Communication Technologies for Networked Smart Cities 1839530294, 9781839530296

Table of contents :
Cover
Contents
About the editors
1 Introduction to communication technologies for networked smart cities
1.1 Introduction
1.2 Overview of the book
1.3 Chapter contributions
References
2 Narrowband IoT technologies for smart city applications
List of acronyms
2.1 Introduction to smart city and IoT
2.2 Wireless technologies/protocols for IoT
2.2.1 Long-range IoT
2.2.2 Why NB-IoT for smart cities?
2.2.2.1 Easy network access, scalability, and management
2.2.2.2 High capacity, low device unit cost, and extended battery life
2.2.2.3 Good coverage
2.2.2.4 Secure connectivity
2.3 Narrowband IoT
2.3.1 Physical layer specifications
2.3.1.1 Physical resources and frame structure
2.3.1.2 Physical signals and channels
2.3.2 MAC layer specifications
2.3.2.1 Random access procedure
2.3.2.2 Radio resource scheduling
2.3.2.3 Hybrid automatic repetition quest (HARQ) operation
2.4 NB-IoT applications in smart cities
2.4.1 Smart lighting
2.4.2 Smart parking
2.4.3 Smart transportation
2.4.4 Smart hospital
2.4.5 Smart home/building
2.4.6 Smart wearables
2.4.7 Smart grid
2.4.8 Industrial applications
2.5 NB-IoT via satellite for smart cities
2.5.1 Relevant applications
2.5.1.1 Disaster management
2.5.1.2 Offloading of extremely crowded areas
2.5.1.3 Transportation
2.5.2 Architecture options
2.5.3 The main challenges of an NB-IoT via satellite network
2.5.3.1 PHY layer
2.5.3.2 MAC layer
2.5.4 Possible solutions
2.5.4.1 Positioning estimation strategy for time/frequency compensation
2.5.4.2 Group-based user scheduling strategy for time/frequency-misalignment reduction
2.5.4.3 New signaling configurations with increased timers
2.5.4.4 HARQ deactivation
2.6 Conclusions
References
3 Wireless green technologies for smart cities
Summary of main acronyms
3.1 Introduction
3.2 Modulation-based simultaneous wireless information and power transfer
3.2.1 System model
3.2.2 Hybrid constellation shaping in M-QAM
3.2.3 Comparison of M-SWIPT and PS-SWIPT
3.2.4 Theoretical symbol error and achievable rate of M-QAM with M-SWIPT
3.2.5 Performance analysis of M-SWIPT
3.3 Frequency-splitting-based simultaneous wireless information and power transmission
3.3.1 Analysis of non-linear distortion due to FS-SWIPT
3.3.2 Performance analysis of FS-SWIPT
3.4 Conclusion
References
4 Channel models for an indoor power line communication system
4.1 Introduction
4.2 Memoryless PLC channel
4.2.1 Multipath channel model
4.2.2 Middleton class A noise model
4.2.3 Single-carrier modulation for PLC
4.2.4 Multicarrier modulation for PLC
4.3 PLC channel with memory
4.4 Hidden Markov models
4.4.1 Model notations
4.4.2 Model architecture
4.4.3 HMM problems
4.4.4 Generalized N-state and three-state HMMs
4.5 Semi-hidden Fritchman Markov models
4.5.1 Generalized SHFMM basics
4.5.2 A three-state SHFMM
4.6 Machine learning estimation algorithm for SHFMMs
4.6.1 The Baum–Welch algorithm
4.6.2 First-order Baum–Welch algorithm for SHFMM
4.7 SHFMM for indoor PLC system
4.7.1 The software-defined NB-PLC transceiver model
4.7.2 Modeling methodology
4.8 Estimated models—state crossover probabilities
4.8.1 Estimated state crossover probabilities (mildly disturbed)
4.8.2 Estimated state crossover probabilities (heavily disturbed)
4.9 Model validation and analysis
4.9.1 Log-likelihood ratio plots for the estimated models
4.9.2 Measured versus model error-free run distribution plots
4.9.3 Measured versus model error probabilities
4.9.4 The chi-square test and the mean square error
4.10 Conclusions
References
5 Non-orthogonal multiple-access-based visible light communications for smart city applications
List of abbreviations
5.1 Introduction
5.1.1 Visible light communication
5.1.2 Non-orthogonal multiple access
5.1.2.1 3GPP standard for NOMA and smart city applications
5.1.2.2 NOMA-VLC for smart city applications
5.1.2.3 Future research directions forVLC in smart cities
5.2 A NOMA-VLC communication system for smart buildings: a use case
