Introduction to internet of things in management science and operations research : implemented studies

Preface
Introduction
Contents
About the Editors
Internet of Things: Technologies and Applications
	1 Introducton
	2 State-of-the-Art Technologies and Applicatons of IoT
		2.1 Technologies
		2.2 Internet of Things in Smart Cities
			2.2.1 Smart Lighting System
				Lamp Unit (LU)
				Local Control Unit (LCU)
				Control Center (CC)
			2.2.2 Waste and Garbage Management
		2.3 Internet of Things in Smart Homes Domain
		2.4 Internet of Things in Agriculture Domain
			2.4.1 Sampling and Mapping of Soil
			2.4.2 Irrigation
			2.4.3 Fertilizer
			2.4.4 Crop Disease and Pest Management
			2.4.5 Crop Monitoring, Forecasting, and Harvesting
		2.5 Industrial Internet of Things (IIOT)
		2.6 Internet of Things in Energy Conservation
		2.7 Internet of Things in Healthcare
			2.7.1 IoT Healthcare Applications
				Monitoring of Blood Glucose Level
				Electrocardiogram Monitoring
				Blood Pressure Monitoring
				Body Temperature Monitoring
				Monitoring of Blood Oxygen Saturation
				Rehabilitation System
				Wheelchair Management
	3 Case Studies
		3.1 Efficient Water Supply and Management
			3.1.1 Water Usage and Management: City of Surrey, Canada
		3.2 IoT in Traffic Control and Management
			3.2.1 Dallas´ Traffic Management System, Texas
			3.2.2 Management of Railway in Germany
		3.3 Garbage and Waste Management
			3.3.1 A Case Study: Pennsula Santary Servce Inc. (PSSI) Implements Contaner Montorng Sensors
			3.3.2 How One Waste Hauler Implemented Image-Based Container Sensors to Manage Inventory and Streamline Services
				The Challenge
				The Solution
				The Results
		3.4 Smart and Efficient Parking
			3.4.1 Smart Parking System in Burlington, Canada
			3.4.2 Solution for Guided Parking
				Services End-to-End
				Advantages of the Result
	4 Conclusion and Future Prospects of IoT
	References
Prescriptive Analytics in Internet of Things with Concentration on Deep Learning
	1 Introduction
	2 Literature Review
		2.1 Internet of Things (IoT)
		2.2 IoT Architecture
			2.2.1 Coding Layer
			2.2.2 Perception Layer
			2.2.3 Network Layer
			2.2.4 Middleware Layer
			2.2.5 Application Layer
			2.2.6 Business Layer
		2.3 Automated Controlling
		2.4 Data Analytics
		2.5 Information Sharing
	3 Deep Learning
		3.1 Deep Belief Network (DBN)
		3.2 Convolutional Neural Network (CNN)
		3.3 Artificial Neural Network (ANN)
		3.4 Deep Neural Network (DNN)
	4 Prescriptive Analytics
		4.1 Descriptive Analytics
		4.2 Predictive Analytics
		4.3 Prescriptive Analytics
		4.4 Detective Analytics
		4.5 Cognitive Analytics
	5 Methodology
	6 Prescriptive Analytics in IoT Through Deep Learning
	7 Research Model and Discussion
		7.1 Deep Learning and Prescriptive Analytics
		7.2 Optimization Algorithms and Prescriptive Algorithms
		7.3 Prescriptive Analytics and Exploitation Capability
		7.4 Exploitation Capability and Firms´ Performance
	8 Conclusion
	9 Future Research
	References
IoT Security and New Trends of Solutions
	1 IoT Security Foundation
	2 IoT Security in Perception Layer
		2.1 RFID Security
		2.2 Sensor and Sensor Network Security
	3 IoT Security in Network Layer
	4 IoT Security in Application Layer
		4.1 General Application Layer Security
		4.2 Cloud Computing Security
	5 Case Studies About Future IoT Attacks
		5.1 Automotive IoT Security
		5.2 Healthcare IoT Security
		5.3 Drone IoT Security
	6 Solutions for the Future of IoT Security
		6.1 Blockchain-Based Security Solution
		6.2 AI-Based Security Solution
	7 Conclusion
	References
A Hybrid Model of Learning Methodology Analyzed Through the Use of Machine Learning Techniques
	1 Introduction
	2 The Learning Hybrid Model Methodology
	3 Canvas Learning Platform and Data Description
	4 Machine Learning Techniques for Learning Analytics
		4.1 Naïve Bayes Classifier
		4.2 Binary Logistic Regression
		4.3 Artificial Neural Networks
		4.4 Hierarchical Agglomerative Clustering (HAC)
		4.5 Multiple Linear Regression Model
