Smart Cities Foundations, Principles, and Applications
<p><b> Provides the foundations and principles needed for addressing the various challenges of developing smart cities </b> <p> Smart cities are emerging as a priority for research and development across the world. They open up significant oppo...
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| Médium: | E-kniha |
| Jazyk: | angličtina |
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Newark
Wiley
2017
John Wiley & Sons, Incorporated Wiley-Blackwell John Wiley & Sons (US) |
| Vydání: | 1 |
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| ISBN: | 9781119226390, 1119226392, 1119226414, 9781119226413, 9781119226437, 1119226430 |
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- Editors Biographies xxiii List of Contributors xxvii Foreword xxxiii Preface xxxv Acknowledgments xxxvii 1 Cyber&ndash;Physical Systems in Smart Cities &ndash; Mastering Technological, Economic, and Social Challenges 1 Martina Fromhold-Eisebith 1.1 Introduction 1 1.2 Setting the Scene: Demarcating the Smart City and Cyber&ndash;Physical 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 FinalThoughts 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 2.5 Describing Big Data Analytics (BDA) 29 2.6 The Big Trends and Use Cases of Big Data Analytics 31 2.7 The Open Data for Next-Generation Cities 38 2.8 The Big Data Analytics (BDA) Platforms 39 2.9 Big Data Analytics Frameworks and Infrastructure 45 2.10 Summary 51 FinalThoughts 51 References 52 3 Multi-Scale Computing for a Sustainable Built Environment 53 Massimiliano Manfren 3.1 Introduction 53 3.2 Modeling and Computing for Sustainability Transitions 55 3.3 Multi-ScaleModeling and Computing for the Built Environment 66 3.4 Research inModeling and Computing for the Built Environment 70 FinalThoughts 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 CandidateWireless Technologies 101 4.3 PHY and MAC Layer Issues in Cognitive Radio Networks 105 4.4 Frequency Envelope Modulation (FEM) 110 4.5 Conclusion 116 FinalThoughts 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 5.3 Issues and Challenges of Crowd-sensing in Smart Cities 135 5.4 Crowd-sensing Frameworks for Smart City 144 5.5 Conclusion 149 FinalThoughts 149 Questions 150 References 150 6 Wide-AreaMonitoring and Control of Smart Energy Cyber-Physical Systems (CPS) 155 Nilanjan R. Chaudhuri 6.1 Introduction 155 6.2 Challenges and Opportunities 156 6.3 Solutions 159 6.4 Conclusions and Future Direction 173 FinalThoughts 175 Questions 175 References 175 7 Smart Technologies and Vehicle-to-X (V2X) Infrastructures for Smart Mobility Cities 181 Bernard Fong, Lixin Situ, and Alvis C. M. Fong 7.1 Introduction 181 7.2 Data Communications in Smart City Infrastructure 182 7.3 Deployment: An Economic Point of View 186 7.4 Connected Cars 195 7.5 Concluding Remarks 202 FinalThoughts 203 Questions 203 References 204 8 Smart Ecology of Cities: Integrating Development Impacts on EcosystemServices for Land Parcels 209 Marc Morrison, Ravi S. Srinivasan, and Cynnamon Dobbs 8.1 Introduction 209 8.2 Need for Smart Ecology of Cities 212 8.3 Ecosystem Service Modeling (CO2 Sequestration, PM10 Filtration, Drainage) 214 8.4 Methodology 219 8.5 Implementation of Development Impacts in Dynamic-SIM Platform 231 8.6 Discussion (Assumptions, Limitations, and FutureWork) 234 8.7 Conclusion 235 FinalThoughts 236 Questions 236 References 236 9 Data-Driven Modeling, Control, and Tools for Smart Cities 243 Madhur Behl and Rahul Mangharam 9.1 Introduction 243 9.2 RelatedWork 248 9.3 Problem Definition 250 9.4 Data-Driven Demand Response 252 9.5 DR Synthesis with Regression Trees 254 9.6 The Case for Using Regression Trees for Demand Response 259 9.7 DR-Advisor: