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Submit a ProposalSmart Healthcare System Design
 | Security and Privacy Aspects Edited by SK Hafizul Islam and Debabrata Samanta Series: Advances in Learning Analytics for Intelligent Cloud-IoT Systems Copyright: 2021 | Status: Published ISBN: 9781119791683 | Hardcover | 380 pages | 125 illustrations Price: $225 USD  |
One Line Description
This book deeply discusses the major challenges and issues for security and privacy aspects of smart health-care systems.
Audience
Researchers and industry engineers in information technology, artificial intelligence, cyber security, as well as designers of healthcare systems, will find this book very valuable.
Researchers and industry engineers in information technology, artificial intelligence, cyber security, as well as designers of healthcare systems, will find this book very valuable.
Description
The Internet-of-Things (IoT) has emerged as a powerful and promising technology, and though it has significant technological, social, and economic impacts, it also poses new security and privacy challenges. Compared with the traditional internet, the IoT has various embedded devices, mobile devices, a server, and the cloud, with different capabilities to support multiple services. The pervasiveness of these devices represents a huge attack surface and, since the IoT connects cyberspace to physical space, known as a cyber-physical system, IoT attacks not only have an impact on information systems, but also affect physical infrastructure, the environment, and even human security.
The purpose of this book is to help achieve a better integration between the work of researchers and practitioners in a single medium for capturing state-of-the-art IoT solutions in healthcare applications, and to address how to improve the proficiency of wireless sensor networks (WSNs) in healthcare. It explores possible automated solutions in everyday life, including the structures of healthcare systems built to handle large amounts of data, thereby improving clinical decisions. The 14 separate chapters address various aspects of the IoT system, such as design challenges, theory, various protocols, implementation issues, as well as several case studies.
Smart Healthcare System Design covers the introduction, development, and applications of smart healthcare models that represent the current state-of-the-art of various domains. The primary focus is on theory, algorithms, and their implementation targeted at real-world problems. It will deal with different applications to give the practitioner a flavor of how IoT architectures are designed and introduced into various situations.
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The Internet-of-Things (IoT) has emerged as a powerful and promising technology, and though it has significant technological, social, and economic impacts, it also poses new security and privacy challenges. Compared with the traditional internet, the IoT has various embedded devices, mobile devices, a server, and the cloud, with different capabilities to support multiple services. The pervasiveness of these devices represents a huge attack surface and, since the IoT connects cyberspace to physical space, known as a cyber-physical system, IoT attacks not only have an impact on information systems, but also affect physical infrastructure, the environment, and even human security.
The purpose of this book is to help achieve a better integration between the work of researchers and practitioners in a single medium for capturing state-of-the-art IoT solutions in healthcare applications, and to address how to improve the proficiency of wireless sensor networks (WSNs) in healthcare. It explores possible automated solutions in everyday life, including the structures of healthcare systems built to handle large amounts of data, thereby improving clinical decisions. The 14 separate chapters address various aspects of the IoT system, such as design challenges, theory, various protocols, implementation issues, as well as several case studies.
Smart Healthcare System Design covers the introduction, development, and applications of smart healthcare models that represent the current state-of-the-art of various domains. The primary focus is on theory, algorithms, and their implementation targeted at real-world problems. It will deal with different applications to give the practitioner a flavor of how IoT architectures are designed and introduced into various situations.
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Author / Editor Details
SK Hafizul Islam received his PhD degree in Computer Science and Engineering in 2013 from the Indian Institute of Technology [IIT (ISM)] Dhanbad, Jharkhand, India. He is an assistant professor in the Department of Computer Science and Engineering, Indian Institute of Information Technology Kalyani (IIIT Kalyani), West Bengal, India. He has authored or coauthored 110 research papers in journals and conference proceedings.
SK Hafizul Islam received his PhD degree in Computer Science and Engineering in 2013 from the Indian Institute of Technology [IIT (ISM)] Dhanbad, Jharkhand, India. He is an assistant professor in the Department of Computer Science and Engineering, Indian Institute of Information Technology Kalyani (IIIT Kalyani), West Bengal, India. He has authored or coauthored 110 research papers in journals and conference proceedings.
Debabrata Samanta is an assistant professor in the Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India. He obtained his PhD in Computer Science and Engg. from the National Institute of Technology, Durgapur, India, in the area of SAR Image Processing. He is the owner of 17 Indian patents and has authored and coauthored more than 135 research papers in international journals.
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Table of Contents
Preface
Acknowledgments
1. Machine Learning Technologies in IoT EEG-Based Healthcare Prediction Karthikeyan M.P., Krishnaveni K. and Muthumani N.
