Information and Knowledge in Internet of Things (EAI/Springer Innovations in Communication and Computing)

Preface
Contents
Part I IoT Knowledge Management
	1 Data Science and Advanced Analytics in Commercial Pharmaceutical Functions: Opportunities, Applications, and Challenges
		1.1 Introduction and Conceptualization
		1.2 Preliminary Literature Review
			1.2.1 Data Science in a Commercial Pharmaceutical Context
			1.2.2 Advanced Analytics in a Commercial Pharmaceutical Context
			1.2.3 The Value of Data in Pharmaceuticals
		1.3 Methodologies
			1.3.1 Systematic Literature Review
				1.3.1.1 Search and Selection
				1.3.1.2 Results
				1.3.1.3 Co-Authorship and Co-Occurrence Data Analysis
			1.3.2 Focus Group
				1.3.2.1 Focus Group Questions
				1.3.2.2 Focus Group Results and Process Conclusions
				1.3.2.3 Focus Group Conclusion
		1.4 Conclusions
			1.4.1 Practical Implications
			1.4.2 Limitations
			1.4.3 Conclusions
		References
	2 Smart TV-Based Lifelogging Systems: Current Trends, Challenges, and the Road Ahead
		2.1 Introduction
		2.2 Related Work
			2.2.1 Desktop-Based Life-logging Systems
			2.2.2 Smartphone-Based Lifelogging Systems
			2.2.3 Wearable Device-Based Life-logging Systems
			2.2.4 Smart TV-Based Lifelogging Systems
		2.3 Applications of Smart TV-Based Lifelogging Systems
			2.3.1 Viewers Watching Behaviors
			2.3.2 Social Context of Viewers
			2.3.3 Viewers Memory Augmentation
			2.3.4 Viewers Monitoring
			2.3.5 Viewers Study Behaviors
			2.3.6 Viewer/User Modelling
			2.3.7 Context-Aware Recommendations
			2.3.8 E-Learning
			2.3.9 E-Commerce
			2.3.10 Building Viewer Adaptive UI
			2.3.11 Viewer Health Issues
			2.3.12 User Interface (UI) Testing
			2.3.13 Accessibility
			2.3.14 Usability Tests
			2.3.15 Psychological Studies
			2.3.16 Serious Games
		2.4 Issues and Challenges of Smart TV-based Lifelogging System
			2.4.1 Updating User Profile Issue
			2.4.2 User Privacy and Security Issues
			2.4.3 User Preferences
			2.4.4 User\'s Characteristics/User\'s Taste
			2.4.5 Storage (Local and Cloud-Based)
			2.4.6 Processing and Presentation Issue
			2.4.7 Accessing and Searching Issue
		2.5 Recommendation and Research Guidelines
			2.5.1 Updating a User Profile
			2.5.2 The Security and Privacy Issues
			2.5.3 Preferences of Each User
			2.5.4 Exact Identity of Viewers
			2.5.5 Removal of Irrelevant Data
		2.6 Conclusion and Future Work
		References
	3 Knowledge Management in Marketing
		3.1 Introduction
		3.2 Methodological Approach
		3.3 Literature Analysis: Themes and Trends
		3.4 Literature Analysis: Themes and Trends
			3.4.1 Developed and Developing Context
			3.4.2 Marketing and Production and Operations
			3.4.3 Boundary Spanners
			3.4.4 Innovation
			3.4.5 Branding
			3.4.6 Media and Content
			3.4.7 Dynamic Capabilities and Conceptual Maps
		3.5 Conclusion
		Annexes
		References
	4 Game-Based Interventions as Support for Learning Difficulties and Knowledge Enhancement in Patients with Dyslexia: A Systematic Literature Review
		4.1 Introduction
		4.2 Related Work
		4.3 Research Methods
			4.3.1 Research Question
			4.3.2 Electronic Databases
			4.3.3 Search Terms and String
			4.3.4 Study Search Procedure
			4.3.5 Inclusion Criteria and Exclusion Criteria
			4.3.6 Quality Assessment Criteria
		4.4 Discussion & Results
		4.5 Conclusion
		References
	5 Knowledge and Data Acquisition in Mobile System for Monitoring Parkinson\'s Disease
		5.1 Introduction
		5.2 Background
		5.3 Base Components of Knowledge and Data Acquisition
			5.3.1 The MeCo System Components
			5.3.2 Knowledge and Data Acquisition Pipeline
				5.3.2.1 Data Collection
				5.3.2.2 Data Processing
				5.3.2.3 Data Analysis
				5.3.2.4 Data Fusion
		5.4 Symptom Score Methodology Via Sensor Data
