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.

Anmerkung: Intro -- Preface -- Contents -- About the Editors -- Communication and Network Technologies -- State of the Art: A Review on Vehicular Communications, Impact of 5G, Fractal Antennas for Future Communication -- 1 Introduction -- 1.1 Vehicular Network Architecture (VNA) -- 1.2 Vehicular Communication Applications (VCA) -- 2 Communications -- 3 Existing Proposals -- 4 Fractal Antennas -- 5 Conclusion -- References -- Energy Enhancement of TORA and DYMO by Optimization of Hello Messaging Using BFO for MANETs -- 1 Introduction -- 1.1 MANET's Route Selection Process -- 1.2 Routing Protocols -- 2 Literature Review -- 3 Problem Formulation and Major Issues -- 4 Proposed Protocol Optimization Using BFOA -- 4.1 Introduction to BFOA -- 4.2 Bacteria Foraging Optimization Algorithm (BFOA) -- 5 Results and Comparison -- 5.1 Implementation Results -- 5.2 Comparisons -- 5.3 Optimization Results -- 6 Result and Conclusion -- References -- Horseshoe-Shaped Multiband Antenna for Wireless Application -- 1 Introduction -- 2 Horseshoe Fractal Design Methodology -- 3 Simulation Setup -- 4 Conclusion -- References -- A Review Paper on Performance Analysis of IEEE 802.11e -- 1 Introduction -- 1.1 Medium Access Control Layer -- 1.2 Distributed Coordination Function (DCF) -- 1.3 Enhanced Distributed Coordination Function (EDCF) -- 2 Literature Review -- 3 Conclusions -- References -- Voice-Controlled IoT Devices Framework for Smart Home -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Hardware Design -- 3.2 System Software -- 4 Analysis -- 5 Conclusion -- References -- Comprehensive Analysis of Social-Based Opportunistic Routing Protocol: A Study -- 1 Introduction -- 2 Research Contributions -- 3 Simulation Setup And Description -- 3.1 ONE (Opportunistic Network Environment) Simulator -- 3.2 Mobility Model and Real-World Traces. , 3.3 Simulation Parameter for Different Movement Model -- 4 Simulation Result -- 5 Conclusion and Future Direction -- References -- An Efficient Delay-Based Load Balancing using AOMDV in MANET -- 1 Introduction -- 2 Related Work -- 3 Delay-Based Load Balancing in MANET -- 4 Experimental Results -- 4.1 Packet Delivery Ratio -- 4.2 Delay -- 4.3 Throughput -- 4.4 Packet Loss Ratio -- 4.5 Normalized Routing Load -- 5 Conclusion -- References -- Metaheuristic-Based Intelligent Solutions Searching Algorithms of Ant Colony Optimization and Backpropagation in Neural Networks -- 1 Introduction -- 2 Swarm Intelligence and Problems Solving -- 2.1 Metaheuristic Algorithms -- 2.2 Ant Colony Optimization Search Algorithm -- 2.3 Neural Networks -- 2.4 Related Works -- 3 Methodology -- 3.1 Mathematical Model of NN Tuning -- 3.2 Algorithm of Tuning Model -- 4 Discussion of Results -- 5 Conclusion -- References -- Evaluating Cohesion Score with Email Clustering -- 1 Introduction -- 2 Related Works -- 2.1 Comparison Table -- 3 Proposed Cohesion Evaluation-Based Cluster System -- 3.1 Problem Formulation -- 3.2 Architecture -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 Evaluation Measure -- 5 Result Analysis and Discussion -- 6 Conclusion -- References -- Congestion Control for Named Data Networking-Based Wireless Ad Hoc Network -- 1 Introduction -- 2 Related Work -- 2.1 Congestion Control for NDN in General -- 2.2 Congestion Control for NDN-Based MANET -- 3 Contribution of This Study -- 4 Standbyme Congestion Control as Suggested Solution -- 4.1 Local Congestion Detection -- 4.2 Hop-by-hop Congestion Notification -- 4.3 Multiple Strategy Congestion Avoidance -- 5 Testbed Design and Facility -- 5.1 Experiment Design and Analysis of Result -- 6 Conclusion and Future Work -- 6.1 Conclusion -- References. , A Comparative Review of Various Techniques for Image Splicing Detection and Localization -- 1 Introduction -- 1.1 Need for Image Forgery Detection -- 1.2 Types of Image Forgery -- 1.3 Image Forgery Detection Techniques -- 2 Methodology -- 3 Literature Survey -- 3.1 Comparative Analysis of the Existing Splicing Localization Techniques -- 4 Conclusion -- References -- Analysis and Synthesis of Performance Parameter of Rectangular Patch Antenna -- 1 Introduction -- 2 Micro Strip Antenna Design -- 3 Experimental Result -- 4 Code for Simulation -- 5 Conclusion -- References -- Advanced Computing Technologies and Latest Electrical and Electronics Trends -- Fog Computing Research Opportunities and Challenges: A Comprehensive Survey -- 1 Introduction -- 2 Working of the System -- 2.1 Fog Nodes -- 2.2 Cloud Platform -- 3 Recent Surveys on Fog Computing -- 4 Application of Fog Computing -- 5 Emerging Challenges -- 6 Advantages/Benefits