Handbook of Smart Cities (eBook)

Muthucumaru Maheswaran   Muthucumaru Maheswaran is an associate professor in the School of Computer Science and Department of Electrical and Computer Engineering at McGill University. He got a PhD in Electrical and Computer Engineering from Purdue University, West Lafayette and a BScEng degree in Electrical and Electronic Engineering from the University of Peradeniya, Sri Lanka. He has researched various issues in scheduling, trust management, and scalable resource discovery mechanisms in Clouds and Grids. Many papers he co-authored in resource management systems have been highly cited by other researchers in the area. Recently, his research has focused in security, resource management, and programming frameworks for Cloud of Things. He has supervised the completion of 8 PhD theses in the above areas. He has published more than 120 technical papers in major journal, conferences, and workshops. He holds a US patent in wide-area content routing.   Elarbi Badidi Dr. Elarbi Badidi is Associate Professor at the College of Information Technology (CIT) of the United Arab Emirates University (UAEU). He received his undergraduate degree in electrical engineering and M.Sc. in computer science from École Mohammedia des Ingénieurs, Rabat, Morocco, and the Ph.D. in computer science from Université de Montréal, Canada. Before joining the UAEU, He served for three years as a bioinformatics group leader at the biochemistry department of Université de Montréal. Dr. Elarbi has over ten years of research experience in service-oriented architecture, cloud computing, and context-aware systems, focusing on quality of service management, service level agreement management, quality of context (QoC) negotiation, QoC based selection, and data-as-a-service provisioning. He has published over sixty peer-reviewed papers in reputed international journals and conferences and eight book chapters. He served on the technical program committees of many international conferences and as a reviewer of several journals. His current research interests lie in the areas of cloud computing, Internet of Things (IoT), big data, data streams processing, and data analytics.

Preface 5
Contents 8
Internet of Things (IoT) Infrastructures for Smart Cities 10
1 IoT and Smart Cities: An Introduction 10
2 Background 11
2.1 Current trends in Internet of Things for Smart Cities 11
2.2 Smart City Applications Around the World 12
2.2.1 Smart Mobility Projects 12
2.2.2 Smart Sustainability Projects 14
2.2.3 Smart Living Projects 15
3 Sensing Architectures for Smart Cities 17
4 Smart City Enabling Wireless Communication Technologies 21
4.1 Short-range Communications 21
4.2 Medium-range Communications 22
4.2.1 802.15.4-based Technologies 22
4.2.2 WiFi 24
4.2.3 Bluetooth Low Energy (BLE) 25
4.3 Long-range Communications 25
4.3.1 LoRaWAN 26
4.3.2 Sigfox 27
4.4 Open Issues 27
5 Data Security in Smart Cities 29
5.1 Examples of IoT Security Vulnerabilities in Smart Cities 30
5.2 IoT Security Threats, Countermeasures and Research Issues 31
5.2.1 IoT Security Threats 31
5.2.2 Countermeasures and Research Issues 33
6 Conclusion 36
References 36
The Role of 5G and IoT in Smart Cities 40
1 Introduction 40
2 Smart Cities 42
3 Internet of Things and Wireless Sensor Networks 46
4 5G Networks 50
5 Role of 5G in IoT 54
6 The Role of 5G and IoT in Smart Cities 57
7 Conclusions and Future Directions 60
References 61
