Table of Contents

Cover

Title Page

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
2.5 Describing Big Data Analytics (BDA)
2.6 The Big Trends and Use Cases of Big Data Analytics
2.7 The Open Data for Next-Generation Cities
2.8 The Big Data Analytics (BDA) Platforms
2.9 Big Data Analytics Frameworks and Infrastructure
2.10 Summary
Final Thoughts
Questions
References

Chapter 3: Multi-Scale Computing for a Sustainable Built Environment

3.1 Introduction
3.2 Modeling and Computing for Sustainability Transitions
3.3 Multi-Scale Modeling and Computing for the Built Environment
3.4 Research in Modeling and Computing for the Built Environment
Final Thoughts
Questions
References

Chapter 4: Autonomous Radios and Open Spectrum in Smart Cities

4.1 Introduction
4.2 Candidate Wireless Technologies
4.3 PHY and MAC Layer Issues in Cognitive Radio Networks
4.4 Frequency Envelope Modulation (FEM)
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
5.3 Issues and Challenges of Crowd-sensing in Smart Cities
5.4 Crowd-sensing Frameworks for Smart City
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
6.3 Solutions
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
7.3 Deployment: An Economic Point of View
7.4 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)
8.4 Methodology
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
9.2 Related Work
9.3 Problem Definition
9.4 Data-Driven Demand Response
9.5 DR Synthesis with Regression Trees
9.6 The Case for Using Regression Trees for Demand Response
9.7 DR-Advisor: Toolbox Design
9.8 Case Study
9.9 Final Thoughts
Questions
References

Chapter 10: Bringing Named Data Networks into Smart Cities

10.1 Introduction
10.2 Future Internet Architectures
10.3 Named Data Networking (NDN)
10.4 NDN-based Application Scenarios for Smart Cities
10.5 Future Aspects of NDN in Smart Cities
10.6 Conclusion
Final Thoughts
Questions
References

Chapter 11: Human Context Sensing in Smart Cities

11.1 Introduction
11.2 Human Context Types
11.3 Sensing Technologies
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
12.2 Case Study – Somerville, Massachusetts
12.3 Looking Ahead
Final Thoughts
Questions
References

Chapter 13: Smart Economic Development

13.1 Introduction
13.2 Perception of Resource Value, Market Outcomes, and Price
13.3 Conscious Consumption and the Sustainability Foundation of Smart Cities
Final Thoughts
Questions
References

Chapter 14: Managing the Cyber Security Life-Cycle of Smart Cities

14.1 Introduction
14.2 Smart City Services
14.3 Smart Services Technologies
14.4 Smart Services Security Issues
14.5 Management of Cyber Security of Smart Cities
14.6 Discussion
14.7 Conclusion
Questions
References

Chapter 15: Mobility as a Service

15.1 Introduction
15.2 Mobility as a Service
15.3 Case Studies on Mobility as a Service
15.4 Conclusions and Further Research
Acknowledgments
Final Thoughts
Questions
References

Chapter 16: Clustering and Fuzzy Reasoning as Data Mining Methods for the Development of Retrofit Strategies for Building Stocks

16.1 Introduction
16.2 Method
16.3 Application Case
16.4 Data Sources and Preprocessing
16.5 Clustering
16.6 Fuzzy Reasoning
16.7 Mixed Fuzzy Reasoning and Clustering
16.8 Postprocessing: Interpretation and Strategy Identification
16.9 Comparison and Discussion of Methods
16.10 Conclusion
Final Thoughts
Questions
Acknowledgments
References

Chapter 17: A Framework to Achieve Large Scale Energy Savings for Building Stocks through Targeted Occupancy Interventions

17.1 Introduction
17.2 Objectives
17.3 Review of Occupancy-Focused Energy Efficiency Interventions
17.4 Role of Occupants' Characteristics in Building Energy Use
17.5 A Conceptual Framework for Delivering Targeted Occupancy-Focused Interventions
17.6 Case Study Example
17.7 Discussion
17.8 Conclusions and Policy Implications
Questions
Acknowledgment
References

Chapter 18: Sustainability in Smart Cities: Balancing Social, Economic, Environmental, and Institutional Aspects of Urban Life

18.1 Introduction
18.2 Sustainability Assessment in Our Cities
18.3 Sustainability in Smart Cities
18.4 Achieving Balanced Sustainability
Final Thoughts
Questions
Appendix 1
References

Chapter 19: Toward Resilience of the Electric Grid

19.1 Electric Grids in Smart Cities
19.2 Threats to Electric Grids
19.3 Electric Grid Response under Threats
19.4 Defense against Threats to Electric Grids
Final Thoughts
Questions
References

Chapter 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

20.1 Introduction
20.2 Generation of Energy
20.3 Storage of Energy
20.5 Summary
Final Thoughts
Questions
References

Chapter 21: Building Cyber-Physical Systems – A Smart Building Use Case

21.1 Foundations – From Automation to Smart Homes
21.2 From Today's Technologically Augmented Houses to Tomorrow's Smart Homes
21.3 Smart Home: A Cyber-Physical Ecosystem
21.4 Connecting Smart Homes and Smart Cities
21.5 Conclusion and Future Research Focus
Final Thoughts
Questions
References

