Internet of Things: Cases and Studies

Preface
Introduction
Contents
About the Editors
1 Blockchain as a Complementary Technology for the Internet of Things: A Survey
	1.1 Introduction
	1.2 Blockchain Technology
	1.3 Blockchain for IoT
	1.4 The Blockchain Consensus
		1.4.1 Proof of Work
		1.4.2 Byzantine Fault Tolerance
		1.4.3 Proof of Stake
		1.4.4 Hybrid Consensus
		1.4.5 Tangle IOTA
		1.4.6 Deep Learning Approaches
		1.4.7 Soft Computing
	1.5 Data Organisation and Consensus: Criticisms
	1.6 Conclusion
	References
2 Enablers and Inhibitors for IoT Implementation
	2.1 Introduction
	2.2 Enablers and Barriers to Digitalization
		2.2.1 Digitalization Process Elements
		2.2.2 Enablers
			2.2.2.1 Technology Enablers
			2.2.2.2 Strategic Enablers
			2.2.2.3 Organizational Enablers
		2.2.3 Barriers
			2.2.3.1 Organizational Barriers
			2.2.3.2 Cultural Barriers
	2.3 Enablers and Barriers to IoT Implementation
		2.3.1 IoT Elements
		2.3.2 Enablers
			2.3.2.1 Technology Enablers
			2.3.2.2 Strategic Enablers
			2.3.2.3 Organizational Enablers
		2.3.3 Barriers
			2.3.3.1 Organizational Barriers
			2.3.3.2 Cultural Barriers
	2.4 Conclusions
	A.1 Annex 1
		A.1.1 Summary of Main Concepts and Characteristics
	References
3 The Combination of AI, Blockchain, and the Internet of Things for Patient Relationship Management
	3.1 Introduction
	3.2 Related Work
	3.3 The Model
		3.3.1 The Data Structure
		3.3.2 Federated Learning
		3.3.3 Consensus
	3.4 Architecture
	3.5 Discussion
	3.6 Conclusion
	References
4 Bibliometric Characteristics of Highly Cited Papers on Internet of Things Assessed with Essential Science Indicators
	4.1 Introduction
	4.2 Data and Methods
	4.3 Results
		4.3.1 Distributions of the IoT-HCPs
		4.3.2 Productive Players
		4.3.3 The Top 15 Most Cited Papers
		4.3.4 Author Keyword Analysis
	4.4 Conclusion
	References
5 A Macroeconomic Aspect of IoT Services: Their Marginal Costs
	5.1 Introduction
	5.2 Information and Business Models
	5.3 A Model of IoT
	5.4 Designing Incentives
	5.5 Conclusions
	References
6 Biclustering Analysis of Countries Using COVID-19 Epidemiological Data
	6.1 Introduction
	6.2 Problem Description
		6.2.1 Greedy Approach: Single Objective Size Maximization-Based Fitness Function
		6.2.2 Data Description
	6.3 Proposed Work: COVID-19 Pattern Identification Using Greedy Two-Way KMeans Algorithms
		6.3.1 Optimize Biclusters Using Greedy Approach
	6.4 Results
		6.4.1 Suggestions
	6.5 Conclusion
	References
7 IoT Applications in Healthcare
	7.1 Introduction
	7.2 IoT Applications for Acute Disease Care
		7.2.1 Vital Sign Monitoring for the Emergency Department
		7.2.2 Acute Care Telemedicine
		7.2.3 IoT-Based Detection and Control of Infectious Diseases
	7.3 IoT Applications for Chronic Disease Care
		7.3.1 IoT Healthcare Applications for Alzheimer's Disease
		7.3.2 IoT Healthcare Applications for Diabetes
		7.3.3 IoT Healthcare Applications for Heart Failure
	7.4 IoT Applications for Self-Health Management
		7.4.1 Sleep and Exercise Monitoring Using Smartwatches
	7.5 Conclusion
	References
8 An Interactive Visiting System Using BLE Devices
	8.1 Introduction
	8.2 Related Work
	8.3 Prototype Architecture
		8.3.1 Databases
		8.3.2 Building Information Modelling (BIM)
		8.3.3 Content Management System (CMS)
		8.3.4 Mobile Application
		8.3.5 BLE Devices
	8.4 System Comparison and Discussion
	8.5 Conclusions and Future Work
	References
9 Systematic Market and Asset Liquidity Risk Processes for Machine Learning: Robust Modeling Algorithms for Multiple-Assets Portfolios
	9.1 Introduction and Overview
	9.2 Literature Review and Motivation of Present Research
	9.3 Modeling of Uncertainty with Robust Machine Learning Processes
		9.3.1 Machine Learning Process for the Modeling of Uncertainty Using a Closed-Form Parametric VaR Algorithms
		9.3.2 Machine Learning Process for the Modeling of Adverse Price Impact Using Al Janabi Model
		9.3.3 Machine Learning Process for the Measurement of Transaction Costs
		9.3.4 Machine Learning Process for the Computation of the Overall Risk Exposure
	9.4 Practical Applications for Contemporary Portfolio Optimization and Selection and Risk Management