Leveraging Cloud Computing and Sensor-Based Devicesin the Operation and Management of Smart Systems 64
1 Introduction 64
2 Cloud Computing 66
3 Unification of Resources 67
3.1 Cloud-Based Middleware for Sensor Equipped Bridges 68
3.2 Cloud-Based Platform for Research Collaboration 70
4 Data Analytics Platforms and Resource Management 72
4.1 MapReduce/Hadoop 72
4.1.1 Closed Systems 74
4.1.2 Open Systems 75
4.1.3 Energy Aware Resource Management 76
4.2 Streaming Data Analytics 77
5 Information Dissemination and Control for Smart Applications 80
5.1 Museum Tour Guide System 80
5.2 Restaurant Management System 83
6 Summary and Conclusions 86
References 87
Mobile Computing, IoT and Big Data for Urban Informatics: Challenges and Opportunities 90
1 Introduction 90
2 Infrastructure and Frameworks for Smart City Management 93
3 Systems and Approaches for Facilitating Smart City Applications 95
3.1 Transportation 95
3.1.1 Sensing Road Conditions 95
3.1.2 Facilitating Traffic Navigation and Parking 97
3.2 Waste Management and Environmental Monitoring 98
3.3 IoT Technology and Retail Industry 100
3.3.1 Discussion and Insights 101
4 Incentive-Based Mobile and IoT Data Collection and Management 101
4.1 Incentive-Based Practical Infrastructure for Mobile Crowdsensing 102
4.2 Security and Privacy of Incentive Mechanisms for Mobile Crowdsensing 103
4.3 Energy-Efficient Incentive Mechanisms for Mobile Crowdsensing 104
4.4 Game Theory and Behavioral Economics for Incentive Mechanism Design 105
4.5 Data-Driven Incentive Mechanisms for Mobile Crowdsensing 107
4.5.1 Discussion and Insights 110
5 Big Data Management and Analytics for IoT Applications 110
6 Knowledge Management for IoT Applications 112
6.1 Background on RDF and SPARQL 113
6.2 SPARQL Query Processing on RDF Triples 114
6.3 RDF Quads for IoT Applications 115
7 IOT Security and Privacy 116
8 Conclusion 117
References 117
5G Wireless Micro Operators for Integrated Casinos and Entertainment in Smart Cities 123
1 Introduction 123
2 Literature Survey 127
3 Micro Cell and 5G Micro Operator 130
3.1 Small Cells in C-RAN 130
3.2 5G Micro Operator 133
4 Integrated Casinos and Entertainment (ICE) Innovation in Smart Cities 134
4.1 Outlook to Future Integrated Casinos and Entertainment 135
4.1.1 Casino Service Delivery 135
4.1.2 Physical Environment 135
4.1.3 Equipment Vendors 135
4.1.4 Technology Vendors 135
4.1.5 Supervisory Agencies 136
4.2 Concentric Value Circles Model for 5G ICEMO 136
4.3 5G ICEMO Business Model 140
5 5G Cloud-Enabled ICEMO 142
5.1 5G ICEMO System Architecture 143
5.2 5G ICEMO Wireless Network Architecture 143
6 Illustrative Use Cases 146
6.1 Mega Jackpot 146
6.2 Anti-Counterfeiting Lottery 147
6.3 Autonomous Transport 149
7 Benchmarking of Las Vegas, Macao and Singapore 150
7.1 Smart Cities in Las Vegas 150
7.2 Smart Cities in Macao 151
7.3 Smart Nation in Singapore 152
8 Conclusion 153
References 154
An IoT-Based Urban Infrastructure System for Smart Cities 158
1 Introduction 159
2 The IoT Architecture 161
3 Smart Parking Application Scenario 163
4 The Implementation of Urban Infrastructure, Hardware Devices and Network Technologies 165
5 Methodology for the IoT Oriented to Smart Parking 165
5.1 Data Sensing 166
5.2 Receiving Data 169
5.3 Data Processing 173
5.4 Database 174
5.5 Presentation 175
6 Conclusions 177
References 177
Vehicular Crowdsensing for Smart Cities 181
1 Introduction 181
2 Background and Characteristics 183