			5.4.1 Segmentation and Feature Extraction
			5.4.2 Symptoms Estimation
			5.4.3 Symptom Scoring Based on Decision Fusion
		5.5 An Example for Accelerometer Data Processing
			5.5.1 Data Collection
			5.5.2 Data Processing
				5.5.2.1 Segmentation
				5.5.2.2 Processing
			5.5.3 Classification
			5.5.4 Fusion Strategy
		5.6 Conclusion and Future Work
		References
	6 How to Manage Knowledge within Hotel Chains in the Era of COVID-19
		6.1 Introduction
		6.2 Literature Review
			6.2.1 Knowledge Management
			6.2.2 Knowledge Sharing Applied to Hotel Sector
			6.2.3 Knowledge Management in Times of Crisis
		6.3 Methodology
			6.3.1 Bibliometric Analysis
			6.3.2 Case Study
			6.3.3 Operationalization of Implicit and Explicit Knowledge
		6.4 Results
			6.4.1 Bibliometric Analysis
			6.4.2 Case Study and Implicit and Explicit Knowledge
		6.5 Conclusion
		References
Part II Decision Support Systems in IoT
	7 An Efficient Supervised Machine Learning Technique for Forecasting Stock Market Trends
		7.1 Introduction
			7.1.1 Problem Formulation
			7.1.2 Research Contributions
		7.2 Literature Review
		7.3 Proposed Methodology
			7.3.1 Dataset Collection
			7.3.2 Data Normalization
			7.3.3 Applying K-Nearest Neighbor Classifier
		7.4 Results and Discussion
			7.4.1 Answer to RQ.1: “How to Apply K-Nearest Neighbor for Prediction Stock Trend?”
				7.4.1.1 Why KNN?
			7.4.2 Answer to RQ.2: How to Minimize Data Sparseness in the Acquired Dataset Using Outlier Detection for Efficient Stock Trend Prediction?
				7.4.2.1 Applying KNN Regressor on Raw Dataset
				7.4.2.2 Applying KNN Regressor on Processed Dataset
			7.4.3 Answer to RQ3: What Is the Efficiency of the Proposed Model with Respect to the Other Baseline Methods?
				7.4.3.1 Comparison of Proposed Model with Other Classifiers
				7.4.3.2 Comparison of Proposed Model with Baseline Method
		7.5 Conclusion, Limitations, and Future Work
			7.5.1 Limitations
			7.5.2 Future Work
		References
	8 Artificial Intelligence Trends: Insights for Digital Economy Policymakers
		8.1 Introduction
		8.2 Artificial Intelligence Conceptualization
		8.3 Decision-Making Complexities Linked to AI
		8.4 Methodological Approach
			8.4.1 Description of the Research
			8.4.2 Stages of Data Collection
			8.4.3 Search Strategy
		8.5 Data Analysis and Discussion
			8.5.1 General Characteristics of the Publications
				8.5.1.1 Type of Publications
				8.5.1.2 Main Research Areas
				8.5.1.3 Authors\' Affiliations
				8.5.1.4 Main Sources: Journals and Conferences
				8.5.1.5 Main Countries
				8.5.1.6 Main Funding Institutions
		8.6 Trends in Artificial Intelligence
		8.7 Insights for Policymakers\' Decision Processes
		8.8 Conclusions, Limitations, and Future Research
		References
	9 Methodological Proposal for the Construction of a Decision Support System (DSS) Applied to IoT
		9.1 Introduction
		9.2 Methodology
		9.3 Development of DSS According to Analysis of SLR
		9.4 Proposal Methodology for the Development of DSS
		9.5 DSS Applied to the Internet of Things (IoT)
		9.6 Conclusions
		References
	10 IoT-Based Pervasive Sentiment Analysis: A Fine-Grained Text Normalization Framework for Context Aware Hybrid Applications
		10.1 Introduction
		10.2 Literature Review
			10.2.1 Levels of Sentiment Analysis
			10.2.2 Approaches Towards Opinion Mining and Sentiment Analysis
				10.2.2.1 Machine Learning (ML)
				10.2.2.2 Lexicon-Based Approach
		10.3 Methodology
			10.3.1 Data Extraction
			10.3.2 Noise Reduction
			10.3.3 Stop Word Removal
			10.3.4 Slang Definition
			10.3.5 Stemming and Lemmatization
			10.3.6 Part-of-Speech Tagging
			10.3.7 Coreference Resolution
			10.3.8 Tag Identification
		10.4 Evaluation Parameters
			10.4.1 Contingency Table