		4.6 Factor Analysis Using Principal Components
	5 Empirical Results
		5.1 Naïve Bayes Classifier
		5.2 Binary Logistic Regression
		5.3 Artificial Neural Networks
		5.4 Hierarchical Agglomerative Clustering
		5.5 Multiple Linear Regression Model
		5.6 Factor Analysis Using Principal Components
		5.7 Summary Results
	6 Conclusions
	References
Internet of Things Cybersecurity: Blockchain as First Securitisation Layer of an IoT Network
	1 Introduction
	2 IoT Cybersecurity Problems
	3 Blockchain Main Concepts and Cybersecurity Problems
	4 IIoT + Blockchain, an Affordable Combination
	5 Study Cases
		5.1 Smart Home
		5.2 Smart Home Cluster for Security Enterprises
		5.3 Smart Updates for IoT Devices
	6 IIoT + Blockchain, Sum-Up Table
	7 Conclusion
	8 Future Research
	References
A Data-Driven Traffic-Responsive Signal Control for a Smart City Road Network Under Uncertainty
	1 Introduction
	2 A Period-Dependent User Equilibrium with Stochastic Travel Delay
		2.1 Notation
		2.2 A Data-Driven Period-Dependent Travel Delay
		2.3 A User Equilibrium Expressed as a Complementarity Problem
	3 A PDSS Traffic-Responsive Signal Control
		3.1 Constraints for Period-Dependent Traffic Signals
		3.2 A Mathematical Program with Equilibrium Constraints (MPEC)
		3.3 A Stochastic MPEC (SMPEC)
	4 Solution Method
		4.1 A Smoothed SMPEC
		4.2 Sensitivity Analysis
		4.3 Two-Stage Solution Approach
			4.3.1 Stage I: A Linear Program for Period-Dependent PI Maximum
			4.3.2 Stage II: A Bundle-Like Approach for Period-Dependent PI Minimum
		4.4 A Level Set Bundle (LSB) Method
		4.5 Solution Scheme
	5 Numerical Experiments and Results
		5.1 Input Data
		5.2 Computational Results
		5.3 Numerical Comparisons with Data-Driven Signal Controls
	6 Conclusions and Remarks
	References
Utilization of Consumer Appliances in Smart Grid Services for Coordination with Renewable Energy Sources
	1 Introduction
	2 Theoretical Background
		2.1 Unit Commitment and Renewable Power Sources
		2.2 Consumer Participation in Smart Grid Services and Demand Response
		2.3 Ecological and Environmental Aspects of Demand Response
	3 A Model of Household Participation in Demand Response
		3.1 Business Model
		3.2 IoT Infrastructure
		3.3 Blockchain Infrastructure and Loyalty Management
	4 Assessing Readiness for Participation in Demand Response
		4.1 Design, Procedure and Methods
		4.2 Results
	5 Conclusion
	References
Application of Internet of Things (IoT) to Demand-Side Management in Smart Grids
	1 Introduction
	2 Demand-Side Management
	3 Utilizing IoT in Smart Grid Demand-Side Management
		3.1 Smart Grid Concept
		3.2 Smart Grid Challenges
		3.3 IoT in Smart Grid
		3.4 Smart Home
	4 IoT-Enabled SHs Algorithms Targeting DSM
	5 Conclusions
	References
Understanding the Factors Influencing Consumers´ Behaviour Towards Autonomous Vehicles Adoption
	1 Introduction
	2 State of the Art Review
		2.1 Brief History and Background of Autonomous Vehicles
		2.2 Autonomous Vehicles as a Disruptive Technology
		2.3 Concept Underlying User Acceptance
		2.4 Key Challenges to Autonomous Vehicles Acceptance
			2.4.1 Safety
			2.4.2 Legal Implications
			2.4.3 Social and Ethical Issues
			2.4.4 Technology
			2.4.5 Infrastructure
			2.4.6 Cost
			2.4.7 Cyber Security
		2.5 Existing Studies of User Acceptance of AVs
			2.5.1 Safety Benefits
			2.5.2 Time Savings
			2.5.3 Fuel Savings
	3 Formulating the Autonomous Vehicles Technology Acceptance Model (AVTAM)
		3.1 Performance Expectancy (PE)
		3.2 Effort Expectancy (EE)
		3.3 Social Influence (SI)
		3.4 Self-Efficacy (SE)
		3.5 Perceived Safety (PS)
		3.6 Anxiety (AX)
		3.7 Trust (T)
		3.8 Legal Regulation (LR)
		3.9 Hedonic Motivation (HM)
		3.10 Price Value (PV)
		3.11 Behavioural Intention (BI)
	4 Method
		4.1 Reliability Analysis
		4.2 Correlation Analysis
		4.3 Regression Analysis
		4.4 Structure Equation Modelling (SEM)
	5 Results
	6 Conclusion and Future Work
	References
Development Path, Experience and Implications of the Internet of Things Industry in Wuxi, Jiangsu Province, China
	1 Introduction
