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Body Sensor Networking, Design and Algorithms.

By: Contributor(s): Material type: TextTextPublisher: Newark : John Wiley & Sons, Incorporated, 2020Copyright date: ©2019Edition: 1st edDescription: 1 online resource (412 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781119390046
Subject(s): Genre/Form: Additional physical formats: Print version:: Body Sensor Networking, Design and AlgorithmsLOC classification:
  • TK5103.35 .S264 2020
Online resources:
Contents:
Cover -- Title Page -- Copyright -- Contents -- Preface -- About the Companion Website -- Chapter 1 Introduction -- 1.1 History of Wearable Technology -- 1.2 Introduction to BSN Technology -- 1.3 BSN Architecture -- 1.4 Layout of the Book -- References -- Chapter 2 Physical, Physiological, Biological, and Behavioural States of the Human Body -- 2.1 Introduction -- 2.2 Physical State of the Human Body -- 2.3 Physiological State of Human Body -- 2.4 Biological State of Human Body -- 2.5 Psychological and Behavioural State of the Human Body -- 2.6 Summary and Conclusions -- References -- Chapter 3 Physical, Physiological, and Biological Measurements -- 3.1 Introduction -- 3.2 Wearable Technology for Gait Monitoring -- 3.2.1 Accelerometer and Its Application to Gait Monitoring -- 3.2.1.1 How Accelerometers Operate -- 3.2.1.2 Accelerometers in Practice -- 3.2.2 Gyroscope and IMU -- 3.2.3 Force Plates -- 3.2.4 Goniometer -- 3.2.5 Electromyography -- 3.2.6 Sensing Fabric -- 3.3 Physiological Sensors -- 3.3.1 Multichannel Measurement of the Nerves Electric Potentials -- 3.3.2 Other Sensors -- 3.4 Biological Sensors -- 3.4.1 The Structures of Biological Sensors - The Principles -- 3.4.2 Emerging Biosensor Technologies -- 3.5 Conclusions -- References -- Chapter 4 Ambulatory and Popular Sensor Measurements -- 4.1 Introduction -- 4.2 Heart Rate -- 4.2.1 HR During Physical Exercise -- 4.3 Respiration -- 4.4 Blood Oxygen Saturation Level -- 4.5 Blood Pressure -- 4.5.1 Cuffless Blood Pressure Measurement -- 4.6 Blood Glucose -- 4.7 Body Temperature -- 4.8 Commercial Sensors -- 4.9 Conclusions -- References -- Chapter 5 Polysomnography and Sleep Analysis -- 5.1 Introduction -- 5.2 Polysomnography -- 5.3 Sleep Stage Classification -- 5.3.1 Sleep Stages -- 5.3.2 EEG‐Based Classification of Sleep Stages -- 5.3.2.1 Time Domain Features.
5.3.2.2 Frequency Domain Features -- 5.3.2.3 Time‐frequency Domain Features -- 5.3.2.4 Short‐time Fourier Transform -- 5.3.2.5 Wavelet Transform -- 5.3.2.6 Matching Pursuit -- 5.3.2.7 Empirical Mode Decomposition -- 5.3.2.8 Nonlinear Features -- 5.3.3 Classification Techniques -- 5.3.3.1 Using Neural Networks -- 5.3.3.2 Application of CNNs -- 5.3.4 Sleep Stage Scoring Using CNN -- 5.4 Monitoring Movements and Body Position During Sleep -- 5.5 Conclusions -- References -- Chapter 6 Noninvasive, Intrusive, and Nonintrusive Measurements -- 6.1 Introduction -- 6.2 Noninvasive Monitoring -- 6.3 Contactless Monitoring -- 6.3.1 Remote Photoplethysmography -- 6.3.1.1 Derivation of Remote PPG -- 6.3.2 Spectral Analysis Using Autoregressive Modelling -- 6.3.3 Estimation of Physiological Parameters Using Remote PPG -- 6.3.3.1 Heart Rate Estimation -- 6.3.3.2 Respiratory Rate Estimation -- 6.3.3.3 Blood Oxygen Saturation Level Estimation -- 6.3.3.4 Pulse Transmit Time Estimation -- 6.3.3.5 Video Pre‐processing -- 6.3.3.6 Selection of Regions of Interest -- 6.3.3.7 Derivation of the rPPG Signal -- 6.3.3.8 Processing rPPG Signals -- 6.3.3.9 Calculation of rPTT/dPTT -- 6.4 Implantable Sensor Systems -- 6.5 Conclusions -- References -- Chapter 7 Single and Multiple Sensor Networking for Gait Analysis -- 7.1 Introduction -- 7.2 Gait Events and Parameters -- 7.2.1 Gait Events -- 7.2.2 Gait Parameters -- 7.2.2.1 Temporal Gait Parameters -- 7.2.2.2 Spatial Gait Parameters -- 7.2.2.3 Kinetic Gait Parameters -- 7.2.2.4 Kinematic Gait Parameters -- 7.3 Standard Gait Measurement Systems -- 7.3.1 Foot Plantar Pressure System -- 7.3.2 Force‐plate Measurement System -- 7.3.3 Optical Motion Capture Systems -- 7.3.4 Microsoft Kinect Image and Depth Sensors -- 7.4 Wearable Sensors for Gait Analysis -- 7.4.1 Single Sensor Platforms -- 7.4.2 Multiple Sensor Platforms.