2.1 Two Different Crowdsensing Paradigms 183
2.2 Central Server for Vehicular Crowdsensing 185
3 Public Vehicular Crowdsensing 186
3.1 Background 186
3.2 System Model 186
3.3 Definitions and Assumptions 187
3.4 Problem Statement 188
3.5 Participant Selection Algorithms 188
4 Personalized Vehicular Crowdsensing 195
4.1 System Model and Assumptions 196
4.2 Definitions 196
4.3 Problem Formulation 199
4.4 Algorithm Design 200
4.5 Experiment Setup 202
4.6 Main Results 204
4.7 The Execution Time 205
4.8 Window Based Scheduling (Static vs. Dynamic) 206
5 Future Works 207
6 Conclusion 208
References 209
Towards a Model for Intelligent Context-Sensitive Computingfor Smart Cities 211
1 Introduction 212
2 Motivating Scenarios 214
3 System Architecture 214
4 Machine Learning Models 217
4.1 The Dataset 219
4.2 Model Design 219
4.3 Implementation and Performance Analysis 220
4.4 Multi-stream Data Prediction 224
5 Challenges and Benefits of Adapting Machine Learning in Intelligent Context Sensitive Computing 224
5.1 Challenges of Incorporating Machine Learning 224
5.2 Benefits of Intelligent Context Sensitive Computing 225
6 Related Work 226
6.1 Cloud Computing, Edge Processing and Smart Cities 226
6.2 Programming and Computing Models for Enabling Smart Cities 226
6.3 Machine Learning and Leveraging Fog Computing for Smart Applications 227
6.4 Sensor Data Prediction Schemes 228
7 Conclusions and Future Work 229
References 230
Intelligent Mobile Messaging for Smart Cities Based on Reinforcement Learning 233
1 Introduction 234
2 Background: Message Types, Messaging Frameworks, and Reinforcement Learning 235
2.1 Message Types 235
2.2 Messaging Framework 236
2.2.1 Route Framework 236
2.2.2 P2P Framework 236
2.2.3 Intelligent (Hybrid) Framework 236
2.3 Reinforcement Learning 237
3 Intelligent Messaging Architecture and Learning Algorithm 238
3.1 States: STree 238
3.2 Actions (Choosing Connection Type) 240
3.3 Reward 240
3.3.1 Server Reward 241
3.3.2 User Reward 241
3.4 Learning Algorithm 242
3.4.1 Learning without QNP 243
3.4.2 Learning with QNP 243
3.5 Adaptation of STree 244
4 Simulation Model 245
4.1 Simulation Engine Components 246
4.2 Steps of Simulation 246
4.2.1 Initialization 246
4.2.2 Simulation at k-th Step 247
5 Experiments 247
5.1 Experiment 1 248
5.2 Experiment 2 249
5.3 Experiment 3 253
6 Conclusion and Future Work 256
References 256
Asymmetric Interoperability for Software Servicesin Smart City Environments 260
1 Introduction 260
2 Background 262
3 Analysing the Service Interoperability Problem 264
3.1 The Main Aspects of Interoperability 264
3.2 A Model of Interoperability 266
3.3 A Model of Coupling 268
3.4 A Structural Model of Service Adaptability and Changeability 270
4 Existing Technologies: Symmetric Service Interoperability 271
5 A Different Approach: Asymmetric Service Interoperability 274
5.1 What Is Asymmetric Interoperability? 274
5.2 Illustrating Compliance 276
5.3 A Data Schema Model 278
5.4 Structural Service Interoperability 279
5.5 Illustrating Conformance 282
5.6 Usefulness of the Approach 284
6 Conclusion 285
References 285
Management of Video Surveillance for Smart Cities 289
1 Introduction 289
2 Background 290
2.1 Key Components for Smart Video Surveillance System 290
2.1.1 Video Acquisition Layer 291
2.1.2 Connectivity Layer 291
2.1.3 Management Layer 292
2.1.4 Application Layer 293
2.2 Example Video Surveillance Applications Around the World 294