Chapter 22: Climate Resilience and the Design of Smart Buildings

22.1 Climate Change and Future Buildings and Cities
22.2 Carbon Inventory and Current Goals
22.3 Incorporating Predicted Climate Variability in Building Design
22.4 Case Studies
22.5 Implications for Future Cities and Net-Zero Buildings
Final Thoughts
Questions
References

Chapter 23: Smart Audio Sensing-Based HVAC Monitoring

23.1 Introduction
23.2 Background
23.3 The Design of SASEM
23.4 Experimental Results
Final Thoughts
Questions
Acknowledgement
References

Chapter 24: Smart Lighting

24.1 Introduction
24.2 Background
24.3 Smart Lighting Applications
24.4 Visible Light Communication (Smart Lighting Communication) System
24.5 Conclusion and Outlook
Final Thoughts
Questions
References

Chapter 25: Large Scale Air-Quality Monitoring in Smart and Sustainable Cities

25.1 Introduction
25.2 Current Approaches to Air Quality Monitoring and Their Limitations
25.3 Overview of a Cloud-based Air Quality Monitoring System
25.4 Cloud-Connected Air Quality Monitors
25.5 Cloud-Side System Design and Considerations
25.6 Data Analytics in the Cloud
25.7 Applications and APIs
Final Thoughts
Questions
References

Chapter 26: The Smart City Production System

26.1 Introduction
26.2 Types of Production System: Historical Evolution
26.3 The Integrated Smart City Production System Framework
26.4 Production System Design
26.5 Chapter Summary
Final Thoughts
Questions
References

Chapter 27: Smart Health Monitoring Using Smart Systems

27.1 Introduction
27.2 Background
27.3 Integration for Monitoring Applications
27.4 Conclusion
Final Thoughts
Questions
References

Chapter 28: Significance of Automated Driving in Japan

28.1 Introduction
28.2 Definitions of Automated Driving Systems
28.3 A History of Research and Development of Automated Driving Systems
28.4 Expected Benefits of Automated Driving
28.5 Issues of Automated Driving for Market Introduction
28.6 Possible Market Introduction of Automated Driving Systems in Japan
28.7 Conclusion
Questions
References

Chapter 29: Environmental-Assisted Vehicular Data in Smart Cities

29.1 Location-Related Security and Privacy Issues in Smart Cities
29.2 Opportunities of Using Environmental Evidences
29.3 Challenges of Creating Location Proofs
29.4 Environmental Evidence-Assisted Vehicular Data Framework
29.5 Conclusion
Final Thoughts
Questions
References

Index

End User License Agreement

List of Illustrations

Chapter 2: Big Data Analytics Processes and Platforms Facilitating Smart Cities

Figure 2.1 The Splunk reference architecture for machine data analytics.
Figure 2.2 Big data analytics platforms, appliances, products, and tools.
Figure 2.3 The reference architecture of Civitas platform.

Chapter 3: Multi-Scale Computing for a Sustainable Built Environment

Figure 3.1 Synthesis of the state of the art of building energy models.
Figure 3.2 Forward and inverse building energy models.
Figure 3.3 Traditional design and rapid virtual prototyping.
Figure 3.4 Workflows considering the state-of-the-art of models.
Figure 3.5 Separated workflows for different types of models.
Figure 3.6 Advanced workflows enabled by model integration.

Chapter 4: Autonomous Radios and Open Spectrum in Smart Cities

Figure 4.1 Past and projected future wireless data rates in the United States.
Figure 4.2 Comparison of regulatory schemes for allocating spectrum. (a) Fixed allocation regime. (b) Open spectrum regime. Note that while some safety-critical bands might require fixed allocation, most of the spectrum will be open.
Figure 4.3 Elements of a 5G HetNet. Note that there will exist multiple types of not only base stations (macrocell BSs and femtocells) but also mobile nodes (high-capability UEs, such as a smartphone or tablet, and low-capacity devices, such as sensors) vying for scarce spectrum.
Figure 4.4 Venn diagram showing the overlap of IoT technologies in their applicability to smart cities.
Figure 4.5 Comparison of cognitive radio spectrum use paradigms. (a) Interweave paradigm, wherein SUs use unoccupied spectrum. (b) Overlay/underlay paradigms, wherein SUs share the same regions of spectrum with the PU.
Figure 4.6 Illustration of the need for and advantage of the spatial diversity provided by cooperative spectrum sensing. Note that SU1 has an unobstructed view of the PU, SU2 has a partially obstructed view, and SU3 has a fully obstructed view.
Figure 4.7 Illustration of the rendezvous problem in cognitive radio. Two nodes attempting to discover each other can only rendezvous and exchange control information when the transmitter and receiver communicate in the same time–frequency slot (here they are not).
Figure 4.8 Example network topologies that can be used with FEM. (a) “Normal” full-duplex configuration. (b) Multihop configuration. (c) Multicast configuration. (d) Multihop to multicast configuration.
Figure 4.9 Crenel filter with $c04-math-002$ . The crenel pattern represented is “001011.”
Figure 4.10 Example spectrogram of a FEM organization period with four nodes and four channels where each channel has a bandwidth of 100 kHz. Note that for each round the crenel patterns change as nodes gain new sensing information.
Figure 4.11 Average network configuration time in rounds as a function of the number of nodes, with the number of channels equaling the number of nodes for each data point. The number of nodes was varied from 4 to 100 in steps of 4; 2000 iterations were averaged for each data point in the figure.
Figure 4.12 Time-domain BER curves of FEM-BPSK as a function of crenel height $c04-math-006$ with $c04-math-007$ (4 of which are training crenels), $c04-math-008$ , and $c04-math-009$ . (a) Without crenel equalization. (b) With crenel equalization. The $c04-math-010$ curve corresponds to unimpaired BPSK. Note that in general the equalized version performs better, but for $c04-math-011$ the impairment added by FEM is negligible even without equalization.
Figure 4.13 FEM crenel detection BER as a function of crenel depth and symbol rate ratio with $c04-math-015$ and $c04-math-016$ . (a) $c04-math-017$ dB. (b) $c04-math-018$ dB. It can be seen that for acceptable error performance, the crenel rate must be approximately 1/1000th of the time-domain symbol rate.
Figure 4.14 Spectrograms for an example FEM organization experiment run on the ORBIT testbed. (a) FEM organization spectrogram. (b) Changing crenel pattern for a single channel (zoomed in).