7.5 Gait Analysis Algorithms Based on Accelerometer/Gyroscope -- 7.5.1 Estimation of Gait Events -- 7.5.2 Estimation of Gait Parameters -- 7.5.2.1 Estimation of Orientation -- 7.5.2.2 Estimating Angles Using Accelerometers -- 7.5.2.3 Estimating Angles Using Gyroscopes -- 7.5.2.4 Fusing Accelerometer and Gyroscope Data -- 7.5.2.5 Quaternion Based Estimation of Orientation -- 7.5.2.6 Step Length Estimation -- 7.6 Conclusions -- References -- Chapter 8 Popular Health Monitoring Systems -- 8.1 Introduction -- 8.2 Technology for Data Acquisition -- 8.3 Physiological Health Monitoring Technologies -- 8.3.1 Predicting Patient Deterioration -- 8.3.2 Ambient Assisted Living: Monitoring Daily Living Activities -- 8.3.3 Monitoring Chronic Obstructive Pulmonary Disease Patients -- 8.3.4 Movement Tracking and Fall Detection/Prevention -- 8.3.5 Monitoring Patients with Dementia -- 8.3.6 Monitoring Patients with Parkinson's Disease -- 8.3.7 Odour Sensitivity Measurement -- 8.4 Conclusions -- References -- Chapter 9 Machine Learning for Sensor Networks -- 9.1 Introduction -- 9.2 Clustering Approaches -- 9.2.1 k‐means Clustering Algorithm -- 9.2.2 Iterative Self‐organising Data Analysis Technique -- 9.2.3 Gap Statistics -- 9.2.4 Density‐based Clustering -- 9.2.5 Affinity‐based Clustering -- 9.2.6 Deep Clustering -- 9.2.7 Semi‐supervised Clustering -- 9.2.7.1 Basic Semi‐supervised Techniques -- 9.2.7.2 Deep Semi‐supervised Techniques -- 9.2.8 Fuzzy Clustering -- 9.3 Classification Algorithms -- 9.3.1 Decision Trees -- 9.3.2 Random Forest -- 9.3.3 Linear Discriminant Analysis -- 9.3.4 Support Vector Machines -- 9.3.5 k‐nearest Neighbour -- 9.3.6 Gaussian Mixture Model -- 9.3.7 Logistic Regression -- 9.3.8 Reinforcement Learning -- 9.3.9 Artificial Neural Networks -- 9.3.9.1 Deep Neural Networks -- 9.3.9.2 Convolutional Neural Networks -- 9.3.9.3 Recent DNN Approaches.
