دانلود کتاب Internet of Things: Cases and Studies

فهرست مطالب :

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