5.2.1 System model
5.2.1.1 Channel models
5.2.2 Achievable rates of the system
5.2.2.1 NOMA-RF communication: BS to relay
5.2.2.2 NOMA-VLC communication: relays to users
5.2.3 Performance trade-off of the system
5.2.4 Energy efficiency of the system
5.3 Conclusion
References
6 A comprehensive review of communication technologies for street lighting applications in smart cities
6.1 Introduction
6.2 Smart street lighting applications in smart cities
6.2.1 Basic street lighting control
6.2.2 Advanced street lighting control
6.2.3 Performance/energy reporting
6.2.4 Environmental/traffic/public safety monitoring
6.2.5 Signage, alerts, and other services
6.2.6 Emergency response
6.3 Architecture: smart street lights system with key components
6.3.1 Control center
6.3.2 Street lights
6.3.3 Sensors
6.3.4 Other optional services and smart city applications
6.3.5 Communication network
6.4 Various communication technologies and protocols supporting smart street lighting applications
6.4.1 Communication technologies and protocols
6.4.1.1 SigFox
6.4.1.2 LoRa
6.4.1.3 Weightless
6.4.1.4 Symphony Link
6.4.1.5 Random phase multiple access
6.4.1.6 Narrowband-IoT
6.4.1.7 LTE-M
6.4.1.8 Power line communication
6.4.1.9 ZigBee
6.4.1.10 6LoWPAN
6.4.1.11 Wireless local area network
6.4.1.12 Wireless mesh
6.4.1.13 WiMAX
6.4.1.14 Cellular
6.4.2 Communication protocols
6.4.2.1 Digital addressable lighting interface
6.4.2.2 X10
6.4.2.3 Insteon
6.4.2.4 Consumer electronic bus
6.5 Network requirements and suitable communication technologies of smart street lighting applications
6.6 Summary
References
7 Smart vehicles for smart cities
7.1 Introduction
7.2 Design goals of autonomous vehicles
7.3 SAE levels—an overview
7.3.1 Level-0 automation
7.3.2 Level-1 automation
7.3.3 Level-2 automation
7.3.4 Level-3 automation
7.3.5 Level-4 automation
7.3.6 Level-5 automation
7.4 Vehicular communication
7.4.1 Vehicle-to-vehicle
7.4.2 Vehicle-to-infrastructure
7.4.3 Vehicle-to-everything
7.4.4 Cellular vehicle-to-everything
7.4.4.1 Working of C-V2X communication
7.5 ITSs enabled by flying RSUs
7.5.1 Traffic modeling
7.5.2 UAV deployment strategy
7.6 Trust framework for vehicular networks
7.6.1 Understanding trust in vehicular networks
7.6.2 Evaluation of the trust model
7.6.2.1 Direct trust computation
7.6.2.2 Recommended trust computation
7.6.3 Decision tree classification model to frame trust rules
7.6.4 Artificial neural networks to train the vehicular nodes
7.7 Conclusion, open issues, and solution directions
References
8 Vehicle-assisted framework for delay-sensitive applications in smart cities
8.1 Introduction
8.2 Vehicular networks
8.3 Vehicle-assisted network and their challenges
8.4 Traditional offloading decision models
8.4.1 Emerging decision models for vehicular networks
8.4.2 Data protection, security, and trust management
8.5 Applications of vehicular networks
8.6 Conclusion
8.7 Future directions
References
9 Big data analytics for intelligent management of autonomous vehicles in smart cities
9.1 Motivation and introduction
9.2 Big data analytic and vehicular mobility modeling for smart city
9.2.1 Description of captured city data
9.2.2 Vehicular mobility models based on data analysis
9.2.2.1 Vehicular traffic flow models
9.2.2.2 Driving behavior models
9.3 Network calculus-assisted intelligent management of autonomous vehicle fleet in smart city
9.3.1 Constructing a resource model through ML
9.3.1.1 Resource model under ML
9.3.1.2 Road system under network calculus
9.3.1.3 Minimizing waiting time by matching optimization
9.3.2 Online traffic modeling and management
9.4 Federated-learning-based autonomous driving for secure intelligentAVs management
9.4.1 Background
9.4.2 FL-based autonomous driving structure
9.4.3 Performance analysis
9.5 Conclusion
References
10 Machine-learning-enabled smart cities
10.1 Machine learning in the context of smart city
10.1.1 Supervised learning
10.1.2 Unsupervised learning
10.2 Smart grid
10.2.1 Smart grid operation