of Fog Computing -- 7 Fog Computing Simulators -- 8 Conclusion and Future Scope -- References -- IIGPTS: IoT-Based Framework for Intelligent Green Public Transportation System -- 1 Introduction -- 1.1 Motivation -- 1.2 Research Contributions -- 1.3 Organization of the paper -- 2 Related Work -- 3 IIGPTS: System components and architecture -- 3.1 System Components -- 3.2 Architecture of IIGPTS -- 4 Performance Evaluation of IIGPTS -- 4.1 Experimental Setup -- 4.2 Simulation Results -- 5 Conclusions and Future Work -- References -- Integrating the AAL CasAware Platform Within an IoT Ecosystem, Leveraging the INTER-IoT Approach -- 1 Introduction -- 2 Interoperability of IoT Artifacts -- 3 CasAware Project Overview -- 3.1 CasAware Architecture -- 3.2 CasAware Integration-Motivating Scenario -- 4 INTER-IoT Project Overview -- 5 Integrating CasAware into INTER-IoT -- 6 Concluding Remarks -- References. , An IoT-Based Solution for Smart Parking -- 1 Introduction -- 2 Background and Related Work -- 3 Proposal and Construction of the Proposed Parking Solution -- 3.1 System Design -- 3.2 System Calibration -- 4 System Evaluation, Demonstration, and Validation -- 5 Conclusion and Future Works -- References -- Online Monitoring of Solar Panel Using I-V Curve and Internet of Things -- 1 Introduction -- 2 Contribution -- 3 Methodologies -- 4 Hardware Descriptions -- 5 Software Description and IoT Implementation -- 6 Conclusion and Future Scope -- References -- Classification of Chest Diseases Using Convolutional Neural Network -- 1 Introduction -- 2 Related Works -- 2.1 Visual Cortex's Receptive Fields -- 2.2 CNN Architecture Origin -- 2.3 Recognition of Image by CNNs Trained Using Gradient Descent -- 2.4 CNN for Lung Nodule Detection -- 3 Convolutional Neural Network -- 4 Methods -- 4.1 Dataset and Preprocessing -- 4.2 Preprocessing Class Labels -- 4.3 CNN Classification Model -- 5 Conclusion -- References -- Age and Gender Prediction Using Convolutional Neural Network -- 1 Introduction -- 2 Machine Learning -- 3 Related Work and Data -- 4 Methodology -- 4.1 Convolutional Neural Network -- 5 Methods for Proposed Work -- 5.1 Data Collection -- 5.2 Dataset Formation -- 5.3 CNN Formation -- 5.4 Training and Testing -- 6 Implemented Work -- 7 Results -- 8 Conclusion -- References -- Vision-Based Human Emotion Recognition Using HOG-KLT Feature -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 Extraction of Histogram of Oriented Gradient Feature -- 3.2 Kanade-Lucas-Tomasi (KLT) Feature -- 3.3 Random Forest (RF) Classifier -- 3.4 Support Vector Machine (SVM) -- 4 Results and Analysis -- 4.1 Performance Evaluation Metrics -- 4.2 GEMEP Dataset -- 4.3 Experimental Results on Random Forest Classifiers -- 5 Conclusion -- References. , An Analysis of Lung Tumor Classification Using SVM and ANN with GLCM Features -- 1 Introduction -- 2 Tumor Types -- 3 Methodology -- 3.1 Segmentation Method -- 3.2 Feature Extraction -- 3.3 Classification of Extracted Tumor -- 4 Proposed Work -- 4.1 Segmentation -- 4.2 Feature Extraction -- 4.3 Tumor Classification -- 5 Conclusions and Future Work -- References -- Lane Detection Models in Autonomous Car -- 1 Introduction -- 1.1 Property of Road -- 2 Related Work -- 2.1 Mapping from an Image to Affordance -- 2.2 Mapping from Affordance to Action -- 3 Implementation -- 3.1 The Open Racing Vehicle Simulator Evaluation -- 3.2 Qualitative Assessment -- 3.3 Comparison with Baselines -- 4 Visualization -- 5 Conclusions -- References -- Various Noises in Medical Images and De-noising Techniques -- 1 Introduction -- 2 De-noising Techniques -- 3 Methods for De-noising an Image -- 4 Results of Comparison of De-noising Algorithms -- 5 Removing Salt and Pepper Noise -- 6 Removing Speckle Noise -- 7 De-noising Results for Synthetic Data -- 8 Conclusion -- References -- Debunking Online Reputation Rumours Using Hybrid of Lexicon-Based and Machine Learning Techniques -- 1 Introduction -- 2 Related Work -- 3 The Proposed ReputeCheck Model -- 3.1 Data Collection -- 3.2 Pre-processing -- 3.3 Feature Engineering -- 3.4 Classification -- 4 Results -- 5 Conclusion -- References -- A Novel Approach for Optimal Digital FIR Filter Design Using Hybrid Grey Wolf and Cuckoo Search Optimization -- 1 Introduction -- 2 Design Model of FIR -- 3 Hybrid Grey Wolf and Cuckoo Search Optimization -- 4 Simulation Results -- 4.1 Low-Pass Filter -- 4.2 High-Pass Filter -- 4.3 Band-Pass Filter -- 4.4 Band-Stop Filter -- 5 Analysis of Simulation Result -- 6 Conclusion -- References -- Quasi-opposition-Based Multi-verse Optimization Algorithm for Feature Selection -- 1 Introduction. , 1.1 Structuring of the Paper.