Toolbox Design 261 9.8 Case Study 263 9.9 Conclusions and OngoingWork 271 References 272 10 Bringing Named Data Networks into Smart Cities 275 Syed Hassan Ahmed, Safdar Hussain Bouk, Dongkyun Kim, and Mahasweta Sarkar 10.1 Introduction 275 10.2 Future Internet Architectures 278 10.3 Named Data Networking (NDN) 282 10.4 NDN-based Application Scenarios for Smart Cities 285 10.5 Future Aspects of NDN in Smart Cities 297 10.6 Conclusion 303 FinalThoughts 304 Questions 304 References 304 11 Human Context Sensing in Smart Cities 311 Juhi Ranjan and KaminWhitehouse 11.1 Introduction 311 11.2 Human Context Types 312 11.3 Sensing Technologies 317 11.4 Conclusion 331 FinalThoughts 332 Questions 332 References 333 12 Smart Cities and the Symbiotic Relationship between Smart Governance and Citizen Engagement 343 Tori Onker 12.1 Smart Governance 344 12.2 Case Study &ndash; Somerville, Massachusetts 348 12.3 Looking Ahead 365 FinalThoughts 368 Questions 370 References 370 13 Smart Economic Development 373 Madhavi Venkatesan 13.1 Introduction 373 13.2 Perception of Resource Value, Market Outcomes, and Price 378 13.3 Conscious Consumption and the Sustainability Foundation of Smart Cities 384 FinalThoughts 388 Questions 388 References 388 14 Managing the Cyber Security Life-Cycle of Smart Cities 391 Mridul S. Barik, Anirban 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 14.6 Discussion 403 14.7 Conclusion 404 Questions 404 References 405 15 Mobility as a Service 409 Christopher Exp&oacute;sito-Izquierdo, Airam Exp&oacute;sito-M&aacute;rquez, and Julio Brito-Santana 15.1 Introduction 409 15.2 Mobility as a Service 413 15.3 Case Studies on Mobility as a Service 427 15.4 Conclusions and Further Research 432 Acknowledgments 433 FinalThoughts 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 Schl&uuml;ter 16.1 Introduction 438 16.2 Method 440 16.3 Application Case 442 16.4 Data Sources and Preprocessing 443 16.5 Clustering 448 16.6 Fuzzy Reasoning 456 16.7 Mixed Fuzzy Reasoning and Clustering 459 16.8 Postprocessing: Interpretation and Strategy Identification 459 16.9 Comparison and Discussion ofMethods 464 16.10 Conclusion 467 FinalThoughts 468 Questions 468 Acknowledgments 469 References 469 17 A Framework to Achieve Large Scale Energy Savings for Building Stocks through Targeted Occupancy Interventions 473 Aslihan Karatas, Allisandra Stoiko, and Carol C. Menassa 17.1 Introduction 474 17.2 Objectives 475 17.3 Review of Occupancy-Focused Energy Efficiency Interventions 476 17.4 Role of Occupants&rsquo; Characteristics in Building Energy Use 481 17.5 A Conceptual Framework for Delivering Targeted Occupancy-Focused Interventions 483 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 Ali Komeily and Ravi Srinivasan 18.1 Introduction 503 18.2 Sustainability Assessment in Our Cities 506 18.3 Sustainability in Smart Cities 508 18.4 Achieving Balanced Sustainability 511 FinalThoughts 526 Questions 527 References 536 19 Toward Resilience of the Electric Grid 541 JiankangWang 19.1 Electric Grids in Smart Cities 541 19.2 Threats to Electric Grids 549 19.3 Electric Grid Response under Threats 558 19.4 Defense against Threats to Electric Grids 564 References 573 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 579 Julian Pra&szlig;, JohannesWeber, Sebastian Staub, Johannes B&uuml;rner, Ralf B&ouml;hm, Thomas Braun, Moritz Hein, MarkusMichl,Michael Beck, and J&ouml;rg Franke 20.1 Generation of Energy 580 20.2 Storage of Energy 585 20.3 Smart Usage of Energy 591 20.4 Summary 604 FinalThoughts 604 Questions 605 References 605 21 Building Cyber-Physical Systems &ndash; A Smart Building Use Case 609 Jupiter Bakakeu, Franziska Sch&auml;fer, Jochen Bauer, MarkusMichl, and J&ouml;rg Franke 21.1 Foundations&mdash;From