10.2.2 Smart grid security
10.2.3 Renewable energy systems
10.3 City mobility
10.3.1 Traffic prediction
10.3.2 Online transportation networks
10.3.3 Self-driving vehicles
10.3.4 Efficient parking garages
10.3.5 Traffic management
10.4 City security and safety
10.5 Smart healthcare
10.6 Smart environment
10.6.1 Smart air monitoring
10.6.2 Smart waste management
10.7 Smart home automation
10.7.1 Device management
10.7.2 Energy management
10.7.3 Home security
10.7.4 Home organisation
10.8 Smart business
10.8.1 Financial services
10.8.1.1 Loan default prediction
10.8.1.2 Online fraud detection
10.8.2 Marketing
10.8.2.1 Product recommendations
10.8.2.2 Email marketing
10.8.2.3 Online customer support
10.9 Standardising smart cities
10.10 Conclusion
References
11 Blockchain-based secure communication in smart cities
11.1 Introduction
11.2 IoT, big data, and smart city
11.2.1 Internet of Things
11.2.2 Big data
11.2.3 Smart city
11.2.3.1 Layered architecture of smart city
11.3 Security and privacy issues in smart city
11.3.1 Cybersecurity threats in smart city
11.3.2 Botnets attacks in smart city
11.3.3 AI-based privacy threats in smart city
11.4 Security and privacy requirements of the smart city
11.4.1 Authentication
11.4.2 Confidentiality
11.4.3 Availability
11.4.4 Integrity
11.4.5 Privacy protection of citizens
11.5 Blockchain in IoT/smart city
11.5.1 Introduction to blockchains
11.5.1.1 Consensus algorithms
11.5.1.2 Types of blockchains
11.5.2 Motivation for application of blockchains in smart city
11.6 Blockchain-based security mechanisms (BBSMs) in smarty city
11.6.1 Securing energy management in smart city
11.6.2 Securing smart transportation in smart city
11.6.2.1 Roadside unit
11.6.2.2 Registration office
11.6.2.3 Cloud server
11.6.2.4 Smart vehicle
11.6.3 Securing health-care systems in smart city
11.7 Case studies: blockchain-enabled smart cities
11.8 Open issues and future research directions
11.8.1 Lightweight security mechanisms
11.8.2 Innovative privacy preserving schemes
11.8.3 Scalability issues
11.8.4 Optimization of consensus algorithms
11.8.5 Fair miner selection
11.8.6 Blockchain standardization
11.9 Conclusion
References
12 A software-defined blockchain-based architecture for scalable and tamper-resistant IoT-enabled smart cities
12.1 Introduction
12.2 Background and literature review
12.2.1 Overview of blockchain technology
12.2.2 Convergence of blockchain and IoT
12.2.3 Blockchain security over SDN
12.3 Architectural design
12.3.1 System design
12.3.2 Flow management
12.3.3 Smart contract design
12.3.4 Consensus algorithm
12.4 Use cases
12.4.1 Blockchain-SDN-enabled Internet of vehicles
12.4.2 When blockchain and SDN meet Internet of Energy (IoE)
12.4.3 Improving security between IoT gateways
12.5 Open challenges and directions for future work
12.5.1 Scalability issues
12.5.2 Power consumption
12.5.3 Storage
12.5.4 Privacy leakage
12.6 Potential future research opportunities
12.6.1 Off-chaining models
12.6.2 Data analytics
12.6.3 Artificial intelligence
12.6.4 Smart contracts
12.7 Conclusion
Acknowledgments
References
13 Blockchain-based secure and trustworthy sensing for IoT-based smart cities
13.1 Introduction
13.2 Basics of blockchain technology
13.2.1 Structure of a blockchain
13.2.2 Blockchain consensus
13.2.3 Smart contracts
13.2.4 Blockchain type
13.2.5 Properties of blockchain
13.3 Security and trust issues in IoT-based smart cities
13.3.1 Security constraints and limitations in IoT
13.3.1.1 Security constraints related to IoT devices
13.3.1.2 Security constraints related to IoT networking
13.3.2 Security requirements and issues for IoT
13.3.2.1 Data integrity
13.3.2.2 Access control and authentication
13.3.2.3 Availability of data and services
13.4 Enhancing the security and trust of IoT-based smart cities using blockchain
13.4.1 Malicious node detection approaches
13.4.2 Trust management schemes
13.4.3 Blockchain-based security and trust mechanisms
13.5 Open challenges
13.6 Conclusion
References
Index
Back Cover