Anmerkung: Intro -- Preface -- Contents -- About the Authors -- 1 Introduction to Cybersecurity -- 1 Introduction to Cybersecurity -- 1.1 Introduction -- 1.2 The Necessity of Cybersecurity -- 1.3 Cybersecurity and Ethics -- 2 Domains of Cybersecurity -- 3 Threats and Actors -- 3.1 Threats in Cyberspace -- 3.2 Types of Threats -- 3.3 Threat Actors and Types of Threat Actors -- 4 Recent Attacks -- 5 Awareness of Cybersecurity in Educational System -- 6 The Outline of the Book -- References -- 2 Being Hidden and Anonymous -- 1 Introduction -- 1.1 The Need for Anonymity -- 2 The Onion Router -- 3 Invisible Internet Project (IIP or I2P) -- 3.1 Working of I2P -- 4 Freenet -- 5 Java Anon Proxy (JAP) -- 6 Summary -- References -- 3 TOR-The Onion Router -- 1 Introduction -- 2 TOR-The Onion Router -- 2.1 Onion Routing -- 3 TOR Browser Installation -- 4 TOR Entities -- 5 TOR Status -- 6 TOR for Mobile-Orbot -- 7 Loopholes in TOR -- 8 What not to Use in TOR -- References -- 4 DarkNet and Hidden Services -- 1 Introduction -- 2 TOR and Its Hidden Service -- 3 Essential Concepts of TOR Hidden Services -- 4 Installation of Hidden Service in Linux -- 5 Countermeasures to Secure Your Own Hidden Service -- References -- 5 Introduction to Digital Forensics -- 1 Introduction to Forensics -- 2 Cyberforensic Process -- 3 Different Artifacts and Forensic Tools -- 3.1 Autopsy -- 3.2 DumpIt -- 3.3 Belkasoft Live RAM Capturer -- 4 Artifacts Gathering -- 4.1 Browser Artifacts -- 4.2 Registry Artifacts -- 4.3 Bulk Extractor -- 5 Network Forensics -- 5.1 ARP Cache Poisoning -- 5.2 Port Mirroring -- 5.3 Flooding -- 5.4 Dynamic Host Control Protocol (DHCP) Redirection -- 5.5 Detection of TOR Traffic in the Captured Traffic -- 6 Conclusion -- References -- 6 Intrusion Detection Systems Fundamentals -- 1 Introduction to Intrusion Detection System -- 2 Techniques to Combat Cyberthreats. , 2.1 Firewall -- 2.2 Authentication -- 2.3 Authorization -- 2.4 Encryption -- 2.5 Intrusion Detection System -- 3 Network-Based Intrusion Detection System (NIDS) -- 4 Host-Based Intrusion Detection System (HIDS) -- 5 Distributed Intrusion Detection System (DIDS) -- 5.1 Signature-Based Analysis -- 5.2 Anomaly-Based Analysis -- 6 Snort-Network-Based Intrusion Detection System -- 6.1 Additional Snort Add-Ons -- 6.2 Installation of Snort in Linux -- 6.3 Snort Rules -- 6.4 Rule Header -- 6.5 Rules Options -- 7 Open-Source Host-Based Intrusion Detection System (OSSEC) -- 7.1 Installation of OSSEC in Linux -- 8 Summary -- References -- 7 Introduction to Malware Analysis -- 1 Introduction of Malware -- 2 Types of Malware -- 3 Malware Symptoms -- 4 Need of Malware Analysis and Spreading Mechanism -- 4.1 Need for Malware Analysis -- 4.2 Malware Spreading Mechanism -- 5 Malware Analysis Prerequisites -- 6 Malware Analysis Environment -- 7 Malware Detection System and Analysis -- 7.1 Malware Detection -- 7.2 Malware Analysis -- 8 Conclusion -- References -- 8 Design of a Virtual Cybersecurity Lab -- 1 Introduction of Cybersecurity -- 2 Tools for Cybersecurity -- 3 Virtualization for Cybersecurity -- 4 Installation and Configuration of VMWare Workstation -- 5 Network Modes in Virtual Machines -- 6 Cybersecurity and Various Attacks -- 7 Defense Strategies Against Various Attacks -- 8 Case Study on Website Attacks -- 9 Conclusion -- References -- 9 Importance of Cyberlaw -- 1 Introduction -- 2 Why Cyberlaw is Necessary -- 3 Global Landscape of Cyberlaw -- 4 Cybercrimes -- 4.1 Categories of Cybercrime -- 4.2 Types of Cybercrime -- 5 Conclusion -- References.