2.3 Challenges of Smart Video Surveillance System 296
3 Video Surveillance Management Platform for Smart City 296
3.1 On-premises Video Management Solutions 297
3.2 Cloud-based Video Management Services 298
3.2.1 Azure Media Services 298
3.2.2 Stratocast 300
3.3 Experimental Testbed 301
3.3.1 Video Management Platform Setup 302
3.3.2 Performance Comparison 302
4 Deployment Challenges of Smart Video Surveillance 306
4.1 Deployment Scenarios 306
4.2 Deployment Estimation 308
4.2.1 Simulation Results 308
4.2.2 Scale-up Estimation 310
5 Conclusion 312
References 312
Intelligent Transportation Systems Enabled ICT Frameworkfor Electric Vehicle Charging in Smart City 315
1 Introduction 316
2 Related Work for CS-Selection 317
2.1 EV Charging in “On-the-Move” Mode 317
2.2 Urban Data in Intelligent Transportation Systems 317
2.3 Communication Technologies in ITS 318
2.4 Scalability of Charging System 318
3 Centralized Charging System 319
3.1 Cellular Network Communication Enabled Charging System 319
3.2 Enabling Internet of EVs for Charging Reservations Relay 320
4 Distributed Charging System 321
4.1 V2I Communication Enabled Charging System 321
4.2 V2V Communication Network Enabled Charging System 322
4.2.1 Basic Charging System without Supporting Charging Reservation 323
4.2.2 Reservation Based Charging System 324
5 Hybrid Charging System 326
5.1 V2I Communication Network Enabled Charging System 326
5.2 V2V Communication Network Enabled Charging System 330
6 Further Discussions 330
6.1 Energy Sustainability 330
6.2 Data Analytics 332
6.3 Security and Privacy 332
7 Conclusion 332
References 333
Green Transportation Choices with IoT and Smart Nudging 335
1 Introduction 335
2 Smart Nudging 337
2.1 Nudging 337
2.2 Personalisation 339
2.3 Situational Awareness 341
3 Smart Nudging Architecture 342
4 Sense 344
4.1 IoT Sensors 344
4.2 Crowdsensing 345
4.2.1 Mobile Sensing 347
4.2.2 Personal Sensing and Crowdsensing 348
4.3 Aggregated Data 348
4.4 Crowdsourcing 349
4.5 Static Data 349
5 Analyse 350
5.1 Methods for Analysis 350
5.2 Back-End Processing 351
5.3 Edge Processing 351
6 Inform and Nudge 352
6.1 Nudging Approaches 352
6.2 Personal Recommendations 353
6.3 Interacting with the User 354
7 Summary 355
References 356
Energy Harvesting in Smart Building Sensing: Overviewand a Proof-of-Concept Study 359
1 Introduction 359
2 Energy Harvesting in Building Environments 361
2.1 Energy Harvesting Opportunities and Efficiencies 362
2.2 Designing for Multi-decade Sustainable Operation 364
3 Sensor Node Design 365
3.1 The Energy Requirements of Modern Ultra-low Power RF 366
3.2 Designing and Testing a TE Harvesting Node 367
3.3 Performance Results 369
4 Multi-hop Environments 371
4.1 Routing Problem Definition 371
4.2 Optimization Formulations for Multi-hop Routing with Energy Harvesting 372
4.3 Simple Routing Strategies 375
5 Conclusion 378
References 380
Building a Data Pipeline for the Management and Processing of Urban Data Streams 382
1 Introduction 383
2 Urban Data Streams 383
3 Urban Data Integration 385
4 Batch and Real–time Processing of Data Streams 388
5 Building an Urban Data Pipeline 389
6 A Framework for Urban Data Management and Processing 390
6.1 Architecture Overview 390
6.1.1 Data Sources 390
6.1.2 Edge Computing 391
6.1.3 Messaging 392
6.1.4 Data Streams Processing 393
6.1.5 Applications 393
6.2 Use Case Scenario 394
7 Discussion 396
8 Conclusion 396
References 397
Index 399