Cognitive Computing for Internet of Medical Things

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
List of Contributors
1. Toward the Internet of Things and Its Applications: A Review on Recent Innovations and Challenges
1.1 Introduction
1.1.1 Sensor Layer
1.1.2 Communication Layer
1.1.3 Network Device Layer
1.1.4 Data Center Layer
1.1.5 Application and Analytics Layer
1.2 IoT and Transportation
1.2.1 Smart Parking
1.2.2 Peer-to-Peer Ridesharing
1.2.3 Self-Driving Cars
1.3 IoT and Smart Cities
1.3.1 Telemedicine
1.3.2 Smart Security
1.3.3 Real-Time Environment Monitoring System
1.4 Precision Agriculture
1.5 Conclusions
References
2. Introduction to Cognitive Computing
2.1 Introduction
2.2 Evolution of Cognitive Computing
2.3 Comparison
2.3.1 Example Case
2.4 Dimensions of Cognitive Computing
2.4.1 Reasoning
2.4.2 Relating
2.4.3 Perception
2.4.4 Learning
2.5 Architecture of Cognitive Computing
2.5.1 IoT in Cognitive Computing
2.5.2 Big Data Analysis in Cognitive Computing
2.5.3 Cloud and Cognitive Computing
2.6 Supporting Technologies for Cognitive Computing
2.6.1 Reinforcement Learning
2.6.1.1 Components
2.6.1.2 Approaches
2.6.2 Cognitive Computing and Deep Learning
2.6.3 Cognitive Computing and Image Processing
2.7 Cognitive Analytics (Coganalytics)
2.8 Applications of Cognitive Computing
2.8.1 Cognitive Computing in Robotic Industry
2.8.2 Cognitive Computing in Emotion Communication
2.8.3 Cognitive Computing in Retail and Logistics Industry
2.8.4 Cognitive Computing in Banking and Finance
2.8.5 Cognitive Computing in the Power and Energy Sector
2.8.6 Cognitive Computing in Cybersecurity
2.8.7 Cognitive Computing in Healthcare
2.8.7.1 Impacts of Cognitive Computing in Healthcare
2.9 Conclusions
References
3. IoT with 5G in Healthcare Systems
3.1 Introduction
3.1.1 Examples of IoT in Healthcare
3.1.2 Working Principles of IoT in Healthcare
3.1.3 Advantages of IoT in Healthcare
3.1.4 Obstacles of IoT in Healthcare
3.2 5G Connectivity
3.2.1 5G-IoT Healthcare Applications
3.2.1.1 From Vision to Reality
3.2.1.2 Abilities of VOLANSYS 5G
3.2.2 5G Connectivity, e-Health, and m-Health
3.2.3 5G in Revolutionized Healthcare
3.2.3.1 Fast Transmission of Huge Imaging Documents
3.2.3.2 Broadening Telemedicine Administrations
3.2.3.3 Enhancing Augmented Reality, Virtual Reality, and Spatial Computing
3.2.3.4 Predictable, Constant Remote Observing
3.2.3.5 ML & AI
3.3 Internet of Medical Things (IoMT)
3.3.1 IoT Security
3.3.2 IoMT Obstacles
3.3.3 Opportunities in Healthcare Information Technology
3.3.4 Computerized Therapeutics (DTx)
3.4 Conclusions
References
4. Communication Protocols for IoMT-Based Healthcare Systems
4.1 Introduction
4.2 Architecture of IoT
4.2.1 Three-Layer Architecture
4.2.2 Middleware Architecture
4.2.3 Service-Oriented Architecture (SOA)
4.2.4 Five-Layer Architecture
4.2.5 Cloud-Specific Architecture
4.3 Architecture of IoMT
4.4 IoMT Communication Protocols
4.4.1 Zigbee Protocol
4.4.2 Radio-Frequency Identification (RFID)
4.4.3 Time Synchronized Mesh Protocol (TSMP)
4.4.4 Near-Field Communication (NFC)
4.4.5 Bluetooth IEEE 802.15.1
4.4.6 Wireless-HART
4.4.7 Weightless
4.4.8 Wireless Fidelity (Wi-Fi)
4.4.9 Constrained Application Protocol (CoAP)
4.4.10 Message Queuing Telemetry Transport (MQTT)
4.4.11 Advanced Message Queuing Protocol (AMQP)
4.4.12 Data Distribution Service (DDS)
4.4.13 Extensible Messaging and Presence Protocol (XMPP)
4.4.14 WebSocket
4.5 Conclusions
References
5. Security and Privacy of Biomedical Data in IoMT
5.1 Introduction
5.1.1 IoMT Architecture
5.1.1.1 Application Layer
5.1.1.2 Network Layer
5.1.1.3 Perception Layer
5.2 Background
5.2.1 Privacy and Security Needs for IHS
5.2.1.1 Requirements at Data Level
5.2.1.2 Requirements at Sensor Level
5.2.1.3 Requirements at Personal Server Level
5.2.1.4 Requirements at Medical Server Level
5.2.2 IHS Security Schemes
5.2.3 The Relationship between IoMT Vulnerabilities and Threats
5.3 Findings
5.3.1 Security Plans for Installable IoMT Devices
5.3.2 Targeted Security and Privacy Aspects in IoMT
5.3.2.1 Breach of Data Confidentiality
5.3.2.2 Attacks Employing Social Engineering (SE)
5.3.2.3 Privacy Invasion