			10.4.2 Precision
			10.4.3 Recall
			10.4.4 F-Measure
			10.4.5 Accuracy
		10.5 Conclusion
		References
Part III IoT Sensing Technology and Applications
	11 Stadium 2.0: Framework to Improve Sports Fans\' Experience in Stadium Through IoT Technology
		11.1 Introduction
		11.2 Sports Fans\' Experience in Stadiums
		11.3 IoT to Improve Sports Fans\' Experience in Stadiums
		11.4 The Framework
			11.4.1 Questionnaire
			11.4.2 Classification
			11.4.3 Reference to Implement IoT Technologies in Sports Venues and Benefits to the Sports Fans\' Experience
		11.5 Evaluation
			11.5.1 Results
		11.6 Conclusion
		References
	12 Smartphone-Based Lifelogging: Toward Realization of Personal Big Data
		12.1 Introduction
		12.2 Lifelogging: Background
			12.2.1 History of Lifelogging
			12.2.2 Lifelogging via Desktop Computing
			12.2.3 Lifelogging via Wearable Computing
		12.3 Smartphone-Based Lifelogging
			12.3.1 Smartphone Technological Developments
			12.3.2 Context Sensing
				12.3.2.1 Passive Video and Audio Contexts
			12.3.3 Smartphone Versus Dedicated Lifelogging Devices
			12.3.4 Smartphone-Based Lifelogging Research
		12.4 Smartphone-Based Lifelogging: Personal Big Data
			12.4.1 General Architecture for Smartphone-Based Lifelogging
			12.4.2 Personal Big Data Applications
			12.4.3 Opportunities and Challenges
			12.4.4 Future Directions/Recommendations
				12.4.4.1 Smartphone Limitations for Lifelogging
				12.4.4.2 Scope of Smartphone-Based Lifelogging
				12.4.4.3 Unit Identification
				12.4.4.4 Data Analysis and Semantic Extraction
				12.4.4.5 Annotating Events
				12.4.4.6 Semantic Organization
				12.4.4.7 Use-Cases and Retrieval Tools
				12.4.4.8 Anonymization of Lifelog Personal Big Data
				12.4.4.9 Humanizing Technology
		12.5 Conclusion
		References
	13 Development of a Mobile IoT Device for Supervision and Alert BPM Problems
		13.1 Introduction
		13.2 IoT as a Solution for the Supervision and Alert BPM System
		13.3 Content
			13.3.1 Heart Pulse Oximeter Sensor
			13.3.2 Filter and Amplification of the Signal Captured by the Sensor
			13.3.3 Atmel Microcontroller
			13.3.4 Communication Module
			13.3.5 IoT ThingSpeak Platform
		13.4 Methodology
			13.4.1 System Algorithms
			13.4.2 Device Design and Dimensions
			13.4.3 Configuration of Parameter
		13.5 Results
			13.5.1 BPM Data Comparison
			13.5.2 Energy Consumption
			13.5.3 Alert Messages
			13.5.4 IoT Platform and Data Send
			13.5.5 Mobile Data Consumption
		13.6 Conclusion
		References
	14 Evaluation of Data Transfer from PLC to Cloud Platforms-Based Real-Time Monitoring Using the Industrial Internet of Things
		14.1 Introduction
		14.2 Proposed System
			14.2.1 Hardware Description
			14.2.2 Software Description
			14.2.3 Communication System Description
		14.3 Content Development
			14.3.1 System Devices
			14.3.2 Protocols of Communication
		14.4 Methodology
		14.5 Results
		14.6 Conclusion
		References
	15 Relationship of Body Mass Index to Body Composition and Somatotype of Infantry Personnel from the Ecuadorian Air Force
		15.1 Introduction
		15.2 Methodology
			15.2.1 Body Mass Index (BMI)
			15.2.2 Body Composition
			15.2.3 Somatotype
			15.2.4 The Applicability of the Internet of Things (IoT)
		15.3 Results
		15.4 Discussion and Conclusions
		References
Part IV Smart Environments
	16 Water Management in the Territorial Development Organization Plans of the Provinces of Bolívar and Cañar
		16.1 Introduction
		16.2 Methodology
		16.3 Results
			16.3.1 River Situation
			16.3.2 Use of Water, Drinking Water Cover, and Sanitation
			16.3.3 Environmental Management
		16.4 Discussion of Results
			16.4.1 River Situation
			16.4.2 Use of Water, Drinking Water Cover, and Sanitation
			16.4.3 Environmental Management