Automation to Smart Homes 610 21.2 From Today&rsquo;s Technologically Augmented Houses to Tomorrow&rsquo;s Smart Homes 612 21.3 Smart Home: A Cyber-Physical Ecosystem 616 21.4 Connecting Smart Homes and Smart Cities 633 21.5 Conclusion and Future Research Focus 635 FinalThoughts 636 Questions 636 References 637 22 Climate Resilience and the Design of Smart Buildings 645 Saranya Gunasingh, NoraWang, Doug Ahl, and Scott Schuetter 22.1 Climate Change and Future Buildings and Cities 646 22.2 Carbon Inventory and Current Goals 648 22.3 Incorporating Predicted Climate Variability in Building Design 650 22.4 Case Studies 652 22.5 Implications for Future Cities and Net-Zero Buildings 666 FinalThoughts 668 Questions 668 References 669 23 Smart Audio Sensing-Based HVAC Monitoring 673 Shahriar Nirjon, Ravi Srinivasan, and Tamim Sookoor 23.1 Introduction 673 23.2 Background 675 23.3 The Design of SASEM 679 23.4 Experimental Results 689 FinalThoughts 693 Questions 693 References 694 24 Smart Lighting 701 Jie Lian and Charles L. Brown 24.1 Introduction 701 24.2 Background 702 24.3 Smart Lighting Applications 703 24.4 Visible Light Communication (Smart Lighting Communication) System 705 24.5 Conclusion and Outlook 722 FinalThoughts 723 Questions 723 References 723 25 Large Scale Air-Quality Monitoring in Smart and Sustainable Cities 729 Xiaofan Jiang 25.1 Introduction 730 25.2 Current Approaches to Air Quality Monitoring and Their Limitations 733 25.3 Overview of a Cloud-based Air QualityMonitoring System 735 25.4 Cloud-Connected Air QualityMonitors 737 25.5 Cloud-Side System Design and Considerations 740 25.6 Data Analytics in the Cloud 743 25.7 Applications and APIs 752 FinalThoughts 752 Questions 755 References 755 26 The Smart City Production System 759 Gary Graham, Jag Srai, Patrick Hennelly, and Roy Meriton 26.1 Introduction 759 26.2 Types of Production System: Historical Evolution 761 26.3 The Integrated Smart C
- 12.2.2.2 Somerville by Design
- 7.3.4 V2X Network Integration and Interoperability -- 7.4 Connected Cars -- 7.4.1 Multi‐Hop Communication in V2X -- 7.4.2 Green V2X Communications in Smart Cities -- 7.4.3 Vehicular Communications Infrastructure Reliability -- 7.4.4 Business Intelligence in Connected Cars -- 7.5 Concluding Remarks -- Final Thoughts -- Questions -- References -- Chapter 8 Smart Ecology of Cities: Integrating Development Impacts on Ecosystem Services for Land Parcels -- 8.1 Introduction -- 8.2 Need for Smart Ecology of Cities -- 8.3 Ecosystem Service Modeling (CO2 Sequestration, PM10 Filtration, Drainage) -- 8.3.1 Overview of Ecosystem Services in Urban Contexts -- 8.3.2 CO2 Sequestration -- 8.3.3 PM10 Filtration -- 8.3.4 Drainage -- 8.4 Methodology -- 8.4.1 Carbon Sequestration -- 8.4.2 Drainage -- 8.4.3 PM10 Filtration -- 8.5 Implementation of Development Impacts in Dynamic‐SIM Platform -- 8.6 Discussion (Assumptions, Limitations, and Future Work) -- 8.7 Conclusion -- Final Thoughts -- Questions -- References -- Chapter 9 Data‐Driven Modeling, Control, and Tools for Smart Cities -- 9.1 Introduction -- 9.1.1 Contributions -- 9.1.2 Experimental Validation and Evaluation -- 9.2 Related Work -- 9.3 Problem Definition -- 9.3.1 DR Baseline Prediction -- 9.3.2 DR Strategy Evaluation -- 9.3.3 DR Strategy Synthesis -- 9.4 Data‐Driven Demand Response -- 9.4.1 Data Description -- 9.4.2 Data‐Driven DR Baseline -- 9.4.3 Data‐Driven DR Evaluation -- 9.5 DR Synthesis with Regression Trees -- 9.5.1 Model‐Based Control with Regression Trees -- 9.5.2 DR Synthesis Optimization -- 9.6 The Case for Using Regression Trees for Demand Response -- 9.7 DR‐Advisor: Toolbox Design -- 9.8 Case Study -- 9.8.1 Building Description -- 9.8.2 Model Validation -- 9.8.3 Energy Prediction Benchmarking -- 9.8.4 DR Evaluation -- 9.8.5 DR Synthesis -- 9.8.5.1 Revenue from Demand Response