5.3.2.4 Message Validation and Data Security Threats
5.3.2.5 Tool/User Authentication Threats
5.3.2.6 Malware Attacks
5.3.3 IoMT Security Measures
5.3.3.1 Non-Technical Safety Precautions
5.3.3.2 Technological Security Procedures
5.4 Discussion
5.4.1 Inexpensive Cryptographic Algorithms
5.4.2 Inexpensive Authentication Protocols
5.4.3 Security Architecture with Layer
5.4.4 Detecting Sensor Anomalies in Medical Devices
5.5 Conclusions and Future Work
5.5.1 Future Research Directions
References
6. Cyber-Security Threats to IoMT-Enabled Healthcare Systems
6.1 Introduction
6.1.1 Where Did IoMT Comes from
6.1.2 Heart of the Problem
6.1.3 Motivations
6.1.4 Contributions
6.2 IoMT Framework, Perception, and Future
6.2.1 Devices of IoMT
6.2.2 Application and Service Domains of IoMT
6.3 IoMT Challenges, Risks, and Concerns
6.3.1 IoMT Challenges
6.3.2 Risks within IoMT
6.3.3 IoMT Concerns
6.4 Cyber-Attacks Aligned with IoMT
6.4.1 Features of Cyber-Attacks
6.4.2 IoMT Targeted Security Aspects
6.5 Methodologies
6.5.1 Dataset Description
6.5.1.1 Characteristic Details
6.5.2 Existing Method
6.5.3 Proposed Method
6.5.3.1 Model Design
6.5.3.2 Experiment Setup
6.5.4 Performance Metrics and Result Evaluation
6.5.4.1 Evaluation Metrics
6.5.4.2 Result Evaluation
6.5.4.3 Model Structure and Performance
6.6 Conclusions
References
7. Using Self-Organizing Map to Find Cardiac Risk Based on Body Mass Index
7.1 Introduction
7.2 Literature Survey
7.3 Methodology
7.3.1 BMI Scales
7.4 Results and Discussion
7.5 Conclusions
References
8. Embedded Medical IoT Devices for Monitoring and Diagnosing Patient Health in Rural Areas Peoples Using IoMT Technology
8.1 Introduction
8.2 HIoT Technology
8.2.1 Location Technology
8.2.2 Technology of Identification
8.2.3 Technology of Communication
8.3 Applications and Services of IoMT
8.3.1 Services
8.3.1.1 Ambient Assisted Living
8.3.1.2 Mobile IoT
8.3.1.3 Wearable Devices
8.3.1.4 Cognitive Computing
8.3.1.5 Reaction of Drug
8.3.1.6 Blockchain
8.3.1.7 Child Health Information
8.3.2 Applications
8.3.2.1 ECG Monitoring
8.3.2.2 Monitoring the Glucose Level
8.3.2.3 Temperature Monitoring
8.3.2.4 Blood Pressure Monitoring
8.3.2.5 Measuring Oxygen Saturation
8.3.2.6 Measuring and Monitoring Asthma
8.3.2.7 Monitoring the Mood
8.3.2.8 Management of Medication
8.3.2.9 Management of Wheelchair
8.3.2.10 Rehabilitation System
8.3.2.11 Other Notable Applications
8.4 Limitations, Challenges, and Opportunities
8.4.1 Servicing and Maintenance Cost
8.4.2 Power Usage
8.4.3 Standardization
8.4.4 Data Privacy and Security
8.4.5 Scalability
8.4.6 Identification
8.4.7 Self-/Automatic Configuration
8.4.8 Continuous Monitoring
8.4.9 Investigation of New Diseases
8.4.10 Impact on Environment
8.5 Conclusions
References
9. Case Studies: Cancer Prediction and Diagnosis in the IoMT Environment
9.1 Introduction
9.1.1 Types of Cancerous Tumors
9.1.2 Cogitation on Cancer Prediction
9.2 ML and IoMT
9.3 Best Cancer Prediction and Diagnosis Using Supervised Algorithm
9.4 Early Detection of Tumor Cells and Symptoms of Breast Cancer
9.5 Various ML-Based Breast Cancer Classification
9.5.1 LR Classifier
9.5.2 SVM in Cancer Detection
9.5.3 DT Classifier
9.6 Performance Evaluation
9.6.1 Result Analysis of LR Classifier
9.6.1.1 Confusion Matrix for LR Classifier
9.6.2 Analysis of SVM Classifier
9.6.2.1 Confusion Matrix for SVM Classifier
9.6.3 Analysis of DT Classifier
9.6.3.1 Confusion Matrix for DT Classifier
9.7 Comparative Analysis of Various Classifiers
9.8 Conclusions
References
10. A Deep Exploration of Imaging Diagnosis Approaches for IoMT-Based Coronavirus Disease of 2019 Diagnosis System – A Case Study
10.1 Introduction
10.2 Backgrounds of Imaging Techniques
10.2.1 PET
10.2.2 Lung Ultrasound
10.2.3 MRI
10.3 Features to Be Pondered of COVID
10.4 Algorithms Deployed for Imaging Techniques
10.4.1 Visual Geometry Group Net
10.4.2 Inception V3 Designs
10.4.2.1 ResNet
10.4.2.2 DenseNet
10.4.2.3 Inf-Net
10.4.2.4 UNet
10.5 Machine Learning Techniques
10.6 Coronavirus Dataset
10.7 Related Works of Coronavirus Disease of 2019 and IoT
10.7.1 Coronavirus Disease of 2019 Symptoms Imaging System
10.8 Conclusions
References
Index