IoT Technologies in Smart-Cities: From sensors to big data, security and trust (Control, Robotics and Sensors)

Cover
Contents
About the editors
Foreword and outline
1 Access control approaches for smart cities
	1.1 Introduction
	1.2 Smart cities
		1.2.1 What is a smart city?
		1.2.2 Smart city applications
		1.2.3 Key enabling technologies for smart cities
			1.2.3.1 IoT-enabled smart cities
			1.2.3.2 CPSs, IoT and smart cities
			1.2.3.3 Data management in smart cities
			1.2.3.4 Blockchain technology
			1.2.3.5 Cloud computing
			1.2.3.6 Machine learning
	1.3 Security and privacy challenges
		1.3.1 Unique characteristics of smart city applications
		1.3.2 Privacy
		1.3.3 Secure interoperation
		1.3.4 Insider attacks
		1.3.5 Trust management
	1.4 AC approaches for smart cities
		1.4.1 AC models
		1.4.2 AC models and frameworks for smart city and smart city applications
		1.4.3 AC models and frameworks for secure interoperation
			1.4.3.1 Basics of secure interoperation
			1.4.3.2 Methods and frameworks for secure interoperation
		1.4.4 Trust-based AC models and frameworks
		1.4.5 AC models and frameworks for insider threats
	1.5 Future research directions and perspectives
	1.6 Conclusions
	Acknowledgment
	References
2 Impact of Internet of Things in smart cities
	2.1 Introduction
	2.2 Smart city
	2.3 IoT technologies for smart cities
	2.4 Smart city: IoT applications
		2.4.1 Smart infrastructure
		2.4.2 Smart building and properties
		2.4.3 Smart industrial environment
		2.4.4 Smart city services
		2.4.5 Smart energy management
		2.4.6 Smart water and weather management
		2.4.7 Smart waste management
	2.5 Applications: world wide
		2.5.1 International-use cases
			2.5.1.1 Amsterdam, The Netherlands
			2.5.1.2 Chicago and NewYork, USA
			2.5.1.3 Busan, South Korea
			2.5.1.4 Nice, France
			2.5.1.5 Padova City, Italy
			2.5.1.6 Sweden
			2.5.1.7 Jiujiang City, China
		2.5.2 National-use cases
			2.5.2.1 Mumbai City
			2.5.2.2 Jaipur City
			2.5.2.3 Nagpur City
			2.5.2.4 Pune City
			2.5.2.5 Vizag City
			2.5.2.6 Hyderabad City
	2.6 Challenges and future directions
	2.7 Conclusion
	References
3 IoT-based smart water
	3.1 Smart city
		3.1.1 IoT in traditional city
		3.1.2 Myths about smart cities
		3.1.3 IoT for communication in smart cities
	3.2 Smart water
	3.3 SW challenges
		3.3.1 Asset maintenance
		3.3.2 Leakage maintenance
		3.3.3 SW metering maintenance
		3.3.4 Challenges
	3.4 Proposed structure for SW
	3.5 IoT as solution
		3.5.1 IoT in asset maintenance and leakage detection
		3.5.2 IoT in SW metering
		3.5.3 IoT in SW bottle
		3.5.4 IoT in SW distribution network
		3.5.5 IoT in SW irrigation
		3.5.6 IoT in remote controlling of SW assets
		3.5.7 IoT for SW pricing or revenue generation
		3.5.8 IoT for smart billing for SW
		3.5.9 IoT for measuring deep water quality
		3.5.10 IoT for predicting water-based natural calamities
	3.6 Lessons
	3.7 Conclusion
	References
4 Contiki-OS IoT data analytics
	4.1 Introduction
	4.2 Contiki OS
	4.3 Contiki-OS message structures
	4.4 Mathematical formulas for required statistics
	4.5 Proposed algorithms
	4.6 Results and discussion
	4.7 Conclusion
	References
5 Analysis of the safety of the Internet of Things in the mesh
	5.1 Introduction
	5.2 Security of the IoT
		5.2.1 X. 509
			5.2.1.1 Strengths of X. 509
			5.2.1.2 Weaknesses of X. 509
		5.2.2 Open Trust Protocol
			5.2.2.1 Strengths of the OTrP
			5.2.2.2 Weaknesses of the OTrP
	5.3 Safety aspects in the IEEE 802.15.4 standard
	5.4 Technical guidelines for securely transferring information between selected IoT mesh points