9.3.10 Gaussian Processes -- 9.3.11 Neural Processes -- 9.3.12 Graph Convolutional Networks -- 9.3.13 Naïve Bayes Classifier -- 9.3.14 Hidden Markov Model -- 9.3.14.1 Forward Algorithm -- 9.3.14.2 Backward Algorithm -- 9.3.14.3 HMM Design -- 9.4 Common Spatial Patterns -- 9.5 Applications of Machine Learning in BSNs and WSNs -- 9.5.1 Human Activity Detection -- 9.5.2 Scoring Sleep Stages -- 9.5.3 Fault Detection -- 9.5.4 Gas Pipeline Leakage Detection -- 9.5.5 Measuring Pollution Level -- 9.5.6 Fatigue‐tracking and Classification System -- 9.5.7 Eye‐blink Artefact Removal from EEG Signals -- 9.5.8 Seizure Detection -- 9.5.9 BCI Applications -- 9.6 Conclusions -- References -- Chapter 10 Signal Processing for Sensor Networks -- 10.1 Introduction -- 10.2 Signal Processing Problems for Sensor Networks -- 10.3 Fundamental Concepts in Signal Processing -- 10.3.1 Nonlinearity of the Medium -- 10.3.2 Nonstationarity -- 10.3.3 Signal Segmentation -- 10.3.4 Signal Filtering -- 10.4 Mathematical Data Models -- 10.4.1 Linear Models -- 10.4.1.1 Prediction Method -- 10.4.1.2 Prony's Method -- 10.4.1.3 Singular Spectrum Analysis -- 10.4.2 Nonlinear Modelling -- 10.4.3 Gaussian Mixture Model -- 10.5 Transform Domain Signal Analysis -- 10.6 Time‐frequency Domain Transforms -- 10.6.1 Short‐time Fourier Transform -- 10.6.2 Wavelet Transform -- 10.6.2.1 Continuous Wavelet Transform -- 10.6.2.2 Examples of Continuous Wavelets -- 10.6.2.3 Discrete Time Wavelet Transform -- 10.6.3 Multiresolution Analysis -- 10.6.4 Synchro‐squeezing Wavelet Transform -- 10.7 Adaptive Filtering -- 10.8 Cooperative Adaptive Filtering -- 10.8.1 Diffusion Adaptation -- 10.9 Multichannel Signal Processing -- 10.9.1 Instantaneous and Convolutive BSS Problems -- 10.9.2 Array Processing -- 10.10 Signal Processing Platforms for BANs -- 10.11 Conclusions -- References.
Chapter 11 Communication Systems for Body Area Networks -- 11.1 Introduction -- 11.2 Short‐range Communication Systems -- 11.2.1 Bluetooth -- 11.2.2 Wi‐Fi -- 11.2.3 ZigBee -- 11.2.4 Radio Frequency Identification Devices -- 11.2.5 Ultrawideband -- 11.2.6 Other Short‐range Communication Methods -- 11.2.7 RF Modules Available in Market -- 11.3 Limitations, Interferences, Noise, and Artefacts -- 11.4 Channel Modelling -- 11.4.1 BAN Propagation Scenarios -- 11.4.1.1 On‐body Channel -- 11.4.1.2 In‐body Channel -- 11.4.1.3 Off‐body Channel -- 11.4.1.4 Body‐to‐body (or Interference) Channel -- 11.4.2 Recent Approaches to BAN Channel Modelling -- 11.4.3 Propagation Models -- 11.4.4 Standards and Guidelines -- 11.5 BAN‐WSN Communications -- 11.6 Routing in WBAN -- 11.6.1 Posture‐based Routing -- 11.6.2 Temperature‐based Routing -- 11.6.3 Cross‐layer Routing -- 11.6.4 Cluster‐based Routing -- 11.6.5 QoS‐based Routing -- 11.7 BAN‐building Network Integration -- 11.8 Cooperative BANs -- 11.9 BAN Security -- 11.10 Conclusions -- References -- Chapter 12 Energy Harvesting Enabled Body Sensor Networks -- 12.1 Introduction -- 12.2 Energy Conservation -- 12.3 Network Capacity -- 12.4 Energy Harvesting -- 12.5 Challenges in Energy Harvesting -- 12.6 Types of Energy Harvesting -- 12.6.1 Harvesting Energy from Kinetic Sources -- 12.6.2 Energy Sources from Radiant Sources -- 12.6.3 Energy Harvesting from Thermal Sources -- 12.6.4 Energy Harvesting from Biochemical and Chemical Sources -- 12.7 Topology Control -- 12.8 Typical Energy Harvesters for BSNs -- 12.9 Predicting Availability of Energy -- 12.10 Reliability of Energy Storage -- 12.11 Conclusions -- References -- Chapter 13 Quality of Service, Security, and Privacy for Wearable Sensor Data -- 13.1 Introduction -- 13.2 Threats to a BAN -- 13.2.1 Denial‐of‐service -- 13.2.2 Man‐in‐the‐middle Attack.