1.1 Introduction
1.1.1 Descriptive Analytics
1.1.2 Analytical Methods
1.1.3 Predictive Analysis
1.1.4 Behavioral Analysis
1.1.5 Data Interpretation
1.1.6 Classification
1.2 Related Works
1.3 Problem Definition
1.4 Research Methodology
1.4.1 Components Used
1.4.2 Specifications and Description About Components
1.4.2.1 Arduino
1.4.2.2 EEG Sensor—Mindwave Mobile Headset
1.4.2.3 Raspberry pi
1.4.2.4 Working
1.4.3 Cloud Feature Extraction
1.4.4 Feature Optimization
1.4.5 Classification and Validation
1.5 Result and Discussion
1.5.1 Result
1.5.2 Discussion
1.6 Conclusion
1.6.1 Future Scope
References
2. Smart Health Application for Remote Tracking of Ambulatory Patients
Shariq Aziz Butt, Muhammad Waqas Anjum, Syed Areeb Hassan, Arindam Garai and Edeh Michael Onyema
2.1 Introduction
2.2 Literature Work
2.3 Smart Computing for Smart Health for Ambulatory Patients
2.4 Challenges With Smart Health
2.4.1 Emergency Support
2.4.2 The Issue With Chronic Disease Monitoring
2.4.3 An Issue With the Tele-Medication
2.4.4 Mobility of Doctor
2.4.5 Application User Interface Issue
2.5 Security Threats
2.5.1 Identity Privacy
2.5.2 Query Privacy
2.5.3 Location of Privacy
2.5.4 Footprint Privacy and Owner Privacy
2.6 Applications of Fuzzy Set Theory in Healthcare and Medical Problems
2.7 Conclusion
References
3. Data-Driven Decision Making in IoT Healthcare Systems—COVID-19: A Case Study
Saroja S., Haseena S. and Blessa Binolin Pepsi M.
3.1 Introduction
3.1.1 Pre-Processing
3.1.2 Classification Algorithms
3.1.2.1 Dummy Classifier
3.1.2.2 Support Vector Machine (SVM)
3.1.2.3 Gradient Boosting
3.1.2.4 Random Forest
3.1.2.5 Ada Boost
3.2 Experimental Analysis
3.3 Multi-Criteria Decision Making (MCDM) Procedure
3.3.1 Simple Multi Attribute Rating Technique (SMART)
3.3.1.1 COVID-19 Disease Classification Using SMART
3.3.2 Weighted Product Model (WPM)
3.3.2.1 COVID-19 Disease Classification
Using WPM
3.3.3 Method for Order Preference by Similarity to the Ideal Solution (TOPSIS)
3.3.3.1 COVID-19 Disease Classification Using TOPSIS
3.4 Conclusion
References
4. Touch and Voice-Assisted Multilingual Communication Prototype for ICU Patients Specific to COVID-19
B. Rajesh Kanna and C.Vijayalakshmi
4.1 Introduction and Motivation
4.1.1 Existing Interaction Approaches and Technology
4.1.2 Challenges and Gaps
4.2 Proposed Prototype of Touch and Voice-Assisted Multilingual Communication
4.3 A Sample Case Study
4.4 Conclusion
References
5. Cloud-Assisted IoT System for Epidemic Disease Detection and Spread Monitoring
Himadri Nath Saha, Reek Roy and Sumanta Chakraborty
5.1 Introduction
5.2 Background & Related Works
5.3 Proposed Model
5.3.1 ThinkSpeak
5.3.2 Blood Oxygen Saturation (SpO2)
5.3.3 Blood Pressure (BP)
5.3.4 Electrocardiogram (ECG)
5.3.5 Body Temperature (BT)
5.3.6 Respiration Rate (RR)
5.3.7 Environmental Parameters
5.4 Methodology
5.5 Performance Analysis
5.6 Future Research Direction
5.7 Conclusion
References
6. Impact of Healthcare 4.0 Technologies for Future Capacity Building to Control Epidemic Diseases
Himadri Nath Saha, Sumanta Chakraborty, Sourav Paul, Rajdeep Ghosh and Dipanwita Chakraborty Bhattacharya
