 537 // 19.1.1.1 Vertical Structure 537 // 19.1.1.2 Transmission 538 // 19.1.1.3 Station and Substation 539 // 19.1.1.4 Distribution 539 // 19.1.2 Operation of Power Systems 540 // 19.1.2.1 Control 541 // 19.1.2.2 Scheduling 542 // 19.1.2.3 Protection 542 // 19.1.2.4 Distribution Automation 543 // 19.2 Threats to Electric Grids 544 // 19.2.1 Threats to the Physical Grid 544 // 19.2.1.1 Threats from Weather Hazards 544 // 19.2.1.2 Threats from Malicious Attacks 545 // 19.2.1.3 Models for Threats on the Physical Grid 546 // 19.2.2 Threats to the Cyber Layers of Power Systems 547 // 19.2.2.1 How Secure Is the Cyber Layer of Power Systems? 547 // 19.2.2.2 Classification of Cyber Attacks 547 // 19.2.2.3 Attacks on the Control Layer 548 // 19.2.2.4 Attacks on the Monitoring Layer 551 // 19.3 Electric Grid Response under Threats 553 // 19.3.1 (Unwanted) Physical Phenomena on Power Grids 553 // 19.3.1.1 Circuit Faults 553 // 19.3.1.2 Frequency Instability 554 // 19.3.1.3 Voltage Instability 555 // 19.3.2 Failing Mechanisms on Electric Grids 556 // 19.3.3 Modeling and Vulnerability Assessment Methods of Grid Response 557 // 19.4 Defense against Threats to Electric Grids 558 // 19.4.1 Recommendations for Grid Resilience
Enhancement 559 // 19.4.2 Core Technologies for Power System Resilience 560 // 19.4.2.1 Emergency Control and Protection 560 // 19.4.2.2 Restoration from the Distribution Level 561 // 19.4.3 Development of Defense Methods against Threats 562 // 19.4.3.1 Defense on the Physical Grid 562 // 19.4.3.2 Defense on the Control Layer 564 // 19.4.3.3 Defense on the Monitoring Layer 565 Final Thoughts 567 // Questions 568 References 568 // 20 Smart Energy and Grid: Novel Approaches for the Efficient Generation, Storage, and Usage of Energy in the Smart Home and the Smart Grid Linkup 575 // Julian Praß, Johannes Weber, Sebastian Staub, Johannes Bürner, Ralf Böhm, Thomas Braun, Moritz Hein, Markus Michl, Michael Beck, and Jörg Franke // 20.1 Introduction 576 // 20.2 Generation of Energy 576 // 20.2.1 Aerodynamically and Aeroacoustically Optimized Small Wind Turbines 576 // 20.2.2 Combined Heat and Power Micro Plants Using Organic Rankine Cycles 578 // 20.3 Storage of Energy 581 // 20.3.1 Thermal Storage Heating Systems 581 // 20.3.2 Connecting Smart Homes to the Smart Grid 584 // 20.4 Smart Usage of Energy 587 // 20.4.1 Energy-Efficient Radiation Heating 587 // 20.4.2 Comfort-Orientated Heating in Smart Homes-Overview and Field Study 593 // 20.4.3 Smart Waste Heat Usage and Recovery from Refrigerators and Freezers 595 // 20.4.4 Ventilation with Heat Recovery 597 // 20.5 Summary 600 // Final Thoughts 600 Questions 601 References 601 // 21 Building Cyber-Physical Systems - A Smart Building
Use Case 605 // Jupiter Bakakeu, Franziska Schäfer, Jochen Bauer, Markus Michl, and Jörg Franke // 21.1 Foundations—From Automation to Smart Homes 606 // 21.2 From Today’s Technologically Augmented Houses to Tomorrow s Smart Homes 608 // xviii Contents // 21.2.1 Smart Home: Past, Present, and Future 608 // 21.2.2 CPS-Based Smart Home Automation: Design Challenges 611 // 21.3 Smart Home: A Cyber-Physical Ecosystem 612 // 21.3.1 Use Cases and Smart Home Scenarios 613 // 21.3.2 Interoperability for CPS-based Smart Home Environments 615 // 21.3.3 Decentralized Coordination and Cooperation: Applying Agent-Based Theory to Adaptive Architectural Environments 618 // 21.3.3.1 Multi-Agent Systems (MAS) 619 // 21.3.3.2 Potentials of MAS for Smart Building Applications 620 // 21.3.3.3 Applying MAS to Smart Building Environment 621 // 21.3.4 Application: A Decentralized Control in Private Homes Based on the Paradigms of the Industry 4.0 624 // 21.3.5 Application: Ambient Assisted Living (AAL) 627 // 21.4 Connecting Smart Homes and Smart Cities 629 // 21.5 Conclusion and Future Research Focus 631 Final Thoughts 632 // Questions 632 References 633 // 22 Climate Resilience and the Design of Smart Buildings 641 Saranya Gunasingh, Nora Wang, Doug Ahi, and Scott Schuetter // 22.1 Climate Change and Future Buildings and Cities 642 // 22.2 Carbon Inventory and Current Goals 644 // 22.3 Incorporating Predicted Climate Variability in Building Design 646 // 22.4 Case Studies 648 // 22.4.1 Modeling