13.2.3 Phishing and Spear Phishing Attacks.
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Cover -- Title Page -- Copyright -- Contents -- Preface -- About the Companion Website -- Chapter 1 Introduction -- 1.1 History of Wearable Technology -- 1.2 Introduction to BSN Technology -- 1.3 BSN Architecture -- 1.4 Layout of the Book -- References -- Chapter 2 Physical, Physiological, Biological, and Behavioural States of the Human Body -- 2.1 Introduction -- 2.2 Physical State of the Human Body -- 2.3 Physiological State of Human Body -- 2.4 Biological State of Human Body -- 2.5 Psychological and Behavioural State of the Human Body -- 2.6 Summary and Conclusions -- References -- Chapter 3 Physical, Physiological, and Biological Measurements -- 3.1 Introduction -- 3.2 Wearable Technology for Gait Monitoring -- 3.2.1 Accelerometer and Its Application to Gait Monitoring -- 3.2.1.1 How Accelerometers Operate -- 3.2.1.2 Accelerometers in Practice -- 3.2.2 Gyroscope and IMU -- 3.2.3 Force Plates -- 3.2.4 Goniometer -- 3.2.5 Electromyography -- 3.2.6 Sensing Fabric -- 3.3 Physiological Sensors -- 3.3.1 Multichannel Measurement of the Nerves Electric Potentials -- 3.3.2 Other Sensors -- 3.4 Biological Sensors -- 3.4.1 The Structures of Biological Sensors - The Principles -- 3.4.2 Emerging Biosensor Technologies -- 3.5 Conclusions -- References -- Chapter 4 Ambulatory and Popular Sensor Measurements -- 4.1 Introduction -- 4.2 Heart Rate -- 4.2.1 HR During Physical Exercise -- 4.3 Respiration -- 4.4 Blood Oxygen Saturation Level -- 4.5 Blood Pressure -- 4.5.1 Cuffless Blood Pressure Measurement -- 4.6 Blood Glucose -- 4.7 Body Temperature -- 4.8 Commercial Sensors -- 4.9 Conclusions -- References -- Chapter 5 Polysomnography and Sleep Analysis -- 5.1 Introduction -- 5.2 Polysomnography -- 5.3 Sleep Stage Classification -- 5.3.1 Sleep Stages -- 5.3.2 EEG‐Based Classification of Sleep Stages -- 5.3.2.1 Time Domain Features.

5.3.2.2 Frequency Domain Features -- 5.3.2.3 Time‐frequency Domain Features -- 5.3.2.4 Short‐time Fourier Transform -- 5.3.2.5 Wavelet Transform -- 5.3.2.6 Matching Pursuit -- 5.3.2.7 Empirical Mode Decomposition -- 5.3.2.8 Nonlinear Features -- 5.3.3 Classification Techniques -- 5.3.3.1 Using Neural Networks -- 5.3.3.2 Application of CNNs -- 5.3.4 Sleep Stage Scoring Using CNN -- 5.4 Monitoring Movements and Body Position During Sleep -- 5.5 Conclusions -- References -- Chapter 6 Noninvasive, Intrusive, and Nonintrusive Measurements -- 6.1 Introduction -- 6.2 Noninvasive Monitoring -- 6.3 Contactless Monitoring -- 6.3.1 Remote Photoplethysmography -- 6.3.1.1 Derivation of Remote PPG -- 6.3.2 Spectral Analysis Using Autoregressive Modelling -- 6.3.3 Estimation of Physiological Parameters Using Remote PPG -- 6.3.3.1 Heart Rate Estimation -- 6.3.3.2 Respiratory Rate Estimation -- 6.3.3.3 Blood Oxygen Saturation Level Estimation -- 6.3.3.4 Pulse Transmit Time Estimation -- 6.3.3.5 Video Pre‐processing -- 6.3.3.6 Selection of Regions of Interest -- 6.3.3.7 Derivation of the rPPG Signal -- 6.3.3.8 Processing rPPG Signals -- 6.3.3.9 Calculation of rPTT/dPTT -- 6.4 Implantable Sensor Systems -- 6.5 Conclusions -- References -- Chapter 7 Single and Multiple Sensor Networking for Gait Analysis -- 7.1 Introduction -- 7.2 Gait Events and Parameters -- 7.2.1 Gait Events -- 7.2.2 Gait Parameters -- 7.2.2.1 Temporal Gait Parameters -- 7.2.2.2 Spatial Gait Parameters -- 7.2.2.3 Kinetic Gait Parameters -- 7.2.2.4 Kinematic Gait Parameters -- 7.3 Standard Gait Measurement Systems -- 7.3.1 Foot Plantar Pressure System -- 7.3.2 Force‐plate Measurement System -- 7.3.3 Optical Motion Capture Systems -- 7.3.4 Microsoft Kinect Image and Depth Sensors -- 7.4 Wearable Sensors for Gait Analysis -- 7.4.1 Single Sensor Platforms -- 7.4.2 Multiple Sensor Platforms.