Chapter 5: Mobile Crowd-Sensing for Smart Cities

Figure 5.1 Classification of crowd-sensing.
Figure 5.2 Architecture of crowd-sensing.
Figure 5.3 Workflow of mobile crowd-sensing.
Figure 5.4 Categories of applications of MCS in smart cities.
Figure 5.5 Comparison of mobile crowd-sensing applications.
Figure 5.6 Role of mobile crowd-sensing in smart city context.
Figure 5.7 Lifecycle of crowd-sensing tasks according to [39].
Figure 5.8 Categorization of incentive mechanisms for mobile crowd-sensing applications.
Figure 5.9 Major issues of crowd-sensing in smart cities and relationship between them.
Figure 5.10 Design of Here- $c05-math-009$ -Now framework.
Figure 5.11 Design of XMPP-based mobile crowd-sensing framework.
Figure 5.12 Design of McSense - a mobile crowd-sensing platform for smart cities.
Figure 5.13 Comparison of frameworks proposed in literature.

Chapter 6: Wide-Area Monitoring and Control of Smart Energy Cyber-Physical Systems (CPS)

Figure 6.1 Extraction of oscillatory components in phasor approach.
Figure 6.2 Test system: 16-machine, 5-area system with a TCSC.
Figure 6.3 Effect of SNR on estimated damping, frequency, and relative Mode shape for mode # 1 after a three-phase fault at $c06-math-053$ near bus 54 followed by line 54–53 outage. Box plots show the lower, median, and upper quartiles and extent of the rest of the data and any outliers .
Figure 6.4 Oscilloscope traces of measured signals and corresponding damping and mode-shape estimation with phasor approach for line 54–53 outage .
Figure 6.5 Operating principle of an adaptive phasor POD (APPOD).
Figure 6.6 System behavior with tie-line 54–53 outage.
Figure 6.7 Oscilloscope recordings from real-time simulation for the case study shown in Figure 6.6.
Figure 6.8 (a) DFigure overall structure (b) $c06-math-080$ – $c06-math-081$ : reference frame used for power system modeling, $c06-math-082$ – $c06-math-083$ : modified reference frame for vector control (c) rotor converter control structure.
Figure 6.9 Dynamic performance of the system (see Figure 6.2) following a self-clearing three-phase fault near bus 60 for 80 ms. Gray trace: SGs G9 and G15. Dark gray trace: DFigure G9 and G15 without PSSs. Black trace: DFigure G9 and G15 with PSSs [3].

Chapter 7: Smart Technologies and Vehicle-to-X (V2X) Infrastructures for Smart Mobility Cities

Figure 7.1 Intelligent transport system block diagram.
Figure 7.2 Map showing motorists' approximate travel times.
Figure 7.3 Network failure analysis.
Figure 7.4 Micromobility vehicle. (a) Personalized flexibility. (b) Assistive disability access.
Figure 7.5 Micromobility management network through fusion prognostic operation.
Figure 7.6 Vehicle sharing for enhanced resource utilization efficiency. (a) Electric bicycle service location. (b) Automated booking system.
Figure 7.7 Personal mobility vehicles.
Figure 7.8 Data coverage extension with multi-hop V2X.
Figure 7.9 Multi-hop network management.
Figure 7.10 Angular similarity performance evaluation of prognostics and network health management.

Chapter 8: Smart Ecology of Cities: Integrating Development Impacts on Ecosystem Services for Land Parcels

Figure 8.1 FNAI CO-OP land cover.
Figure 8.2 Capstone land cover and parcel dataset.
Figure 8.3 NumPy raster calculation with different sized arrays.
Figure 8.4 Alachua County carbon sequestration.
Figure 8.5 Raster and polygon soil data.
Figure 8.6 Zonal statistics result.
Figure 8.7 Chart detailing CN designation for agricultural land.
Figure 8.8 Dynamic-SIM platform.