- 9.9 Final Thoughts -- Questions -- References -- Chapter 10 Bringing Named Data Networks into Smart Cities -- 10.1 Introduction -- 10.2 Future Internet Architectures -- 10.2.1 Data‐Oriented Network Architecture (DONA) -- 10.2.2 Network of Information (NetInf) -- 10.2.3 Publish Subscribe Internet Technology (PURSUIT) -- 10.3 Named Data Networking (NDN) -- 10.4 NDN‐based Application Scenarios for Smart Cities -- 10.4.1 NDN in IoT for Smart Cities -- 10.4.2 NDN in Smart Grid for Smart Cities -- 10.4.3 NDN in WSN for Smart Cities -- 10.4.4 NDN in MANETs for Smart Cities -- 10.4.5 NDN in VANETs for Smart Cities -- 10.4.6 NDN in Climate Data Communications -- 10.5 Future Aspects of NDN in Smart Cities -- 10.5.1 NDN Content/Data -- 10.5.2 Naming Content/Data in NDN -- 10.5.3 NDN Data Structures -- 10.5.4 NDN Message Forwarding -- 10.5.5 Content Discovery in NDN -- 10.5.6 NDN in Dynamic Network Topology -- 10.5.7 Content Caching in NDN -- 10.5.8 Security and Privacy -- 10.5.9 Evaluation Methods -- 10.6 Conclusion -- Final Thoughts -- Questions -- References -- Chapter 11 Human Context Sensing in Smart Cities -- 11.1 Introduction -- 11.2 Human Context Types -- 11.2.1 Physiological Context -- 11.2.2 Emotive Context -- 11.2.3 Functional Context -- 11.2.4 Location Context -- 11.3 Sensing Technologies -- 11.3.1 Video and Audio -- 11.3.2 Wearables -- 11.3.3 Smartphones -- 11.3.4 Environment -- 11.4 Conclusion -- Final Thoughts -- Questions -- References -- Chapter 12 Smart Cities and the Symbiotic Relationship between Smart Governance and Citizen Engagement -- 12.1 Smart Governance -- 12.1.1 Smart Governance and Smart Cities -- 12.1.2 The Role of Planning & -- Design -- 12.2 Case Study - Somerville, Massachusetts -- 12.2.1 Slumerville to Somerville -- 12.2.1.1 Professionalizing City Hall -- 12.2.2 Planning Somerville -- 12.2.2.1 SomerVision
- 3.2 Modeling and Computing for Sustainability Transitions -- 3.2.1 Multilevel Perspective Modeling -- 3.2.2 Technological and Social Learning -- 3.2.3 Multidisciplinary System Thinking -- 3.2.4 Long‐Term Thinking for the Built Environment -- 3.2.5 Data and Modeling Techniques -- 3.3 Multi‐Scale Modeling and Computing for the Built Environment -- 3.3.1 Virtual Prototyping for Design Optimization -- 3.3.2 Performance Optimization Across Building Life Cycle Phases -- 3.4 Research in Modeling and Computing for the Built Environment -- 3.4.1 Building Energy Balance Analysis -- 3.4.2 Forward/Inverse Modeling and Visualization Techniques -- 3.4.3 Workflows and Integration of Modeling Strategies -- 3.4.4 Research Advances in Modeling and Computing -- Final Thoughts -- Questions -- References -- Chapter 4 Autonomous Radios and Open Spectrum in Smart Cities -- 4.1 Introduction -- 4.2 Candidate Wireless Technologies -- 4.2.1 Open Spectrum -- 4.2.2 5G Wireless Technologies -- 4.2.3 Internet of Things (IoT) -- 4.3 PHY and MAC Layer Issues in Cognitive Radio Networks -- 4.3.1 Spectrum Sensing -- 4.3.1.1 Detection Methods -- 4.3.1.2 Cooperative Spectrum Sensing -- 4.3.1.3 Other Sensing Issues -- 4.3.2 Spectrum Management and Handoff -- 4.3.3 Rendezvous Problem -- 4.3.4 Coexistence -- 4.4 Frequency Envelope Modulation (FEM) -- 4.4.1 Network Self‐Configuration -- 4.4.2 Physical Layer Performance -- 4.4.3 Experimental Results -- 4.5 Conclusion -- Final Thoughts -- Questions -- References -- Chapter 5 Mobile Crowd‐Sensing for Smart Cities -- 5.1 Introduction -- 5.2 Overview of Mobile Crowd‐Sensing -- 5.2.1 Categories of Crowd‐sensing -- 5.2.2 Architecture of Mobile Crowd‐sensing -- 5.2.3 Applications of Mobile Crowd‐sensing in Smart City -- 5.2.3.1 Applications in Infrastructure -- 5.2.3.2 Environmental Applications -- 5.2.3.3 Social Applications