7.5 Gait Analysis Algorithms Based on Accelerometer/Gyroscope -- 7.5.1 Estimation of Gait Events -- 7.5.2 Estimation of Gait Parameters -- 7.5.2.1 Estimation of Orientation -- 7.5.2.2 Estimating Angles Using Accelerometers -- 7.5.2.3 Estimating Angles Using Gyroscopes -- 7.5.2.4 Fusing Accelerometer and Gyroscope Data -- 7.5.2.5 Quaternion Based Estimation of Orientation -- 7.5.2.6 Step Length Estimation -- 7.6 Conclusions -- References -- Chapter 8 Popular Health Monitoring Systems -- 8.1 Introduction -- 8.2 Technology for Data Acquisition -- 8.3 Physiological Health Monitoring Technologies -- 8.3.1 Predicting Patient Deterioration -- 8.3.2 Ambient Assisted Living: Monitoring Daily Living Activities -- 8.3.3 Monitoring Chronic Obstructive Pulmonary Disease Patients -- 8.3.4 Movement Tracking and Fall Detection/Prevention -- 8.3.5 Monitoring Patients with Dementia -- 8.3.6 Monitoring Patients with Parkinson's Disease -- 8.3.7 Odour Sensitivity Measurement -- 8.4 Conclusions -- References -- Chapter 9 Machine Learning for Sensor Networks -- 9.1 Introduction -- 9.2 Clustering Approaches -- 9.2.1 k‐means Clustering Algorithm -- 9.2.2 Iterative Self‐organising Data Analysis Technique -- 9.2.3 Gap Statistics -- 9.2.4 Density‐based Clustering -- 9.2.5 Affinity‐based Clustering -- 9.2.6 Deep Clustering -- 9.2.7 Semi‐supervised Clustering -- 9.2.7.1 Basic Semi‐supervised Techniques -- 9.2.7.2 Deep Semi‐supervised Techniques -- 9.2.8 Fuzzy Clustering -- 9.3 Classification Algorithms -- 9.3.1 Decision Trees -- 9.3.2 Random Forest -- 9.3.3 Linear Discriminant Analysis -- 9.3.4 Support Vector Machines -- 9.3.5 k‐nearest Neighbour -- 9.3.6 Gaussian Mixture Model -- 9.3.7 Logistic Regression -- 9.3.8 Reinforcement Learning -- 9.3.9 Artificial Neural Networks -- 9.3.9.1 Deep Neural Networks -- 9.3.9.2 Convolutional Neural Networks -- 9.3.9.3 Recent DNN Approaches.