Chapter 9: Data-Driven Modeling, Control, and Tools for Smart Cities

Figure 9.1 Majority of DR today is manual and rule based. (a) The fixed rule based DR is inconsistent and could under perform compared with the required curtailment, resulting in DR penalties. (b) Using data-driven models, DR-Advisor uses DR strategy evaluation and DR strategy synthesis for a sustained and sufficient curtailment.
Figure 9.2 DR-Advisor architecture.
Figure 9.3 Example of a demand response timeline.
Figure 9.4 Example of a regression tree with linear regression model in leaves. Not suitable for control due to the mixed order of the controllable $c09-math-046$ (solid blue) and uncontrollable $c09-math-047$ features.
Figure 9.5 Example of a tree structure obtained using the mbCRT algorithm. The separation of variables allows using the linear model in the leaf to use only control variables.
Figure 9.6 DR synthesis with thermal comfort constraints. Each tree is responsible for contributing one constraint to the demand response optimization.
Figure 9.7 Linear model assumption at the leaves. (a) The comparison between fitted values and ground truth values of power consumption for one of the leaves in the power consumption prediction tree. (b) The residual error between fitted and actual power consumption values for all the leaf nodes of the tree.
Figure 9.8 Screenshot of the DR-Advisor MATLAB-Based GUI.
Figure 9.9 DR-Advisor workflow.
Figure 9.10 DR-Advisor model identification tab.
Figure 9.11 Eight different buildings on the Penn campus were modeled with DR-Advisor.
Figure 9.12 Model validation for the clinical research building at Penn.
Figure 9.13 Rule-based strategies used in DR evaluation. CHSTP denotes chiller set point and CLGSTP denotes zone cooling temperature set point.
Figure 9.14 Prediction of power consumption for three strategies. DR evaluation shows that Strategy 1 (S1) leads to maximum power curtailment.
Figure 9.15 DR synthesis using the mbCRT algorithm for July 17, 2013. A curtailment of $c09-math-081$ is sustained during the DR event period.
Figure 9.16 Optimal DR strategy as determined by the mbCRT algorithm.
Figure 9.17 The mbCRT algorithm maintains the zone temperatures within the specified comfort bounds during the DR event.
Figure 9.18 Zoomed-in view of the DR synthesis showing how the mbCRT algorithm selects the appropriate linear model for each time step based on the forecast of the disturbances.

Chapter 10: Bringing Named Data Networks into Smart Cities

Figure 10.1 Data-oriented network architecture (DONA): overview.
Figure 10.2 Network of Information (NetInf) overview.
Figure 10.3 PURSUIT overview.
Figure 10.4 Basic operational perspectives of CCN versus NDN. (a) Content-Centric Networking (CCN). (b) Named Data Networking (NDN).
Figure 10.5 NDN potentials and applications.
Figure 10.6 Components of the smart grid.
Figure 10.7 Generic overview of WSN deployment.
Figure 10.8 Different applications and services supported by MANETs.
Figure 10.9 Application overview of VANETs.

Chapter 11: Human Context Sensing in Smart Cities

Figure 11.1 Functional human context such as a person's gait (a) or physiological context such as jaundice indications from skin color in babies (b) is sensed through video-based technologies [41, 42].
Figure 11.2 Wearable sensing technologies: (a) Detecting back posture using sensors embedded in a smart shirt. (b) Determining physical activities and respiratory pattern using device clipped to clothes. (c) First person views using wearable camera.
Figure 11.3 A variety of sensors embedded in the environment can sense the functional context of a child's eating habits (a) or physiological context such as heart rate and respiratory rate when a person is sitting (b).

Chapter 12: Smart Cities and the Symbiotic Relationship between Smart Governance and Citizen Engagement

Figure 12.1 Union Square Prospect Street, no build condition.
Figure 12.2 Union Square, Prospect Street, proposed.
Figure 12.3 Gilman Square.
Figure 12.4 Gilman Square, rendering of new public square abutting the proposed Green Line MBTA station.
Figure 12.5 Davis Square, Cutter Plaza existing conditions.
Figure 12.6 Davis Square, Cutter Plaza pop-up plaza 1.
Figure 12.7 Davis Square, Cutter Plaza pop-up plaza 2.
Figure 12.8 Davis Square Cutter Plaza image build out.
Figure 12.9 Union Square, walking tour.
Figure 12.10 Union Square, Prospect Street in January.
Figure 12.11 Union Square, out in the cold.

Chapter 14: Managing the Cyber Security Life-Cycle of Smart Cities

Figure 14.1 Smart city cyber security life cycle.

Chapter 15: Mobility as a Service

Figure 15.1 Example of MaaS service package.