Anmerkung: Intro -- Preface -- Contents -- About the Editors -- The Rudiments of Energy Conservation and IoT -- 1 Introduction -- 2 Paradigmatic View of Energy-Efficient IoT -- 3 Pragmatic Energy-Efficient IoT System Architecture -- 4 Issues of Energy Conservation in IoT -- 5 Energy Conservation Approaches for IoT Devices and Its Perspectives -- 5.1 Node Activity Management -- 5.2 Data Aggregation and Transmission Process -- 5.3 Media Access Control (MAC) Protocol -- 5.4 Security Management -- 5.5 Topology Management -- 5.6 Routing -- 6 Energy-Efficient System Design for IoT Devices -- 7 Conclusions -- References -- Existing Enabling Technologies and Solutions for Energy Management in IoT -- 1 Introduction -- 2 Architectures of IoT -- 2.1 Three-Layer Architecture -- 2.2 Four Layer Architecture -- 2.3 Five-Layer Architecture -- 3 Components of IoT -- 3.1 Identification -- 3.2 Sensing -- 3.3 Communication -- 3.4 Computation -- 3.5 Services -- 3.6 Semantics -- 4 Applications -- 4.1 Home Automation -- 4.2 Health care -- 4.3 Transportation -- 4.4 Logistics -- 4.5 Smart Environment and Agriculture -- 5 Challenges in IoT -- 6 Energy Management -- 6.1 Energy Harvesting -- 6.2 Energy Conservation -- 7 Research Directions -- 8 Conclusion -- References -- Energy-Efficient System Design for Internet of Things (IoT) Devices -- 1 Introduction -- 2 Operation -- 3 Energy Conservation -- 3.1 Solar Energy Harvesting -- 3.2 Thermal Energy Harvesting -- 3.3 Vibrational Energy Harvesting -- 3.4 Electrostatic Energy Harvesting -- 3.5 Wind Energy Harvesting -- 3.6 RF Energy Harvesting -- 4 Harvesting Module -- 4.1 Rectenna Model -- 4.2 Sensing Antenna -- 4.3 DC-DC Converter -- 4.4 Power Management Unit -- 5 Wireless Energy Harvesting -- 5.1 Near Field Communication -- 5.2 Inductive Coupling -- 6 Applications -- 6.1 Home Appliances -- 6.2 Healthcare Devices. , 6.3 Automatic Vehicles -- 6.4 Business Infrastructure -- 6.5 Farming and Poultry -- 6.6 Smart Utilities -- References -- Models and Algorithms for Energy Conservation in Internet of Things -- 1 Introduction -- 2 Data Centers -- 2.1 Big Data -- 2.2 Cloud Computing -- 3 Virtualization -- 4 Load Balancing -- 4.1 Hardware Versus Software Load Balancing -- 5 Energy Consumptions in Data Centers -- 5.1 Green Computing -- 5.2 Power Calculation at Data Center -- 6 Static Energy-Efficient Algorithms -- 6.1 Exact Allocation Algorithm -- 6.2 Best Fit Heuristic Algorithm -- 7 Dynamic Energy-Efficient Algorithms -- 7.1 Hardware Level Solution -- 7.2 Software Level Solution -- 8 Summary -- References -- An Energy-Efficient IoT Group-Based Architecture for Smart Cities -- 1 Introduction -- 2 Related Work -- 3 System Description -- 3.1 The WSN for e-Health and Human Well-Being Monitoring -- 3.2 Utilities Monitoring Systems -- 3.3 Air Quality and Climate Monitoring Systems -- 3.4 Emergency Situations Monitoring -- 3.5 Other Systems -- 4 Proposed Architecture for the Smart City -- 5 Conclusion and Future Work -- References -- Context-Aware Automation Based Energy Conservation Techniques for IoT Ecosystem -- 1 Introduction -- 1.1 Communication Technologies -- 1.2 Pricing Policies -- 2 Introduction -- 3 Related Work -- 3.1 Demand-Side Management -- 3.2 Usage of Renewable Energy Source -- 3.3 Context-Aware Automation -- 3.4 Feedback-Based Automation -- 4 Case Studies -- 5 Proposed Framework -- 6 Future Directions and Challenges -- 7 Conclusion -- References -- Energy Conservation in IoT-Based Smart Home and Its Automation -- 1 Introduction -- 2 Electrical Network End-to-End System -- 2.1 Generation -- 2.2 Transmission and Distribution -- 2.3 Automation in Demand, Supply, and Monitoring -- 2.4 Load Shedding and Control -- 3 Causes of Energy Losses and Preventive Actions. , 