6.1 Introduction
6.2 Background and Related Works
6.3 System Design and Architecture
6.4 Methodology
6.5 Performance Analysis
6.6 Future Research Direction
6.7 Conclusion
References
7. Security and Privacy of IoT Devices in Healthcare Systems
Himadri Nath Saha and Subhradip Debnath
7.1 Introduction
7.2 Background and Related Works
7.3 Proposed System Design and Architecture
7.3.1.1 Wireless Body Area Network
7.3.1.2 Centralized Network Coordinator
7.3.1.3 Local Server
7.3.1.4 Cloud Server
7.3.1.5 Dedicated Network Connection
7.4 Methodology
7.5 Performance Analysis
7.6 Future Research Direction
7.7 Conclusion
References
8. An IoT-Based Diet Monitoring Healthcare System for Women Suganyadevi S., Shamia D. and Balasamy K.
8.1 Introduction
8.2 Background
8.2.1 Food Consumption
8.2.2 Food Consumption Monitoring
8.2.3 Health Monitoring Methods Using Physical Methodology
8.2.3.1 Traditional Form of Self-Report
8.2.3.2 Self-Reporting Methodology Through Smart Phones
8.2.3.3 Food Frequency Questionnaire
8.2.4 Methods for Health Tracking Using Automated Approach
8.2.4.1 Pressure Process
8.2.4.2 Surveillance Video Method
8.2.4.3 Method of Doppler Sensing
8.3 Necessity of Wearable Approach?
8.4 Different Approaches for Wearable Sensing
8.4.1 Approach of Acoustics
8.4.1.1 Detection of Chewing
8.4.1.2 Detection of Swallowing
8.4.1.3 Shared Chewing/Swallowing Discovery
8.5 Description of the Methodology
8.6 Description of Various Components Used
8.6.1 Sensors
8.6.1.1 Sensors for Cardio-Vascular Monitoring
8.6.1.2 Sensors for Activity Monitoring
8.6.1.3 Sensors for Body Temperature Monitoring
8.6.1.4 Sensor for Galvanic Skin Response (GSR) Monitoring
8.6.1.5 Sensor for Monitoring the Blood Oxygen Saturation (SpO2)
8.7 Strategy of Communication for Wearable Systems
8.8 Conclusion
References
9. A Secure Framework for Protecting Clinical Data in Medical IoT Environment
Balasamy K., Krishnaraj N., Ramprasath J. and Ramprakash P.
9.1 Introduction
9.1.1 Medical IoT Background & Perspective
9.1.1.1 Medical IoT Communication Network
9.2 Medical IoT Application Domains
9.2.1 Smart Doctor
9.2.2 Smart Medical Practitioner
9.2.3 Smart Technology
9.2.4 Smart Receptionist
9.2.5 Disaster Response Systems (DRS)
9.3 Medical IoT Concerns
9.3.1 Security Concerns
9.3.2 Privacy Concerns
9.3.3 Trust Concerns
9.4 Need for Security in Medical IoT
9.5 Components for Enhancing Data Security in Medical IoT
9.5.1 Confidentiality
9.5.2 Integrity
9.5.3 Authentication
9.5.4 Non-Repudiation
9.5.5 Privacy
9.6 Vulnerabilities in Medical IoT Environment
9.6.1 Patient Privacy Protection
9.6.2 Patient Safety
9.6.3 Unauthorized Access
9.6.4 Medical IoT Security Constraints
9.7 Solutions for IoT Healthcare Cyber-Security
9.7.1 Architecture of the Smart Healthcare System
9.7.1.1 Data Perception Layer
9.7.1.2 Data Communication Layer
9.7.1.3 Data Storage Layer
9.7.1.4 Data Application Layer
9.8 Execution of Trusted Environment
9.8.1 Root of Trust Security Services
9.8.2 Chain of Trust Security Services
9.9 Patient Registration Using Medical IoT Devices
9.9.1 Encryption
9.9.2 Key Generation
9.9.3 Security by Isolation
9.9.4 Virtualization
9.10 Trusted Communication Using Block Chain
9.10.1 Record Creation Using IoT Gateways
9.10.2 Accessibility to Patient Medical History
9.10.3 Patient Enquiry With Hospital Authority
9.10.4 Block Chain Based IoT System Architecture
9.10.4.1 First Layer
9.10.4.2 Second Layer
9.10.4.3 Third Layer
9.11 Conclusion
References
10. Efficient Data Transmission and Remote Monitoring System for IoT Applications
Laith Farhan, Firas MaanAbdulsattar, Laith Alzubaidi, Mohammed A. Fadhel, Banu ÇalışUslu and Muthana Al-Amidie
10.1 Introduction
10.2 Network Configuration
10.2.1 Message Queuing Telemetry Transport (MQTT) Protocol
10.2.2 Embedded Database SQLite
10.2.3 Eclipse Paho Library
10.2.4 Raspberry Pi Single Board Computer
10.2.5 Custard Pi Add-On Board
10.2.6 Pressure Transmitter (Type 663)
10.3 Data Filtering and Predicting Processes