Chapter 16: Clustering and Fuzzy Reasoning as Data Mining Methods for the Development of Retrofit Strategies for Building Stocks

Figure 16.1 Conventional type–age classification for deriving groups for building stock management.
Figure 16.2 Data mining for strategic building retrofit.
Figure 16.3 Principle of clustering according to the reaction to EEM.
Figure 16.4 Example dendrogram for hierarchical clustering.
Figure 16.5 Typical result of applying clustering to feature space based on cost efficiency in terms of reduction in g CO₂ eq. per invested Swiss Franc.
Figure 16.6 One selected scatterplot from the matrix in Figure 16.5 that shows the instances and their allocations to clusters in detail.
Figure 16.7 Spatial clustering identifies geographically close buildings with complementary characteristics, such as buildings with residual heat and those with more heat demand.
Figure 16.8 Results of one-step clustering experiment for spatial aspects (building locations) and nonspatial aspects (CO₂ emissions and age of the heating system) based on Zernez data. Panels (a), (c), and (d) show an equally weighted clustering (Clustering 1). Panels (e) and (f) show results with spatial aspects weighted three times higher than nonspatial ones (Clustering 2). Panel (b) shows the applied compression (blue dots) in relation to the original position (gray dots).
Figure 16.9 Two-step clustering for the identification of energy networks.
Figure 16.10 Ramp-shaped membership functions for fuzzy reasoning.
Figure 16.11 Membership functions for retrofitting strategies in Zernez.
Figure 16.12 Groups of buildings identified using a spatialized fuzzy reasoning approach.
Figure 16.13 Mixed identification procedure using fuzzy reasoning to filter the building stock, followed by hierarchical agglomerative clustering for spatial group identification. (a) Building candidates identified by fuzzy reasoning. (b) Network candidates identified by hierarchical agglomerative clustering.
Figure 16.14 Heat maps for the case study: (a) heating energy consumption, (b) association with different clusters of response to retrofit measures.
Figure 16.15 Transformation strategies for the Zernez case study.

Chapter 17: A Framework to Achieve Large Scale Energy Savings for Building Stocks through Targeted Occupancy Interventions

Figure 17.1 Building energy use intervention strategies.
Figure 17.2 Framework for designing effective energy policy tools.
Figure 17.3 MOA levels of occupants.
Figure 17.4 Occupants' energy use characteristics: (a) before intervention, (b) after intervention.
Figure 17.5 Criteria for evaluating intervention strategies.

Chapter 18: Sustainability in Smart Cities: Balancing Social, Economic, Environmental, and Institutional Aspects of Urban Life

Figure 18.1 The commonly used depiction of sustainable development based on the three pillars of sustainability.
Figure 18.2 The proposed graphic for all dimensions of sustainable development, which includes social, economic, environmental, and institutional.
Figure 18.3 Multiple levels of sustainability assessment in built environment.
Figure 18.4 Different aspects of balanced assessment of sustainability.
Figure 18.5 The result of word cloud on 60 definitions of smart cities collected from a diverse set of publications including academia, corporate, and government perspectives.
Figure 18.6 Typical dimensions of smart cities.
Figure 18.7 Smart cities dimensions versus sustainability dimensions.
Figure 18.8 x-Axis represents $c018-math-006$ , and y-axis represents the density. Green solid plots relate to KDE of Miami. Red dotted plot relates to KDE of Orlando and Tampa.
Figure 18.9 x-Axis represents $c018-math-007$ , and y-axis represents the density. For both cities, green plot relates to overall KDE, blue plot relates to KDE of blocks located in bin 1, red plot relates to KDE of block located in bin 2, and yellow plot relates to KDE of block located in bin 3.
Figure 18.10 Iterative process of context identification over time.
Figure 18.11 Result of k-means clustering, with three clusters on more than 160,000 parcels located in Gainesville, FL.

Chapter 19: Toward Resilience of the Electric Grid

Figure 19.1 Vertical structure of power systems.
Figure 19.2 Intrusion points on the monitoring layer of power grids.
Figure 19.3 Four classes of cyber attacks on power systems. Control–monitoring attack is illustrated by (b). General control attack is illustrated also by the same diagram of (b) if taken out the shaded area. (a) Monitoring attack. (b) Control–monitoring (CM) attack. (c) Monitoring–control (MC) attack.

Chapter 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

Figure 20.1 Office building with roof-mounted vertical axis wind turbines [3].
Figure 20.2 Two possible setups for linking an ORC plant with a heating system.
Figure 20.3 Qualitative chart of the heat and electricity demand of a single house during one day.
Figure 20.4 Schematic representation of the cross-linked system components and their communication paths.
Figure 20.5 Comparison of standard and modified charging of thermal storages.
Figure 20.6 Structure of an agent-based energy grid.
Figure 20.7 Energy consumption per household [21]. Source: Bumann (2015) [21]. Reproduced with permission of Energieverbrauch im haushalt.
Figure 20.8 Schematic representation of the process $c020-math-006$ .
Figure 20.9 Different specimens coated with an additive structured conductor.
Figure 20.10 Various applications for additive manufactured heating structures.
Figure 20.11 Classification of smart heating control systems.
Figure 20.12 Performance of smart home heating systems with intelligent individual room control – field study results.
Figure 20.13 Schematic of the wall-mounted friction ventilator.
Figure 20.14 Operating principle of the friction ventilator [44].
Figure 20.15 Schematic of the test bench used to investigate the friction ventilator [44].