	2 Analysis of the Development Path of the IoT Industry in Wuxi
		2.1 Initiation Stage (2009-2012)
			2.1.1 Determination of the Development Orientation
			2.1.2 Actively Taking Actions
		2.2 Development Stage (2013-2015)
			2.2.1 Forming Industrial Layout of IoT with One Core and Multiple Key Areas
			2.2.2 Constantly Improving Support by R&D Institutions and Public Service Platforms
			2.2.3 Constantly Expanded Application Demonstration Fields
		2.3 Rapid Growth Stage (2016-2019)
			2.3.1 Agglomerative Development Stage of the IoT Industry
			2.3.2 Transition from Government-Dominated to Market-Dominated Application Mode
			2.3.3 Rapid Development of Key Enterprises
	3 Development Experience and Implications of the IoT Industry in Wuxi City
		3.1 Adhering to the Coordinated Driving to Foster the Development Mode Under the Coupling Driving of the Government and Market
		3.2 Adhering to Taking Well-Targeted Steps and Building an Open, Collaborative and Sharing Industrial Ecology
		3.3 Persisting Demand Guidance and Cultivating the Innovation Culture of Fault Tolerance, Trial and Error and Error Correction
	4 Existing Problems in the Development Process of the IoT Industry in Wuxi
		4.1 Intensively Dominated by the Government and Still in the Partially Market-Dominated Stage
		4.2 Being Urgent to Make Breakthroughs in Core Technologies and Strengthening the Standardization Construction
		4.3 Impeded Connection Between Technologies and the Market and Weak Driving Force of Key Enterprises
	5 Countermeasures for Promoting the Sustainable Development of the IoT Industry in Wuxi
		5.1 Establishing a Dual Mechanism of Combining the Government Regulation with the Market Regulation
		5.2 Building a Dual Driving Mechanism of Combining Technological Innovation and Financial Innovation
		5.3 Building a Dual-Chain Integration Mechanism of the Innovation and Industry Chains
	References
Use of Convolutional Neural Networks for Quality Control in Automotive Industry
	1 Introduction
	2 Modeling and Approach
	3 Case Study
	4 Results
	5 Conclusion
	References
Using Other Multi-Attribute Decision-Making Techniques to Measure Efficiency When Data Envelopment Analysis Fails
	1 Introduction
	2 Data Envelopment Analysis
		2.1 Description and Uses
		2.2 Methodology
		2.3 Strengths and Limitations to DEA
		2.4 Illustrative Examples
	3 Multi-Attribute Decision-Making
		3.1 The Technique of Order Preference by Similarity to Ideal Solution (TOPSIS)
		3.2 TOPSIS Methodology and Efficiency Modification
			3.2.1 Step 1
			3.2.2 Step 2
			3.2.3 Step 3
			3.2.4 Step 3a
			3.2.5 Step 3b
			3.2.6 Step 4
			3.2.7 Step 5
			3.2.8 Step 6
			3.2.9 Step 7
			3.2.10 Step 8
	4 Entropy Weighting Scheme
	5 Illustrative Example
	6 Conclusions
	References
Comprehensive Potential Evaluation for the Rooftop PV Development Based on IPO
	1 Introduction
	2 Background
		2.1 Literature Review
			2.1.1 Rooftop PV Potential Evaluation
			2.1.2 Methods of Comprehensive Evaluation
			2.1.3 Application of IPO
		2.2 Problem Description and Research Framework
	3 Research Methodology
		3.1 Evaluation Index System
			3.1.1 Literature Research
			3.1.2 Internet Public Opinion
		3.2 Data Processing
			3.2.1 Economic Potential Value
				Net Present Value (NPV)
				Internal Rate of Return (IRR)
			3.2.2 Technical Potential Value
			3.2.3 Environmental Potential Value
		3.3 Modeling Process
			3.3.1 Weight Definition
			3.3.2 Comprehensive Potential Evaluation
	4 Case Study
		4.1 Research Case Area
		4.2 Calculation Result of PV Generation Potential
			4.2.1 Technical Potential Value
			4.2.2 Environmental Potential Value
			4.2.3 Economic Potential Value
		4.3 Evaluation Results of Comprehensive Potential
			4.3.1 Calculation of Index Weight
			4.3.2 Comprehensive Potential Evaluation Analysis
		4.4 Result Analysis and Discussion
			4.4.1 Subsystem Potential Value
			4.4.2 Comprehensive Potential Value
		4.5 Strategy Suggestion
			4.5.1 Management Suggestion
			4.5.2 Proposition
	5 Conclusions and Future Research
	References
Index