Anmerkung: Intro -- Preface -- Contents -- About the Editor -- Introduction and Background of FDA -- Introduction -- 1 Introduction -- 1.1 Internet of Things (IoT) Applications -- 1.2 Fog Computing and Its Role in FDA -- 1.3 Process Model for FDA -- 1.4 FDA Attributes for IoT Applications -- 1.5 Classification for FDA in IoT Application -- 1.6 FDA Research Challenges and Future Direction -- 2 Conclusion -- References -- Introduction to Fog Data Analytics for IoT Applications -- 1 Introduction -- 1.1 Formally Defining Fog Computing -- 1.2 Cisco Vision of Fog Computing -- 2 Role of Fog Computing in IoT Applications -- 3 Why Use Fog Computing -- 4 Architecture of Fog Computing -- 5 How Fog Computing Works? -- 6 Fog Node -- 7 Characterization of Fog Computing -- 8 Fog Computing Versus Cloud Computing -- 8.1 Main Differences Between Fog and Cloud Paradigm -- 9 Edge Computing Versus Fog Computing -- 10 Fog Computing Advantages and Disadvantages -- 11 Fog Computing Applications -- References -- Fog Data Analytics: Systematic Computational Classification and Procedural Paradigm -- 1 Introduction -- 2 Literature Survey -- 3 Taxonomy of Fog Data Analytics -- 4 Case studies of Fog Data Analytics -- 5 Conclusion -- References -- Fog Computing: Building a Road to IoT with Fog Analytics -- 1 Introduction to Fog Computing -- 1.1 IoT Driven Economy and Its Challenges -- 1.2 Fog Computing and Cloud Comparison -- 2 Fog Computing as Solution -- 2.1 Definition of Fog Computing -- 2.2 Fog Computing Platform -- 2.3 Fog Computing: Characteristics -- 2.4 Fog Computing: Architecture -- 2.5 ParStream -- 3 Layers in Fog Computing Architecture -- 3.1 Present Challenges -- 4 Application Management in Fog -- 4.1 Latency-Aware Application Development -- 4.2 Distributed Application Development -- 5 Fog Analytics -- 5.1 Introduction. , 5.2 Fog Computing, Stream Data Analytics, and Big Data Analytics -- 5.3 Machine Learning for Fog Ecosystem -- 5.4 Distributed Parallel Association Rule Mining Techniques for Big Data Scenario -- 5.5 Dynamic Association Mining -- 6 Deep Learning and Big Data -- 7 Approaches for Fog Analytics -- 8 Research Directions -- 9 Case Study -- 10 Conclusion -- References -- Data Collection in Fog Data Analytics -- 1 Introduction -- 2 Methods of Collecting Data -- 3 Optimized Collection of Compressive Data -- 4 Management of Big Data -- 5 Case Studies -- 5.1 Data Collection in Moving Vehicles -- 5.2 Fog Computing in Industrial Automation -- 5.3 Collection of Data Under Water -- 5.4 Water Conservation in Agriculture Using Fog and IoT -- 5.5 IoT Implementation for Collection of Data Using QR Codes -- 5.6 Indoor Air Quality Monitoring Using IoT and Fog -- 5.7 Emotional Profiles -- 5.8 Health Monitoring System Using Fog Computing -- 5.9 Collecting Data Related to Elderly Behaviour -- 5.10 Telehealth Big Data Through Fog Computing -- 5.11 BLE-Based Data Collection -- 5.12 Safety Management System for Miners -- 5.13 Healthcare 4.0 -- 5.14 Comparison on Case Studies -- 6 Conclusions -- References -- Emerging Technologies and Architecture for FDA -- Mobile FOG Architecture Assisted Continuous Acquisition of Fetal ECG Data for Efficient Prediction -- 1 Introduction -- 2 Motivation -- 3 Fetal ECG Analysis and Synthesis -- 3.1 Data Extraction -- 3.2 Pre-processing and Generation of ECG Signal -- 3.3 Fetal ECG Extraction Using Adaptive Noise Cancellation -- 3.4 LMS Extraction -- 3.5 QRS Peak Detection -- 4 Mobile FOG Enabled Architecture -- 5 Design and Implementation -- 6 Results -- 7 Discussion and Outcome -- 8 Conclusion -- References -- Proposed Framework for Fog Computing to Improve Quality-of-Service in IoT Applications -- 1 Introduction -- 2 Cloud Computing. , 3 Fog Computing -- 3.1 Layered Architecture of FC -- 3.2 Challenges of FC -- 4 Data Analytics in FC -- 4.1 Fog Analytics -- 5 Resource Scheduling in FC -- 6 Proposed Framework -- 7 Conclusion and Future Work -- References -- Fog Data Based Statistical Analysis to Check Effects of Yajna and Mantra Science: Next Generation Health Practices -- 1 Introduction -- 1.1 Different Diseases -- 1.2 Machine Vision -- 1.3 Yajna Science and Cure for Different Diseases -- 1.4 Mantra Science -- 1.5 Effects of Yajna and Mantra on Human Health -- 1.6 Role of Technology in Addressing the Problem of Integration of Healthcare System -- 1.7 Impact of Yagya in Reducing the Atmospheric Pollution -- 2 Literature Survey -- 3 Methodology -- 4 Result and Discussion -- 5 Analysis of Fasting Blood Sugar Parameter (FBS) -- 6 Novelty in Our Work -- 7 Recommendations -- 8 Future Scope and Possible Applications -- 9 Limitations -- 10 Conclusions -- References -- Role of IoT in