Chapter 21: Building Cyber-Physical Systems – A Smart Building Use Case

Figure 21.1 Classification of the smart home notion.
Figure 21.2 Adapted version of Glasberg's smart home representation [34] including fields of application, functional possibilities, and certain components.
Figure 21.3 For the development of our architecture, we started with two comfort and smart energy use cases resulting in a conflict use case.
Figure 21.4 Interoperability level in smart home and smart building environment.
Figure 21.5 Semantic agent framework and the role of agents in CPS.
Figure 21.6 Overview of the implemented smart home environment. The living room is equipped with smart sensors (temperature, light, air) and smart actuators (window, light, thermostats, ventilators) that communicate and negotiate their actions in order to efficiently support the individual resident's needs.
Figure 21.7 Excerpt of the implemented information model.
Figure 21.8 UML sequence diagram of the interaction between a thermostat (Actor 1 in room 1) and surrounding smart sensors (Sensor 1a and 1b in room 1 and sensor 2 in room 2). After discovering the devices in the neighborhood, the thermostat selects the sensors of interest based on the location and provided services. Afterward, the thermostat issues a negotiation to cooperate with the sensor. If the sensor agrees to the cooperation, the thermostat opens a secure session and subscribes to the smart sensor's events. The smart actor selects and performs its actions based on the sensor events received.
Figure 21.9 Representation of a smart home as a service platform on the Internet of Things (IoT). External web services are called through available APIs. The communication between the sensors and actuators of different vendors occurs by using middleware. Furthermore, the service platform offers a human–machine interface (HMI).

Chapter 22: Climate Resilience and the Design of Smart Buildings

Figure 22.1 Model calibration process.
Figure 22.2 Average seasonal temperature differences from NARCCAP climate models for Mississippi region.
Figure 22.3 Low and high impact climate data.
Figure 22.4 Monthly energy use – uncalibrated versus calibrated models.
Figure 22.5 Expected range of climate change impacts on energy use and energy cost.
Figure 22.6 Expected change in building performance for each climate scenario for the Chicago multifamily building. Proposed includes 12 possible energy conservation measure upgrades over baseline code.
Figure 22.7 Modeled 2050 average monthly air temperature change from current climate.

Chapter 23: Smart Audio Sensing-Based HVAC Monitoring

Figure 23.1 Overview of the proposed research showing the interactions among the three specific aims of this project.
Figure 23.2 Sensing platform consisting of a USB condenser microphone, a BeagleBone Black SoC, and USB wireless accessory.
Figure 23.3 Acoustic event detection pipeline.
Figure 23.4 Distributed and ensemble approach toward detection of faulty components in HVAC systems.
Figure 23.5 Full life cycle of the proposed integrated platform for predictive maintenance.
Figure 23.6 AHU rooms: (a) Harn Museum, (b) Phillips Center, and (c) Southwest Recreation Center.
Figure 23.7 Deployment of the smartphone sensor network in three air handling unit rooms.
Figure 23.8 The similarity between transition matrices computed on two different time spans are noteworthy. This phenomenon provides an empirical validity of our acoustic modeling strategy. (a) Transition matrix 1. (b) Transition matrix 2.

Chapter 24: Smart Lighting

Figure 24.1 Outdoor smart lighting infrastructure for data-driven applications.
Figure 24.2 Illuminations of smart lighting in an outdoor configuration between several vehicles.
Figure 24.3 Indoor positioning using smart lighting technique.
Figure 24.4 The structure of the multiple-LED array lamp: (a) side view and (b) bottom view.
Figure 24.5 Multi-detector model structure, (a) four-detector model, top and side view, (b) seven-detector model, top and side view (similar to [28]).
Figure 24.6 Basic indoor VLC channel model.
Figure 24.7 Light rays classification.
Figure 24.8 LOS light rays model.
Figure 24.9 Normalized impulse response of multi-LED lamp model with semiangle $c024-math-076$ at (1.25, 0.625, 0).
Figure 24.10 Configuration diagram of a typical VLC MISO system.
Figure 24.11 Block diagram of $c024-math-106$ -PAM schemes.
Figure 24.12 4-PAM modulation.
Figure 24.13 Time waveforms for PPM and OOK.
Figure 24.14 A TDMA stream divided into different time slots for different users.
Figure 24.15 The shape of an 18-element angle diversity transmitter.
Figure 24.16 OOC $c024-math-127$ with length of 7.
Figure 24.17 Block diagram of OFDMA.
Figure 24.18 Circular cell for indoor VLC systems.
Figure 24.19 Multicell configuration in VLC systems.
Figure 24.20 Block diagram of the proposed PAJO algorithm to support $c024-math-143$ users simultaneously.
Figure 24.21 WD-PAJO principles: (a) WD-PAJO geometry structure with multiple users and (b) covered area classification.
Figure 24.22 Nonlinear transfer function of a LED.
Figure 24.23 Illumination distribution comparison of (a) data transmission case and (b) no data transmission case. The small dots represent the 4 virtual users and 16 real users, with $c024-math-168$ tolerance, and the 25-LED model.