3.1 Electrical Network Improvement -- 3.2 Smart Energy Monitoring Devices -- 4 Automation and Control in Electrical Network -- 4.1 Automation Devices -- 4.2 Standards for Automation Devices -- 4.3 Communication Hardware and Automation Protocols -- 5 Energy Conservation Key Area -- 5.1 Smart Buildings -- 5.2 Smart Homes -- 5.3 Smart Appliances -- 6 Energy Conservation in Smart Home and IoT -- 6.1 Automation and Sensors in Smart Home -- 6.2 Industry Trends and Present Technology -- 6.3 Energy Conservation Components of Smart Home -- 6.4 Renewable Energy Sources with IoT in Smart Home -- 7 Artificial Intelligence in Energy Conservation-Methods and Technology -- 7.1 Digital Signal Processing and IoT -- 7.2 Artificial Intelligence in Smart Home -- 8 Cloud Data Processing Using IoT Devices -- 9 Conclusions -- References -- IoT Architecture for Preventive Energy Conservation of Smart Buildings -- 1 Introduction -- 1.1 Prevalent Smart Components -- 1.2 IoT System Architectures -- 1.3 Smart Buildings -- 1.4 Energy Efficiency in Smart Building IoT Systems -- 2 Requirements and Approaches for Energy Efficiency in Smart Buildings -- 2.1 Requirements for Environmental Conservation -- 2.2 Requirement for Energy Modeling -- 2.3 Requirement for Energy Consumption Monitoring and Evaluation -- 3 Existing Application Architectures -- 3.1 Smart Energy Metering Architectures -- 3.2 Smart Lighting Architectures -- 3.3 Energy Management Interfaces for Buildings -- 3.4 Energy-Efficient Smart Building Automation Architectures -- 3.5 Energy-Efficient Implementations in Smart Grid -- 3.6 Energy-Efficient Comfort Management Systems in Smart Buildings -- 3.7 Energy Monitoring and Saving Methods in Smart Buildings -- 4 Open Challenges and Future Work -- 4.1 Lack of Interoperability for Currently Used Protocols -- 4.2 Need for a Cost-Effective Architecture that Conserves Energy. , 4.3 Integration of Renewable Energy Sources in Smart Buildings -- 4.4 Maintainability of Energy-Efficient Architectures -- 5 Conclusion -- References -- Designing Energy-Efficient IoT-Based Intelligent Transport System: Need, Architecture, Characteristics, Challenges, and Applications -- 1 Introduction -- 1.1 Intelligent Transport System -- 1.2 Motivations for IoT in Transportation -- 1.3 Architecture of ITS -- 2 Key Technologies and Related Power Optimization Bottlenecks -- 2.1 Perception Technology: Precision, Reliability, and Power Constraints -- 2.2 Communication Technology and Related Power Issues -- 2.3 Information Extraction and Underlying Power Issues -- 3 Energy Efficiency Challenges and Corresponding Solutions -- 3.1 Precision, Density, and Reliability of Perception and Smart Sensing Solutions -- 3.2 Information Exchange Based Solutions -- 3.3 Computational Feasibility and Distributed Computing Solutions -- 3.4 Data Collection and Pooling with Energy-Efficient Solutions -- 4 Further Challenges and Opportunities -- 4.1 Further Involvement of Internet of Vehicle (IoV) -- 4.2 Cooperative Automated Vehicle (CAV) Scheme -- 4.3 Utilization of Multiple-Source Data in ITS -- 4.4 Software-Defined Radio (SDR)-Based Communication -- 4.5 Energy Harvesting Corridors -- 5 Conclusion and Future Work -- References -- Capacity Estimation of Electric Vehicle Aggregator for Ancillary Services to the Grid -- 1 Historical Perspective -- 2 Development of Electric Vehicles -- 3 Motivation for Vehicle to Everything (V2X) and V2G Technology -- 4 Electric Vehicles and Solar Power Plants in Smart Grid Environment -- 5 Potential of EV to Grid Connection -- 6 Capacity Estimation of Aggregator -- 7 Battery Management System -- 8 Grid Connection and Performance Testing of V2G -- 9 Commercial Value of V2G -- 10 Challenges and Opportunities -- 11 