FDA -- Process Model for Fog Data Analytics for IoT Applications -- 1 Need for Fog Computing -- 2 Fog Computing -- 2.1 Advantages of Fog Computing -- 2.2 Cloud-vs-Edge-vs-Fog Computing -- 3 Fog Computing Architecture -- 4 Taxonomy and Process Model for FDA -- 4.1 Data Collection -- 4.2 Fog Nodes Formation -- 4.3 Fog Nodes Connection -- 4.4 Data Storage -- 4.5 Data Analytics -- 4.6 Data Security -- 5 Process Model -- 5.1 Transport IoT Data Through Fog Node -- 6 Challenges -- 7 Case Study -- 7.1 IoT Enabled E-Health Monitoring System (ECG Monitoring Device) Architecture with Fog Layer and Cloud -- 8 Different Tools for Implementation of Fog Computing -- 9 Conclusion -- References -- Medical Analytics Based on Artificial Neural Networks Using Cognitive Internet of Things -- 1 Introduction -- 1.1 Problem Statement -- 1.2 Objectives -- 2 Literature Review -- 2.1 HealthSense -- 2.2 Case Study. , 2.3 Scope of Presented Work -- 2.4 IBM Watson -- 3 Artificial Intelligence and Machine Learning -- 3.1 Artificial Intelligence -- 3.2 Types of Artificial Intelligence -- 3.3 Examples of Artificial Intelligence Systems -- 3.4 Artificial Intelligence Applications -- 3.5 Artificial Intelligence Elements -- 3.6 Machine Learning -- 3.7 Putting Machine Learning to Work -- 3.8 Risks and Limitations -- 3.9 Machine Learning Methods in Healthcare -- 3.10 Support Vector Machines -- 3.11 Artificial Neural Networks -- 4 Methodology -- 4.1 Cognitive Radio Architecture -- 4.2 WBAN: Wireless Body Area Network Architecture -- 4.3 Cognitive Remote Patient Monitoring -- 4.4 CogRPM System -- 4.5 Proposed System Architecture -- 4.6 Communication Protocols for Cognitive-Based RPM System -- 4.7 Priority Wait and Scheduling for CRN -- 5 Results -- 5.1 User-Friendly GUI -- 5.2 Cognitive Radio Results -- 5.3 SVM and Linear Regression ML Algorithm -- 5.4 Neural Network Modeling -- 5.5 Heart Disease Detection and Prediction -- 5.6 Chronic Kidney Disease Detection and Prediction -- 6 Conclusion -- References -- Application of IoT-Based Smart Devices in Health Care Using Fog Computing -- 1 Introduction -- 2 Integrated Architecture of Fog Computing and IoT -- 3 Technologies Used in Fog Computing -- 4 Health Care with Fog Computing and IoT -- 5 Fog Computing Based Healthcare Services and Applications -- 6 Issues and Challenges in Fog Computing -- 7 Conclusion -- References -- Data Reduction Techniques in Fog Data Analytics for IoT Applications -- 1 Introduction -- 2 Related Work -- 2.1 Fog Data Analytics and IoT -- 2.2 Challenges of Fog Data Analytics (FDA) for IoT Applications -- 3 Data Reduction Strategies in FDA for IoT -- 3.1 Missing Values Ratio -- 3.2 Low Variance Filter -- 3.3 Principal Component Analysis -- 3.4 Random Forest -- 3.5 Backward Feature Elimination. , 4 Framework for Data Reduction Strategies in FDA -- 4.1 FDA Framework for IoT -- 4.2 FDA for Image Classification with IoT -- 4.3 Summary -- 5 Conclusion -- References -- Security Issues, Research Challenges, and Opportunities -- Background and Research Challenges for Fog Data Analytics and IoT -- 1 Introduction -- 1.1 Background -- 1.2 Current Situation of Cloud Networking and the Need to Change -- 1.3 Introduction to Fog -- 2 Taxonomy of Fog Data Analytics -- 2.1 Components and Connections -- 2.2 Data Collection -- 2.3 Data Storage -- 3 Architecture and Processing -- 3.1 Interaction Between Layers -- 3.2 Interconnectivity of Nodes -- 4 Challenges and Scope of Improvement -- 4.1 Heterogeneity -- 4.2 Adaptability and Mobility -- 4.3 QoS/QoE -- 4.4 Security and Privacy -- 5 Example Use Case -- 5.1 Smart Traffic Light System -- 6 Conclusion and Future Advancements -- References -- Behavior-Based Approach for Fog Data Analytics: An Approach Toward Security and Privacy -- 1 Introduction -- 2 Our Contribution -- 3 Related Work -- 4 Attack Architecture at Various Layers of Fog Computing -- 5 Typing Behavior Parameters -- 6 Experiment and Results -- 6.1 New User Registration -- 6.2 Distance Metrics and Error Types -- 6.3 User Identification -- 6.4 Results -- 7 Case Study -- 8 Conclusion -- References -- Data Security and Privacy Functions in Fog Data Analytics -- 1 Introduction -- 1.1 What is Fog Computing -- 1.2 Characteristics of Fog Computing -- 1.3 CIA -- 1.4 Security Concerns with Respect to Fog Computing -- 2 Privacy and Security Issues -- 2.1 Need for Privacy and Security in the Fog Architecture -- 2.2 Vulnerability of Fog Nodes -- 2.3 Privacy and Security Fog Versus Cloud -- 3 Types of Attacks -- 3.1 Man in the Middle Attack -- 3.2 Authentication -- 3.3 Distributed Denial of Service -- 3.4 Single Point of Failure and Fault Tolerance. , 3.5 Data Privacy Attacks.