- 5.3 Issues and Challenges of Crowd‐sensing in Smart Cities -- 5.3.1 Task Assignment Problem -- 5.3.2 User Profiling and Trustworthiness -- 5.3.3 Incentive Mechanisms -- 5.3.4 Localized Analytics -- 5.3.5 Security and Privacy -- 5.4 Crowd‐sensing Frameworks for Smart City -- 5.4.1 Here‐n‐Now Framework -- 5.4.2 Crowd‐sensing Framework based on XMPP -- 5.4.3 McSense -- 5.4.4 Supporting Framework for Crowd‐sensing Apps -- 5.5 Conclusion -- Final Thoughts -- Questions -- References -- Chapter 6 Wide‐Area Monitoring and Control of Smart Energy Cyber‐Physical Systems (CPS) -- 6.1 Introduction -- 6.2 Challenges and Opportunities -- 6.2.1 Wide‐Area Monitoring: Damping, Frequency, and Mode‐shape Estimation -- 6.2.2 Wide‐Area Damping Control: Latency Compensation -- 6.2.3 Wide‐Area Damping Control with Wind Farms -- 6.3 Solutions -- 6.3.1 Phasor Approach -- 6.3.2 Wide‐Area Monitoring: Damping, Frequency, and Mode‐Shape Estimation -- 6.3.3 Test System: 16‐Machine, 5‐Area System -- 6.3.3.1 Simulation Results -- 6.3.4 Wide‐Area Damping Control: Latency Compensation -- 6.3.4.1 Simulation Results -- 6.3.5 Wide‐Area Damping Control with Wind Farms -- 6.3.5.1 DFIG‐based Wind Farm Modeling -- 6.3.5.2 Rotor Side Converter (RSC) Control -- 6.3.5.3 Grid Side Converter (GSC) Control -- 6.3.5.4 Control Input -- 6.3.5.5 Coordinated Control Design -- 6.3.5.6 Simulation Results -- 6.4 Conclusions and Future Direction -- Final Thoughts -- Questions -- References -- Chapter 7 Smart Technologies and Vehicle‐to‐X (V2X) Infrastructures for Smart Mobility Cities -- 7.1 Introduction -- 7.2 Data Communications in Smart City Infrastructure -- 7.2.1 Data Acquisition -- 7.2.2 Traffic Surveillance -- 7.3 Deployment: An Economic Point of View -- 7.3.1 Detecting Abnormal Events -- 7.3.2 Network Failure -- 7.3.3 Micromobility Data Communications
- Cover -- Title Page -- Copyright -- Contents -- Editors Biographies -- List of Contributors -- Foreword -- Preface -- Acknowledgments -- Chapter 1 Cyber-Physical Systems in Smart Cities - Mastering Technological, Economic, and Social Challenges -- 1.1 Introduction -- 1.2 Setting the Scene: Demarcating the Smart City and Cyber-Physical Systems -- 1.3 Process Fields of CPS‐Driven Smart City Development -- 1.4 Economic and Social Challenges of Implementing the CPS‐Enhanced Smart City -- 1.5 Conclusions: Suggestions for Planning the CPS‐Driven Smart City -- Final Thoughts -- Questions -- References -- Chapter 2 Big Data Analytics Processes and Platforms Facilitating Smart Cities -- 2.1 Introduction -- 2.2 Why Big Data Analytics (BDA) Is Significant for Smarter Cities -- 2.3 Describing the Big Data Paradigm -- 2.4 The Prominent Sources of Big Data -- 2.4.1 The Salient Implications of Big Data -- 2.4.2 Information and Communication Infrastructures for Big Data and Its Platforms -- 2.4.3 Transitioning from Big Data to Big Insights -- 2.5 Describing Big Data Analytics (BDA) -- 2.6 The Big Trends and Use Cases of Big Data Analytics -- 2.6.1 Customer Satisfaction Analysis -- 2.6.2 Market Sentiment Analysis -- 2.6.3 Epidemic Analysis -- 2.6.4 Using Big Data Analytics in Healthcare -- 2.6.5 Analytics of Machine Data by Splunk -- 2.7 The Open Data for Next‐Generation Cities -- 2.8 The Big Data Analytics (BDA) Platforms -- 2.8.1 Civitas: The Smart City Middleware -- 2.8.2 Hitachi Smart City Platform -- 2.8.3 Data Collection -- 2.8.4 Data Analysis -- 2.8.5 Application Coordination -- 2.9 Big Data Analytics Frameworks and Infrastructure -- 2.9.1 Apache Hadoop Software Framework -- 2.9.2 NoSQL Databases -- 2.10 Summary -- Final Thoughts -- Questions -- References -- Chapter 3 Multi‐Scale Computing for a Sustainable Built Environment -- 3.1 Introduction