Discussion and Conclusion. , References -- Need and Design of Smart and Secure Energy-Efficient IoT-Based Healthcare Framework -- 1 Introduction -- 2 Data Generation in IoT Environment -- 3 Applications of IoT -- 4 Publication Trends of IoT -- 5 Critical Human Disorders -- 6 Energy-Efficient IoT Systems (Related Works) -- 7 Role of IoT in Designing Energy-Optimized Systems -- 7.1 Proposed Energy-Efficient IoT-Based Healthcare System for Neurological and Psychological Disorder Patients -- 8 Conclusion -- References -- Medical Information Processing Using Smartphone Under IoT Framework -- 1 Introduction -- 1.1 Motivation -- 1.2 Objectives -- 1.3 Organization of the Chapter -- 2 System Model -- 3 System Requirement -- 4 Importance of Cloud for Smartphone-Enabled IoT -- 5 Internet of Medical Things (IoMT) Using Smartphone -- 6 Biomedical Data Processing -- 6.1 Transmission of Medical Image Signals -- 6.2 Transmission of Biomedical Signals (ECG, EEG, and EMG) -- 6.3 Transmission of Medical Video Signals -- 6.4 Teletrauma System -- 7 Application of IoT -- 7.1 Application Oriented to Health Care -- 8 Application Standards/Protocols Use in IoT (Health Care) -- 9 Challenges -- 10 Conclusion -- References -- Contributing Toward Green IoT: An Awareness-Based Approach -- 1 Introduction -- 2 A Walkthrough of Internets of Things and Its Applications -- 2.1 Challenges of Internet of Things -- 3 Green IoT: An Overview -- 3.1 Smart Homes -- 3.2 Smart Cities -- 3.3 Energy-Efficient Smart Health Care -- 4 Various Approaches to Achieve Green IoT -- 5 Awareness-Based Approach Toward Green IoT -- 5.1 Energy Awareness Campaigns -- 5.2 IoT-Based Smart Metering -- 5.3 Promoting Recycling -- 5.4 Creating Awareness About Green Information Communication Technology -- 5.5 Promoting the Usage of Sensor Cloud: A Step Toward Green IoT. , 6 Creating Awareness Through Prototyping: A Green IoT-Based Smart Home Model.

Anmerkung: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1. Introduction to Signal Processing and Machine Learning -- 1.1 Introduction -- 1.2 Basic Terminologies -- 1.2.1 Signal Processing -- 1.2.1.1 Continuous and Discrete Signals -- 1.2.1.2 Sampling and Quantization -- 1.2.1.3 Change of Basis -- 1.2.1.4 Importance of Time Domain and Frequency Domain Analyses -- 1.2.2 Machine Learning -- 1.3 Distance-Based Signal Classification, Nearest Neighbor Classifier, and Hilbert Space -- 1.3.1 Distance-Based Signal Classification -- 1.3.1.1 Metric Space -- 1.3.1.2 Normed Linear Space -- 1.3.1.3 Inner Product Space -- 1.3.2 Nearest Neighbor Classification -- 1.3.3 Hilbert Space -- 1.4 Fusion of Machine Learning in Signal Processing -- 1.5 Benefits of Adopting Machine Learning in Signal Processing -- 1.6 Conclusion -- References -- 2. Learning Theory (Supervised/Unsupervised) for Signal Processing -- 2.1 Introduction -- 2.1.1 Signal Processing -- 2.2 Machine Learning -- 2.2.1 Why Do We Need ML for Signal Processing? -- 2.2.2 Speaker ID - A Utilization of ML Calculations in Sign Handling -- 2.2.3 Discourse and Audio Processing -- 2.2.4 Discourse Recognition -- 2.2.5 Listening Devices -- 2.2.6 Independent Driving -- 2.2.7 Picture Processing and Analysis -- 2.2.8 Wearables -- 2.2.9 Information Science -- 2.2.10 Wireless Systems and Networks -- 2.3 Machine Learning Algorithms -- 2.4 Supervised Learning -- 2.5 Unsupervised Learning -- 2.6 Semi-Supervised Learning -- 2.7 Reinforcement Learning -- 2.8 Use Case of Signal Processing Using Supervised and Unsupervised Learning -- 2.8.1 Features and Classifiers -- 2.8.2 Linear Classifiers -- 2.8.3 Decision Hyperplanes -- 2.8.4 Least Squares Methods -- 2.8.5 Mean Square Estimation -- 2.8.6 Support Vector machines -- 2.8.7 Non-Linear Regression. , 2.8.8 Non-Linearity of