		5.4.1 Networking modes
			5.4.1.1 Smart metering
			5.4.1.2 Smart lighting
			5.4.1.3 Industry 4.0
		5.4.2 Types of communication
			5.4.2.1 Node authorization
			5.4.2.2 Communication between nodes and end to end
	5.5 Security of the implementation of the mesh IoT
		5.5.1 Bluetooth mesh networking
		5.5.2 Disruption-tolerant mesh network
		5.5.3 ELIoT Pro
		5.5.4 LoRa
		5.5.5 Thread
		5.5.6 Z-Wave
		5.5.7 Zigbee
	5.6 Comparison of security aspects in different networks
	5.7 Summary
	References
6 Design of smart urban drainage systems using evolutionary decision tree model
	6.1 Introduction
		6.1.1 Overview of drainage systems design criteria
		6.1.2 Overview of machine learning application in drainage systems design
	6.2 Materials and methods
		6.2.1 Experimental data
		6.2.2 Data preparation
		6.2.3 Performance indices
		6.2.4 Decision tree
		6.2.5 GP model
		6.2.6 Hybrid EDT model
		6.2.7 Comparison of models
		6.2.8 IoT applications in drainage systems
	6.3 Conclusions
	References
7 Statistical analysis for sensory E-Health applications: opportunities and challenges
	7.1 Introduction
	7.2 Background
	7.3 Data set
		7.3.1 Aruba test bed
		7.3.2 Data set and activities
		7.3.3 Data preparation
			7.3.3.1 CSV conversion
			7.3.3.2 Missing/corrupted value handling
			7.3.3.3 Data set segmentation
	7.4 Opportunities and directions
	7.5 Conclusion
	References
8 Cybersecurity attacks on medical IoT devices for smart city healthcare services
	8.1 Introduction
	8.2 Smart city healthcare
		8.2.1 Overview
		8.2.2 Smart city healthcare systems
			8.2.2.1 Telemonitoring systems
			8.2.2.2 Smart drug delivery
			8.2.2.3 Remote healthcare provision system
			8.2.2.4 Smart surgery
		8.2.3 Benefits arising from smart city healthcare
	8.3 IoT medical devices for smart city healthcare systems
		8.3.1 Overview
		8.3.2 Medical IoT devices security objectives
			8.3.2.1 Information security objectives
			8.3.2.2 Access-level security objectives
			8.3.2.3 Functional security objectives
	8.4 Cybersecurity attacks on medical IoT devices
		8.4.1 Attack vectors
		8.4.2 Post-compromise actions
		8.4.3 Impact
		8.4.4 Impact evaluation
	8.5 Security countermeasures
		8.5.1 Ensuring confidentiality and integrity
		8.5.2 Ensuring availability
		8.5.3 Ensuring authentication and authorization
		8.5.4 Ensuring nonrepudiation
	8.6 Emerging good practice and approaches (standards)
	8.7 Conclusions and further challenges
	References
9 HaLow: registering thousands of low-power sensors in smart cities
	9.1 Introduction
	9.2 IEEE 802.1ah HaLow: an overview
		9.2.1 Use cases for HaLow
			9.2.1.1 Smart-grid: low-power wireless sensor networks
			9.2.1.2 Long-range backhaul
		9.2.2 HaLow MAC layer
	9.3 Link set-up in HaLow networks for massive IoT devices registration
		9.3.1 Link set-up in WLANs
		9.3.2 Distributed authentication control
		9.3.3 Centralized authentication control
	9.4 Case study: hybrid slotted-CSMA/CA-TDMA
		9.4.1 SIFT geometric distribution
			Hybrid slotted-CSMA/CA-TDMA
		9.4.2 Expected number of AuthReq frames in a C-Slot
	9.5 Conclusions
	References
10 Statistical analysis of low-power sensor motes used in IoT applications
	10.1 Introduction
	10.2 Literature review
	10.3 Data set: Mica2Dot sensors and statistical analysis
		10.3.1 Descriptive analysis
		10.3.2 Linear regression model (ordinary least squares estimates)
		10.3.3 Ordered logit models
	10.4 Conclusion and future works
	References
Appendix A. Variables by duration groups
11 Conclusions and recommendations
Index
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