Chapter 25: Large Scale Air-Quality Monitoring in Smart and Sustainable Cities

Figure 25.1 Air quality in Delhi, India, is ranked the worst in the world by BusinessInsider using information from WHO (a). Beijing, China, has also gained worldwide attention recently for its dangerous levels of PM2.5 (b).
Figure 25.2 Air Quality Index published by EPA for PM2.5 [6].
Figure 25.3 WHO air quality guidelines and interim targets for particulate matter: 24-h concentrations.
Figure 25.4 (a) Thermo Scientific 1405-DF TEOM Continuous Dichotomous Ambient Air Monitor; (b) Dylos DC1100 Air Quality Monitor.
Figure 25.5 Architecture of AirCloud system.
Figure 25.6 Cloud-connected air quality monitors – AQM and miniAQM.
Figure 25.7 Communication between monitors and cloud.
Figure 25.8 (A) Pollution source and air purifier are used to change PM2.5 concentrations, while air conditioning is used to change the temperature and humidity, we run sensors across a wide range to calibrate. (B) Hardware calibration process: (a) the hardware calibration procedure; (b) standard sensor board; (c) fitting results.
Figure 25.9 Data exchange framework.
Figure 25.10 Data representations (a) and ID definitions (b).
Figure 25.11 Air quality analytics engine.
Figure 25.12 (a) The original signal of PPD42NJ during about 5 days where the signal is sampled every 5 min. (b) Thermo versus PPD42NJ. A large deviation exists between PPD42NJ and Thermo readings, which we use as ground truth.
Figure 25.13 Neural network
Figure 25.14 AQM PM2.5 in time domain. (a) Raw data; (b) reconstructed data; (c) calibrated by ANN; (d) calibrated by GP inference model.
Figure 25.15 Confusion matrix of the deployment dataset. (a) Raw data; (b) reconstructed data; (c) calibrated by ANN; (d) calibrated by GP inference model.
Figure 25.16 Overall effectiveness of analytics engine and spatial inference heatmap. (a) Overall prediction results of each step in the analytics engine; (b) heatmap generated using spatial inference.
Figure 25.17 Web applications built on top of the AirCloud platform. (a) Visualization web app to view time-series data. (b) Trip planning web app – select the healthiest route.
Figure 25.18 (a) and (b) are iOS games, while (c) is an air quality-aware aerial drone, all using AirCloud APIs and by third-party developers. (d) AQM phone app used to connect to the miniAQM device; (e) MyAir app used to show PM2.5 information in detail. (a) AirPet; (b) AirFace; (c) aerial drone; (d) AQM; (e) MyAir.

Chapter 26: The Smart City Production System

Figure 26.1 A categorization of the different types of production system.
Figure 26.2 The integrated smart city production system framework.

Chapter 27: Smart Health Monitoring Using Smart Systems

Figure 27.1 Advanced metering infrastructure.
Figure 27.2 Information obtained by increasing interval reading.
Figure 27.3 Metering architectures of a conventional energy meter and a smart meter.
Figure 27.4 Forty-eight individual readings showing a single 24-h period.
Figure 27.5 MMSE graph.
Figure 27.6 Data applications for health monitoring.
Figure 27.7 Energy usage over a 1-year period between the hours of 1:30 a.m. and 4:00 a.m.

Chapter 28: Significance of Automated Driving in Japan

Figure 28.1 The automated driving system with inductive cable: (a) the vehicle and (b) the inductive cable and onboard sensors at the front bumper.
Figure 28.2 Intelligent vehicle.
Figure 28.3 A merging scene of the cooperative driving system.
Figure 28.4 An automated transit bus by California PATH.
Figure 28.5 IMTS bus driving along a dedicated lane on a theme park.
Figure 28.6 An automated platoon of trucks within “Energy ITS.”
Figure 28.7 Fuel-saving improvement by platooning.
Figure 28.8 The estimated future population of Japan.
Figure 28.9 The estimated future rate of elderly people.
Figure 28.10 Rates of population in urban and rural areas.
Figure 28.11 Examples of small, low-speed automated vehicles: ParkShuttle in the Netherlands (late 1990s) (a) and CyberCars (2011) (b).
Figure 28.12 An automated vehicle for the transportation means in a sparsely populated city (a) and its test drive on a public roadway (b).
Figure 28.13 Examples of a virtual big vehicle composed of four small vehicles. Image Courtesy of Prof. Manabu Omae of Keio University.
Figure 28.14 Evaluation of CACC by truck drivers in Energy ITS survey (information service means that situations ahead of a lead truck are transmitted to drivers on following trucks via vehicle-to-vehicle communications).

Chapter 29: Environmental-Assisted Vehicular Data in Smart Cities

Figure 29.1 Environmental evidence-based data collecting process.
Figure 29.2 The generation of target environmental index.
Figure 29.3 Distinguish six vehicle flows in Table 29.1 by RSUs. The black vertices represent intersections, edges indicate road stretches, and the gray boxes give the potential places where an RSU can be deployed.
Figure 29.4 Distinguish four vehicle flows by roadside stations.
Figure 29.5 Securely distinguishable rate (SDR).
Figure 29.6 Alternate deploying rate (ADR).
Figure 29.7 Clock synchronization problem among RSU stations. All four roadside stations are not synchronized. The system can never tell whether vehicle A or B passed the shadowed region first.