Methodology 648 // 22.4.2 Analysis of Climate Scenarios and Impacts 651 // 22.4.3 Results 652 // 22.4.3.1 Case 1: Southern Mississippi Space Center 652 // 22.4.3.2 Case 2: Chicago Multifamily Building 658 // 22.4.3.3 Case 3: Fort Collins Multifamily Building 660 // 22.4.4 Limitations of the Study and Future Work 662 // 22.5 Implications for Future Cities and Net-Zero Buildings 662 Final Thoughts 664 Questions 664 References 665 // 23 Smart Audio Sensing-Based HVAC Monitoring 669 ShahriarNirjon, Ravi Srinivasan, and Tamim Sookoor // 23.1 Introduction 669 // 23.2 Background 671 // 23.2.1 HVAC Failure Detection 671 // 23.2.2 HVAC Failures and Acoustics 672 // 23.2.3 Strategy for Acoustic-Based HVAC Fault Detection 674 // Contents // xix // 23.3 The Design of SASEM 675 // 23.3.1 Building a Low-Cost Sensing Platform 675 // 23.3.2 Acoustic Modeling of HVAC Systems 678 // 23.3.3 Decision Support System 682 // 23.4 Experimental Results 685 // 23.4.1 Spectral Analysis of HVAC Sounds 685 // 23.4.2 Longer-Term Deployment 687 Final Thoughts 689 Questions 689 Acknowledgement 690 References 690 // 24 Smart Lighting 697 Jie Lion // 24.1 Introduction 697 // 24.2 Background 698 // 24.3 Smart Lighting Applications 699 // 24.4 Visible Light Communication (Smart Lighting Communication) System 701 // 24.4.1 System Description 702 // 24.4.1.1 Transmitter and Receiver Model 702 // 24.4.1.2 Indoor VLC Channel Model 702 // 24.4.2 VLC MIMO Technology 706 // 24.4.2.1 Modulation Schemes in VLC Systems 708
// 24.4.3 On-Off Keying (OOK) 708 // 24.4.3.1 Ai-ary Pulse Amplitude Modulation (AÍ-PAM) 709 // 24.4.3.2 Pulse Position Modulation (PPM) 710 // 24.4.4 Multiuser VLC Systems 710 // 24.4.4.1 Multiple Access Schemes 710 // 24.4.4.2 Cellular Structure for VLC Systems 713 // 24.4.5 Practical Considerations 716 // 24.4.5.1 Intersymbol Interference (ISI) 716 // 24.4.5.2 Nonlinearity of LEDs 717 // 24.4.5.3 Illumination Requirements and Dimming Control 717 // 24.5 Conclusion and Outlook 718 Final Thoughts 719 Questions 719 References 719 // 25 Large Scale Air-Quality Monitoring in Smart and Sustainable Cities 725 // Xioofon Jiang // 25.1 Introduction 726 // 25.2 Current Approaches to Air Quality Monitoring and Their Limitations 729 // Contents // 25.3 Overview of a Cloud-based Air Quality Monitoring System 731 // 25.3.1 Data Sources 731 // 25.3.2 Data Representation and Storage 731 // 25.3.3 Air Quality Analytics Engine 732 // 25.3.4 APIs and Applications 732 // 25.4 Cloud-Connected Air Quality Monitors 733 // 25.4.1 Sensor Selection 733 // 25.4.2 Mechanical Design 734 // 25.4.3 Data Communication 734 // 25.4.4 Hardware Calibration 734 // 25.5 Cloud-Side System Design and Considerations 736 // 25.5.1 sMAP 736 // 25.5.2 Data Format, Authentication, Storage and Web Services 737 // 25.6 Data Analytics in the Cloud 739 // 25.6.1 Filtering Using Signal Reconstruction 739 // 25.6.2 Calibration Using Artificial Neural Networks 740 // 25.6.2.1 Neural Network Model 741 // 25.6.3 Online Inference