Activation Functions -- 2.8.8.1 Sigmoid Function -- 2.8.8.2 Rectified Linear Unit (ReLU) -- 2.8.9 Classification -- 2.8.9.1 Linear Classification -- 2.8.9.2 Two-Class Classification -- 2.8.9.3 Geometrical Interpretation of Derivatives -- 2.8.9.4 Multiclass Classification: Loss Function -- 2.8.10 Mean Squared Error -- 2.8.11 Multilabel Classification -- 2.8.12 Gradient Descent -- 2.8.12.1 Learning Rate -- 2.8.13 Hyperparameter Tuning -- 2.8.13.1 Validation -- 2.8.14 Regularization -- 2.8.14.1 How Does Regularization Work? -- 2.8.15 Regularization Techniques -- 2.8.15.1 Ridge Regression (L2 Regularization) -- 2.8.16 Lasso Regression (L1 Regularization) -- 2.8.17 K-Means Clustering -- 2.8.18 The KNN Algorithm -- 2.8.19 Clustering -- 2.8.20 Clustering Methods -- 2.9 Deep Learning for Signal Data -- 2.9.1 Traditional Time Series Analysis -- 2.9.2 Recurrence Domain Analysis -- 2.9.3 Long Short-Term Memory Models for Human Activity Recognition -- 2.9.4 External Device HAR -- 2.9.5 Signal Processing on GPUs -- 2.9.6 Signal Processing on FPGAs -- 2.9.7 Signal Processing is coming to the Forefront of Data Analysis -- 2.10 Conclusion -- References -- 3. Supervised and Unsupervised Learning Theory for Signal Processing -- 3.1 Introduction -- 3.1.1 Supervised Learning -- 3.1.2 Unsupervised Learning -- 3.1.3 Reinforcement Learning -- 3.1.4 Semi-Supervised Learning -- 3.2 Supervised Learning Method -- 3.2.1 Classicfiation Problems -- 3.2.2 Regression Problems -- 3.2.3 Examples of Supervised Learning -- 3.3 Unsupervised Learning Method -- 3.3.1 Illustrations of Unsupervised Learning -- 3.4 Semi-Supervised Learning Method -- 3.5 Binary Classification -- 3.5.1 Different Classes -- 3.5.2 Classification in Preparation -- 3.5.2.1 Logistic Regression Model -- 3.5.2.2 Odds Ratio -- 3.5.2.3 Logit Function -- 3.5.2.4 The Sigmoid Function. , 3.5.2.5 Support Vector Machines -- 3.5.2.6 Maximum Margin Lines -- 3.6 Conclusion -- References -- 4. Applications of Signal Processing -- 4.1 Introduction -- 4.2 Audio Signal Processing -- 4.2.1 Machine Learning in Audio Signal Processing -- 4.2.1.1 Spectrum and Cepstrum -- 4.2.1.2 Mel Frequency Cepstral Coefficients -- 4.2.1.3 Gammatone Frequency Cepstral Coefficients -- 4.2.1.4 Building the Classifier -- 4.3 Audio Compression -- 4.3.1 Modeling and Coding -- 4.3.2 Lossless Compression -- 4.3.3 Lossy Compression -- 4.3.4 Compressed Audio with Machine Learning Applications -- 4.4 Digital Image Processing -- 4.4.1 Fields Overlapping with Image Processing -- 4.4.2 Digital Image Processing System -- 4.4.3 Machine Learning with Digital Image Processing -- 4.4.3.1 Image Classification -- 4.4.3.2 Data Labelling -- 4.4.3.3 Location Detection -- 4.5 Video Compression -- 4.5.1 Video Compression Model -- 4.5.2 Machine Learning in Video Compression -- 4.5.2.1 Development Savings -- 4.5.2.2 Improving Encoder Density -- 4.6 Digital Communications -- 4.6.1 Machine Learning in Digital Communications -- 4.6.1.1 Communication Networks -- 4.6.1.2 Wireless Communication -- 4.6.1.3 Smart Infrastructure and IoT -- 4.6.1.4 Security and Privacy -- 4.6.1.5 Multimedia Communication -- 4.6.2 Healthcare -- 4.6.2.1 Personalized Medical Treatment -- 4.6.2.2 Clinical Research and Trial -- 4.6.2.3 Diagnosis of Disease -- 4.6.2.4 Smart Health Records -- 4.6.2.5 Medical Imaging -- 4.6.2.6 Drug Discovery -- 4.6.2.7 Outbreak Prediction -- 4.6.3 Seismology -- 4.6.3.1 Interpreting Seismic Observations -- 4.6.3.2 Machine Learning in Seismology -- 4.6.4 Speech Recognition -- 4.6.5 Computer Vision -- 4.6.6 Economic Forecasting -- 4.7 Conclusion -- References -- 5. Dive in Deep Learning: Computer Vision, Natural Language Processing, and Signal Processing -- 5.1 Deep Learning: Introduction. , 5.2 Past, Present, and