List of Tables

Chapter 1: Cyber–Physical Systems in Smart Cities – Mastering Technological, Economic, and Social Challenges

Table 1.1 Smart City Process Fields, CPS Applications, and Sustainability Effects
Table 1.2 Importance of Economic Context at Different Spatial Scales and Social Acceptance for Smart City Process Fields

Chapter 3: Multi-Scale Computing for a Sustainable Built Environment

Table 3.1 Description of the PDCA (Deming) cycle
Table 3.2 Role of stakeholders with respect to sustainability assessment
Table 3.3 Use of information in modeling and computing for the built environment in different processes at different scales
Table 3.4 Features of different types of models
Table 3.5 Automation versus autonomation in the built environment
Table 3.6 Features of advanced modeling and computing tools for the built environment
Table 3.7 Reference building dataset
Table 3.8 Issues for multi-scale spatial data aggregation
Table 3.9 Heat and mass balance modeling in buildings
Table 3.10 Data acquired by automation systems and sensors
Table 3.11 Examples of software to simulate, optimize, and control multiple dynamics from building to city scale
Table 3.12 Numerical techniques for multiple applications in the built environment

Chapter 6: Wide-Area Monitoring and Control of Smart Energy Cyber-Physical Systems (CPS)

Table 6.1 Modal controllability of DFIG rotor currents [3]
Table 6.2 Modal observability of local and remote power flow signals [3]

Chapter 7: Smart Technologies and Vehicle-to-X (V2X) Infrastructures for Smart Mobility Cities

Table 7.1 Mainstream EV charging specifications (220 V market)

Chapter 9: Data-Driven Modeling, Control, and Tools for Smart Cities

Table 9.1 Model validation with Penn data
Table 9.2 ASHRAE energy prediction competition results

Chapter 12: Smart Cities and the Symbiotic Relationship between Smart Governance and Citizen Engagement

Table 12.1 Recommendations table

Chapter 16: Clustering and Fuzzy Reasoning as Data Mining Methods for the Development of Retrofit Strategies for Building Stocks

Table 16.1 Developed strategies with average cost efficiency per cluster

Chapter 17: A Framework to Achieve Large Scale Energy Savings for Building Stocks through Targeted Occupancy Interventions

Table 17.1 Metrics for measuring occupancy motivation level for energy conservation
Table 17.2 Metrics for measuring occupancy opportunity level for energy conservation
Table 17.3 Metrics for measuring occupancy ability level for energy conservation
Table 17.4 Characteristics of intervention strategies from building energy conservation perspective

Chapter 18: Sustainability in Smart Cities: Balancing Social, Economic, Environmental, and Institutional Aspects of Urban Life

Table 18.1 Some of the defining factors in neighborhood context, for sustainability assessment
Table 18.2 Measures used for clustering Gainesville, FL, based on it urban form
Table 18.3 The first four parcels with their corresponding measurements

Chapter 19: Toward Resilience of the Electric Grid

Table 19.1 Attacks on communication networks

Chapter 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

Table 20.1 Qualitative comparison of different sensors to detect persons in rooms

Chapter 22: Climate Resilience and the Design of Smart Buildings

Table 22.1 Energy use – measured versus modeled
Table 22.2 Dry-bulb temperature summary for TMY and future climate scenarios at SSC
Table 22.3 Future electricity, gas, and peal electric demand variation from current conditions
Table 22.4 Climate change adaptation strategies at SSC
Table 22.5 Dry-bulb temperature summary for TMY and future climate scenarios in Chicago
Table 22.6 Climate change adaptation strategies for the Chicago multifamily building
Table 22.7 Comparison of top four energy conservation strategies and their expected energy savings under current and future climate scenario

Chapter 23: Smart Audio Sensing-Based HVAC Monitoring

Table 23.1 Faults in HVAC systems. OA, RA, EA, SA, and MA stand for outside, return, exhaust, supply, and mixed air
Table 23.2 Monetary and nonmonetary decision factors
Table 23.3 Spectrogram plots of different AHU units within the three buildings

Chapter 24: Smart Lighting

Table 24.1 Power saving measurement collected during 6 days
Table 24.2 OOC $c024-math-128$ sequence indexes for various length

Chapter 25: Large Scale Air-Quality Monitoring in Smart and Sustainable Cities

Table 25.1 Category of POIs

Chapter 27: Smart Health Monitoring Using Smart Systems

Table 27.1 Smart meter data parameters
Table 27.2 Single smart meter data sample taken from a dataset containing 78,000 individual smart meters
Table 27.3 Home plug readings over a 1-h period

Chapter 28: Significance of Automated Driving in Japan

Table 28.1 Automated driving levels and their definitions (Japanese government Cabinet Office, 2015)
Table 28.2 A history of automated driving systems and technology

Chapter 29: Environmental-Assisted Vehicular Data in Smart Cities

Table 29.1 RSU-based tags of given flows in Figure 29.3
Table 29.2 The construction of $c029-math-271$ for flows in Figure 29.3

Smart Cities

Foundations, Principles, and Applications

Edited by

Houbing Song, Ravi Srinivasan, Tamim Sookoor, and Sabina Jeschke

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Names: Song, Houbing, editor. | Srinivasan, Ravi, editor. | Sookoor, Tamim, 1984- editor. | Jeschke, Sabina, editor.

Title: Smart cities : foundations, principles, and applications / edited by Houbing Song, Ravi Srinivasan, Tamim Sookoor, Sabina Jeschke.