9.3.10 Gaussian Processes -- 9.3.11 Neural Processes -- 9.3.12 Graph Convolutional Networks -- 9.3.13 Naïve Bayes Classifier -- 9.3.14 Hidden Markov Model -- 9.3.14.1 Forward Algorithm -- 9.3.14.2 Backward Algorithm -- 9.3.14.3 HMM Design -- 9.4 Common Spatial Patterns -- 9.5 Applications of Machine Learning in BSNs and WSNs -- 9.5.1 Human Activity Detection -- 9.5.2 Scoring Sleep Stages -- 9.5.3 Fault Detection -- 9.5.4 Gas Pipeline Leakage Detection -- 9.5.5 Measuring Pollution Level -- 9.5.6 Fatigue‐tracking and Classification System -- 9.5.7 Eye‐blink Artefact Removal from EEG Signals -- 9.5.8 Seizure Detection -- 9.5.9 BCI Applications -- 9.6 Conclusions -- References -- Chapter 10 Signal Processing for Sensor Networks -- 10.1 Introduction -- 10.2 Signal Processing Problems for Sensor Networks -- 10.3 Fundamental Concepts in Signal Processing -- 10.3.1 Nonlinearity of the Medium -- 10.3.2 Nonstationarity -- 10.3.3 Signal Segmentation -- 10.3.4 Signal Filtering -- 10.4 Mathematical Data Models -- 10.4.1 Linear Models -- 10.4.1.1 Prediction Method -- 10.4.1.2 Prony's Method -- 10.4.1.3 Singular Spectrum Analysis -- 10.4.2 Nonlinear Modelling -- 10.4.3 Gaussian Mixture Model -- 10.5 Transform Domain Signal Analysis -- 10.6 Time‐frequency Domain Transforms -- 10.6.1 Short‐time Fourier Transform -- 10.6.2 Wavelet Transform -- 10.6.2.1 Continuous Wavelet Transform -- 10.6.2.2 Examples of Continuous Wavelets -- 10.6.2.3 Discrete Time Wavelet Transform -- 10.6.3 Multiresolution Analysis -- 10.6.4 Synchro‐squeezing Wavelet Transform -- 10.7 Adaptive Filtering -- 10.8 Cooperative Adaptive Filtering -- 10.8.1 Diffusion Adaptation -- 10.9 Multichannel Signal Processing -- 10.9.1 Instantaneous and Convolutive BSS Problems -- 10.9.2 Array Processing -- 10.10 Signal Processing Platforms for BANs -- 10.11 Conclusions -- References.

Chapter 11 Communication Systems for Body Area Networks -- 11.1 Introduction -- 11.2 Short‐range Communication Systems -- 11.2.1 Bluetooth -- 11.2.2 Wi‐Fi -- 11.2.3 ZigBee -- 11.2.4 Radio Frequency Identification Devices -- 11.2.5 Ultrawideband -- 11.2.6 Other Short‐range Communication Methods -- 11.2.7 RF Modules Available in Market -- 11.3 Limitations, Interferences, Noise, and Artefacts -- 11.4 Channel Modelling -- 11.4.1 BAN Propagation Scenarios -- 11.4.1.1 On‐body Channel -- 11.4.1.2 In‐body Channel -- 11.4.1.3 Off‐body Channel -- 11.4.1.4 Body‐to‐body (or Interference) Channel -- 11.4.2 Recent Approaches to BAN Channel Modelling -- 11.4.3 Propagation Models -- 11.4.4 Standards and Guidelines -- 11.5 BAN‐WSN Communications -- 11.6 Routing in WBAN -- 11.6.1 Posture‐based Routing -- 11.6.2 Temperature‐based Routing -- 11.6.3 Cross‐layer Routing -- 11.6.4 Cluster‐based Routing -- 11.6.5 QoS‐based Routing -- 11.7 BAN‐building Network Integration -- 11.8 Cooperative BANs -- 11.9 BAN Security -- 11.10 Conclusions -- References -- Chapter 12 Energy Harvesting Enabled Body Sensor Networks -- 12.1 Introduction -- 12.2 Energy Conservation -- 12.3 Network Capacity -- 12.4 Energy Harvesting -- 12.5 Challenges in Energy Harvesting -- 12.6 Types of Energy Harvesting -- 12.6.1 Harvesting Energy from Kinetic Sources -- 12.6.2 Energy Sources from Radiant Sources -- 12.6.3 Energy Harvesting from Thermal Sources -- 12.6.4 Energy Harvesting from Biochemical and Chemical Sources -- 12.7 Topology Control -- 12.8 Typical Energy Harvesters for BSNs -- 12.9 Predicting Availability of Energy -- 12.10 Reliability of Energy Storage -- 12.11 Conclusions -- References -- Chapter 13 Quality of Service, Security, and Privacy for Wearable Sensor Data -- 13.1 Introduction -- 13.2 Threats to a BAN -- 13.2.1 Denial‐of‐service -- 13.2.2 Man‐in‐the‐middle Attack.

13.2.3 Phishing and Spear Phishing Attacks.

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