Model 742 // 25.6.3.1 Gaussian Process 742 // 25.6.3.2 Gaussian Process Regression 743 // 25.6.4 Evaluation of Effectiveness 744 // 25.7 Applications and APIs 748 Final Thoughts 748 Questions 751 References 751 // 26 The Smart City Production System 755 // Gory Graham, Jag Srai, Patrick Hennelly, and Roy Meriton // 26.1 Introduction 755 // 26.2 Types of Production System: Historical Evolution 757 // 26.2.1 Pure Fordism 1920s Onward 757 // 26.2.2 Toyota Production System (TPS) 758 // 26.2.3 Post-Fordism 759 // 26.3 The Integrated Smart City Production System Framework 761 // 26.3.1 Smart City Infrastructure 761 // 26.3.2 Big Data 762 // 26.3.3 Industrial Internet of Things 762 // 26.4 Production System Design 763 // 26.4.1 Network Design 763 // 26.4.2 Redistributed Manufacturing (RDM) 764 // 26.4.3 Manufacturing Scale and Inventory 766 // 26.4.4 Distribution and Service 766 // 26.5 Chapter Summary 767 // Final Thoughts 768 Questions 768 References 768 // 27 Smart Health Monitoring Using Smart Systems 773 Carl Chalmers // 27.1 Introduction 773 // 27.2 Background 775 // 27.2.1 Advanced Metering Infrastructure 775 // 27.2.2 Smart Meters 777 // 27.2.3 AMI Implementation Challenges 781 // 27.2.4 Patient Behavior and Uses 782 // 27.2.4.1 Active Monitoring for Behavioral Changes with Dementia 783 // 27.2.4.2 Active Monitoring for Behavioral Changes in Depression and Other Mental Illness 784 // 27.2.4.3 Prediction for EIP 785 // 27.2.5 Current Assistive Technologies 785 // 27.3 Integration
for Monitoring Applications 786 // 27.3.1 Case Study 787 // 27.4 Conclusion 788 Final Thoughts 789 Questions 789 References 789 // 28 Significance of Automated Driving in Japan 793 Sadayuki Tsugawa // 28.1 Introduction 793 // 28.2 Definitions of Automated Driving Systems 794 // 28.3 A History of Research and Development of Automated Driving Systems 795 // 28.3.1 Classification of Automated Driving Systems 795 // 28.3.2 The First Period of Automated Driving Systems 796 // 28.3.3 The Second Period of Automated Driving Systems 798 // 28.3.4 The Third Period of Automated Driving Systems 798 // 28.3.5 The Fourth Period of Automated Driving Systems 801 // 28.4 Expected Benefits of Automated Driving 804 // 28.4.1 Safety 804 // 28.4.2 Efficiency 804 // 28.5 Issues of Automated Driving for Market Introduction 805 // 28.5.1 Performance of Human Drivers 805 // 28.5.2 What we Learned from Experiments 805 // 28.5.3 Reliability and MTBF Requirements of the Systems and the Devices 806 // xxii I Contents // 28.5.4 Issues on Human Factors 807 // 28.6 Possible Market Introduction of Automated Driving Systems in Japan 808 // 28.6.1 Population Issues 808 // 28.6.2 Small, Low-Speed Automated Vehicles 809 // 28.6.3 Automated Truck Platoons 813 // 28.6.4 Cooperative Adaptive Cruise Control 814 // 28.7 Conclusion 815 Questions 816 References 816 // 29 Environmental-Assisted Vehicular Data in Smart Cities 819 Wei Chong, Huonyong Zheng, Jie Wu, Chiu C. Ton, ond Hoibin Ling // 29.1 Location-Related Security
and Privacy Issues in Smart Cities 820 // 29.2 Opportunities of Using Environmental Evidences 822 // 29.3 Challenges of Creating Location Proofs 823 // 29.4 Environmental Evidence-Assisted Vehicular Data Framework 825 // 29.4.1 System Model and Attack Model 825 // 29.4.2 Roadside Unit-Based Environmental Evidence Construction 826 // 29.4.3 Environmental Evidence-Assisted Application Models 827 // 29.4.3.1 Location Claim Verification 827 // 29.4.3.2 Privacy-Preserved Data Collecting 827 // 29.4.3.3 Environmental Index-Based Data Retrieval 829 // 29.4.4 Optimal Placement of Roadside Units 830 // 29.4.4.1 Problem Formulation 831 // 29.4.4.2 Properties 832 // 29.4.4.3 Approximation for the Optimal RSU Placement 834 // 29.4.4.4 Extension: Optimal RSU Placement with Package Loss 836 // 29.4.4.5 Performance Analysis 837 // 29.4.5 Time Synchronization among Roadside Units 839 // 29.5 Conclusion 841 Final Thoughts 841 Questions 842 References 842 // Index 845

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