		16.5 Conclusion
		References
	17 IoT-Based Smart Agriculture and Poultry Farms for Environmental Sustainability and Development
		17.1 Introduction
		17.2 Related Works
		17.3 IoT Open Issues and Challenges
		17.4 Case Studies
			17.4.1 Poultry Farming Applications
				17.4.1.1 Network Topology
				17.4.1.2 Implemented Experimental Design
				17.4.1.3 Obtained Results
				17.4.1.4 Communication
				17.4.1.5 Sampling Frequency
				17.4.1.6 Data Processing
			17.4.2 Tree Growth Analysis, Oil Palm Plantation, Using Deep Learning Techniques from a Case Study
				17.4.2.1 Methodology
				17.4.2.2 Results
		17.5 Future Research Direction
		17.6 Conclusion and Recommendations
		References
	18 Conceptualization of a Dialectic Between an Internet of Things System and Cultural Heritage
		18.1 Introduction
		18.2 LOCUS\' Project Description Considering the Importance of Cultural Heritage and IoT
		18.3 Internet of Things, Cultural Heritage, and Smart Territories
		18.4 Methodology
			18.4.1 Amiais\' Cultural Heritage Survey
			18.4.2 Amiais\' Cultural Heritage Results
				18.4.2.1 Cultural Heritage and Playfulness
				18.4.2.2 Labor Dimension
				18.4.2.3 Emotional Connections/Families
				18.4.2.4 Religion Dimension
			18.4.3 Appealing Cultural Heritage: IoT and Smart Territories
		18.5 Conclusion
		References
Part V Security and Privacy
	19 Participative Sensing Challenges
		19.1 Introduction
		19.2 Remote Sensing
		19.3 Urban Sensing
		19.4 Participative Sensing
			19.4.1 Smart City Participative Sensing
			19.4.2 Participative Sensing Systems
			19.4.3 Participative Sensing Networks
		19.5 Healthcare Participative Sensing
		19.6 Conclusion
		References
	20 Novel Heuristic Scheme to Enforce Safety and Confidentiality Using Feature-Based Encryption in Multi-cloud Environment (MCE)
		20.1 Introduction
		20.2 Background
		20.3 The Goal of the Novel Scheme
		20.4 The Extent of the Feature-Based (FB) Scheme
			20.4.1 Foreword of the FB Scheme
			20.4.2 Development of Confidentiality Scheme
		20.5 Problems Concerning Cloud Safety
			20.5.1 Multi-cloud Framework
			20.5.2 Duplication of Applications
			20.5.3 Separation of Application System into Layers
			20.5.4 Separation of Application into Pieces
		20.6 Novel Feature-Based Encryption
			20.6.1 Method of Structuring the Cloud Environment
			20.6.2 Cloud Scheme
				20.6.2.1 Feasibility
			20.6.3 Information Separation by Cryptography
				20.6.3.1 Repository System
		20.7 Examination of FB Scheme
			20.7.1 Time of Creation
			20.7.2 Directory Storage Space
			20.7.3 Time for Locating a Data
			20.7.4 Accuracy
		20.8 Rundown of Conventional Cloud Schemes
		20.9 Conclusion
		References
	21 From the Traditional Police Model to Intelligence-Led Policing Model: Comparative Study
		21.1 Introduction
		21.2 Theoretical Background
			21.2.1 Traditional Policing Model
				21.2.1.1 Advantages and Disadvantages
			21.2.2 Intelligence-Led Policing
				21.2.2.1 Advantages and Disadvantages
			21.2.3 Policing in the Republican National Guard
		21.3 Materials and Methods
			21.3.1 Research Question and Instruments
			21.3.2 Participants and Data Analysis (Dimensions and Variables)
		21.4 Results
			21.4.1 Data Demonstration
				21.4.1.1 The Policing Model Implemented by the Republican National Guard (Tables 21.2, 21.3, and 21.4)
				21.4.1.2 Requirements for the Implementation of Intelligence-Led Policing (Tables 21.5 and 21.6)
				21.4.1.3 Comparison of the Models Under Analysis (Table 21.7)
				21.4.1.4 Requirements for the Implementation of Intelligence-Led Policing in the Republican National Guard (Tables 21.8, 21.9, 21.10, 21.11, 21.12, and 21.13)
			21.4.2 Descriptive Analysis
		21.5 Discussion
		21.6 Conclusions and Recommendations
		References
Index