Future of Deep- Learning -- 5.3 Natural Language Processing -- 5.3.1 Word Embeddings -- 5.3.1.1 Word2vec -- 5.3.2 Global Vectors for Word Representation -- 5.3.3 Convolutional Neural Networks -- 5.3.4 Feature Selection and Preprocessing -- 5.3.4.1 Tokenization -- 5.3.4.2 Stop Word Removal -- 5.3.4.3 Stemming -- 5.3.4.4 Lemmatization -- 5.3.5 Named Entity Recognition -- 5.4 Image Processing -- 5.4.1 Introduction to Image Processing and Computer Vision -- 5.4.1.1 Scene Understanding -- 5.4.2 Localization -- 5.4.3 Smart Cities and Surveillance -- 5.4.4 Medical Imaging -- 5.4.5 Object Representation -- 5.4.6 Object Detection -- 5.5 Audio Processing and Deep Learning -- 5.5.1 Audio Data Handling Using Python -- 5.5.2 Spectrogram -- 5.5.3 Wavelet- Based Feature Extraction -- 5.5.4 Current Methods -- 5.5.4.1 Audio Classification -- 5.5.4.2 Audio Fingerprinting -- 5.5.4.3 Feature Extraction -- 5.5.4.4 Speech Classification -- 5.5.4.5 Music Processing -- 5.5.4.6 Natural Sound Processing -- 5.5.4.7 Technological Tools -- 5.6 Conclusion -- References -- 6. Brain-Computer Interfacing -- 6.1 Introduction to BCI and Its Components -- 6.1.1 BCI Components -- 6.2 Framework/Architecture of BCI -- 6.3 Functions of BCI -- 6.3.1 Correspondence and Control -- 6.3.2 Client State Checking -- 6.4 Applications of BCI -- 6.4.1 Healthcare -- 6.4.1.1 Prevention -- 6.4.1.2 Detection and Diagnosis -- 6.4.1.3 Rehabilitation and Restoration -- 6.4.2 Neuroergonomics and Smart Environment -- 6.4.3 Neuromarketing and Advertisement -- 6.4.4 Pedagogical and Self-Regulating Oneself -- 6.4.5 Games and Entertainment -- 6.4.6 Security and Authentication -- 6.5 Signal Acquisition -- 6.5.1 Invasive Techniques -- 6.5.1.1 Intracortical -- 6.5.1.2 ECoG and Cortical Surface -- 6.5.2 Noninvasive Techniques -- 6.5.2.1 Magneto-encephalography (MEG). , 6.5.2.2 fMRI (functional Magnetic Resonance Imaging) -- 6.5.2.3 fNIRS (functional Near-Infrared Spectroscopy) -- 6.5.2.4 EEG (Electroencephalogram) -- 6.6 Electrical Signal of BCI -- 6.6.1 Evoked Potential (EP) or Evoked Response -- 6.6.2 Event-Related Desynchronization and Synchronization -- 6.7 Challenges of BCI and Proposed Solutions -- 6.7.1 Challenges of Usability -- 6.7.2 Technical Issues -- 6.7.3 Proposed Solutions -- 6.7.3.1 Noise Removal -- 6.7.3.2 Disconnectedness of Multiple Classes -- 6.8 Conclusion -- References -- 7. Adaptive Filters and Neural Net -- 7.1 Introduction -- 7.1.1 Adaptive Filtering Problem -- 7.2 Linear Adaptive Filter Implementation -- 7.2.1 Stochastic Gradient Approach -- 7.2.2 Least Square Estimation -- 7.3 Nonlinear Adaptive Filters -- 7.3.1 Volterra-Based Nonlinear Adaptive Filter -- 7.4 Applications of Adaptive Filter -- 7.4.1 Biomedical Applications -- 7.4.1.1 ECG Power-Line Interference Removal -- 7.4.1.2 Maternal-Fetal ECG Separation -- 7.4.2 Speech Processing -- 7.4.2.1 Noise Cancelation -- 7.4.3 Communication Systems -- 7.4.3.1 Channel Equalization in Data Transmission Systems -- 7.4.3.2 Multiple Access Interference Mitigation in CDMA -- 7.4.4 Adaptive Feedback Cancellation in Hearing Aids -- 7.5 Neural Network -- 7.5.1 Learning Techniques in ANN -- 7.6 Single and Multilayer Neural Net -- 7.6.1 Single-Layer Neural Networks -- 7.6.2 Multilayer Neural Net -- 7.7 Applications of Neural Networks -- 7.7.1 ECG Classicafition -- 7.7.1.1 Methodology -- 7.7.2 Speech Recognition -- 7.7.2.1 Methodology -- 7.7.3 Communication Systems -- 7.7.3.1 Mobile Station Location Identification Using ANN -- 7.7.3.2 ANN-Based Call Handoff Management Scheme for Mobile Cellular Network -- 7.7.3.3 A Hybrid Path Loss Prediction Model based on Artificial  Neural Networks -- 7.7.3.4 Classification of Primary Radio Signals. , 7.7.3.5 Channel Capacity Estimation Using ANN.