	9.5 Concluding Remarks and Future Directions
	References
10 Context Modelling in Ambient Assisted Living: Trends and Lessons
	10.1 Introduction
	10.2 Ambient Assisted Living Services
		10.2.1 Definition of AAL Services
		10.2.2 Services for Inhabitants
		10.2.3 Services for Caregiver
		10.2.4 Basic Services
			10.2.4.1 Activity Recognition
			10.2.4.2 Posture Recognition
			10.2.4.3 Localization
			10.2.4.4 Predictive Services
	10.3 Context Information and Context Awareness in AAL Systems
		10.3.1 Contextual Information on Inhabitants
			10.3.1.1 Static Information on Inhabitants
			10.3.1.2 Dynamic Information on Inhabitants
		10.3.2 Environmental Information
		10.3.3 Physical Environmental Information
		10.3.4 Social Environment
		10.3.5 Temporal Information
		10.3.6 Spatial Information
		10.3.7 Delimiting the Context
		10.3.8 Heterogeneity of Data
	10.4 Approaches of Context Modelling in AAL Systems
		10.4.1 Knowledge-Based Approaches
		10.4.2 Data-Driven Approaches
		10.4.3 Hybrid Approaches
		10.4.4 Comparison Between Approaches
	10.5 Discussion
		10.5.1 Nature of Data
		10.5.2 Visual Sensors
		10.5.3 Biosensors
		10.5.4 Activity, Body Posture and Fall Recognition Services
		10.5.5 Predictive Services
		10.5.6 Temporal Reasoning
		10.5.7 Services for Inhabitants
	10.6 Conclusion
	References
11 Design of Algorithm for IoT-Based Application: Case Study on Intelligent Transport Systems
	11.1 Introduction
	11.2 IoT Applications
	11.3 Machine Learning and IoT in Transportation Research
	11.4 Problem-Solving Techniques for IoT-Based Transportation
		11.4.1 Time Series Analysis
		11.4.2 Machine Learning Techniques
			11.4.2.1 Supervised Learning
			11.4.2.2 Unsupervised Learning
			11.4.2.3 Reinforcement Learning
	11.5 Traffic Sequence Mining Framework for Prediction of Traffic Volume on Highways
		11.5.1 Problem Description
		11.5.2 Methodology
		11.5.3 Mining Frequent Traffic Sequence Rules
	11.6 Learning Extreme Transportation Traffic Conditions Using Local and Global Instance-Based Regression
		11.6.1 Problem Description
	11.7 Dynamic Vehicle Routing
		11.7.1 Problem Description
	11.8 Discussion
	11.9 Conclusion
	References
12 Examining Spatial Movement Patterns of Travelers: Cases in Tourist Destinations
	12.1 Introduction
	12.2 Attempts to Utilize IoT in Tourism
		12.2.1 Extracting Location Data of People Through IoT
		12.2.2 Tourism Research on Wi-Fi Tracking Sensors
	12.3 Utilizing Mobile Kukan Toukei to Examine the Movement Patterns of Travelers
		12.3.1 Identifying the Number of Travelers and Their Characteristics in Tourist Destinations in Nagoya City
		12.3.2 The Results of the Survey Conducted in Nagoya City
	12.4 Analyzing the Wi-Fi Tracking Sensor Data with Other Survey Data to Clarify Travelers' Movement Patterns
		12.4.1 Analysis Overview
		12.4.2 Widespread Travel Routes for Tourists within the Kyoto by the Sea Tourism Zone
		12.4.3 Flow of Tourists Visiting Ine Town
		12.4.4 Trends in the Use of Ine Town Parking Lot
		12.4.5 Categorization of Tourism Based on Survey Response Data
		12.4.6 Understanding Tourist Movements Through a Combination of Wi-Fi Tracking Data and Other Data
	12.5 Conclusion
	12.6 Future Research
	References
13 Use of UAVS, Computer Vision, and IOT for Traffic Analysis
	13.1 Introduction
		13.1.1 Road Safety in the Roundabouts
		13.1.2 Accidents in Roundabouts
		13.1.3 Objectives with IoT for Traffic Analysis
	13.2 Case Study and Experimental Setup
		13.2.1 Description
		13.2.2 Speed Control
		13.2.3 Hardware
			13.2.3.1 The Air System
			13.2.3.2 The Ground System
	13.3 Methodology
		13.3.1 Infrastructure Information
		13.3.2 Information of Moving Vehicles
	13.4 Results
		13.4.1 Analysis of Trajectories
			13.4.1.1 Trajectory 1
			13.4.1.2 Trajectory 2
			13.4.1.3 Trajectory 3
			13.4.1.4 Trajectory 4
			13.4.1.5 Trajectory 5
		13.4.2 Analysis of Average Speeds
		13.4.3 Analysis of Instantaneous Speeds
		13.4.4 Vehicle Counting and Classification
		13.4.5 Traffic Density Analysis
		13.4.6 Trouble Spots Inside the Roundabout
	13.5 Conclusions
	References
Index