10.3.1 Filtering Process
10.3.2 Predicting Process
10.3.3 Remote Monitoring Systems
10.4 Experimental Setup
10.4.1 Implementation Using Python
10.4.1.1 Prerequisites
10.4.2 Monitoring Data
10.4.3 Experimental Results
10.4.3.1 IoT Device Results
10.4.3.2 Traditional Network Results
10.5 Conclusion
References
11. IoT in Current Times and its Prospective Advancements
T. Venkat Narayana Rao, Abhishek Duggirala, Muralidhar Kurni and Syed Tabassum Sultana
11.1 Introduction
11.1.1 Introduction to Industry 4.0
11.1.2 Introduction to IoT
11.1.3 Introduction to IIoT
11.2 How IIoT Advances Industrial Engineering in Industry 4.0 Era
11.3 IoT and its Current Applications
11.3.1 Home Automation
11.3.2 Wearables
11.3.3 Connected Cars
11.3.4 Smart Grid
11.4 Application Areas of IIoT
11.4.1 IIoT in Healthcare
11.4.2 IIoT in Mining
11.4.3 IIoT in Agriculture
11.4.4 IIoT in Aerospace
11.4.5 IIoT in Smart Cities
11.4.6 IIoT in Supply Chain Management
11.5 Challenges of Existing Systems
11.5.1 Security
11.5.2 Integration
11.5.3 Connectivity Issues
11.6 Future Advancements
11.6.1 Data Analytics in IoT
11.6.2 Edge Computing
11.6.3 Secured IoT Through Blockchain
11.6.4 A Fusion of AR and IoT
11.6.5 Accelerating IoT Through 5G
11.7 Case Study of DeWalt
11.8 Conclusion
References
12. Reliance on Artificial Intelligence, Machine Learning and Deep Learning in the Era of Industry 4.0
T. Venkat Narayana Rao, Akhila Gaddam, Muralidhar Kurni and K. Saritha
12.1 Introduction to Artificial Intelligence
12.1.1 History of AI
12.1.2 Views of AI
12.1.3 Types of AI
12.1.4 Intelligent Agents
12.2 AI and its Related Fields
12.3 What is Industry 4.0?
12.4 Industrial Revolutions
12.4.1 First Industrial Revolution (1765)
12.4.2 Second Industrial Revolution (1870)
12.4.3 Third Industrial Revolution (1969)
12.4.4 Fourth Industrial Revolution
12.5 Reasons for Shifting Towards Industry 4.0
12.6 Role of AI in Industry 4.0
12.7 Role of ML in Industry 4.0
12.8 Role of Deep Learning in Industry 4.0
12.9 Applications of AI, ML, and DL in Industry 4.0
12.10 Challenges
12.11 Top Companies That Use AI to Augment Manufacturing Processes in the Era of Industry 4.0
12.12 Conclusion
References
13. The Implementation of AI and AI-Empowered Imaging System to Fight Against COVID-19—A Review
Sanjay Chakraborty and Lopamudra Dey
13.1 Introduction
13.2 AI-Assisted Methods
13.2.1 AI-Driven Tools to Diagnose COVID-19 and Drug Discovery
13.2.2 AI-Empowered Image Processing to Diagnosis
13.3 Optimistic Treatments and Cures
13.4 Challenges and Future Research Issues
13.5 Conclusion
References
14. Implementation of Machine Learning Techniques for the Analysis of Transmission Dynamics of COVID-19
C. Vijayalakshmi and S. Bangusha Devi
14.1 Introduction
14.2 Data Analysis
14.3 Methodology
14.3.1 Linear Regression Model
14.3.2 Time Series Model
14.4 Results and Discussions
14.4.1 Model Estimation and Studying its Adequacy
14.4.2 Regression Model for Daily New Cases and New Deaths
14.5 Conclusions
References
Index
Back to Top
Preface
Acknowledgments
1. Machine Learning Technologies in IoT EEG-Based Healthcare Prediction Karthikeyan M.P., Krishnaveni K. and Muthumani N.
1.1 Introduction
1.1.1 Descriptive Analytics
1.1.2 Analytical Methods
1.1.3 Predictive Analysis
1.1.4 Behavioral Analysis
1.1.5 Data Interpretation
1.1.6 Classification
1.2 Related Works
1.3 Problem Definition
1.4 Research Methodology
1.4.1 Components Used
1.4.2 Specifications and Description About Components
1.4.2.1 Arduino
1.4.2.2 EEG Sensor—Mindwave Mobile Headset
1.4.2.3 Raspberry pi
1.4.2.4 Working
1.4.3 Cloud Feature Extraction
1.4.4 Feature Optimization
1.4.5 Classification and Validation
1.5 Result and Discussion
1.5.1 Result
1.5.2 Discussion
1.6 Conclusion
1.6.1 Future Scope
References
2. Smart Health Application for Remote Tracking of Ambulatory Patients
Shariq Aziz Butt, Muhammad Waqas Anjum, Syed Areeb Hassan, Arindam Garai and Edeh Michael Onyema
2.1 Introduction
2.2 Literature Work
2.3 Smart Computing for Smart Health for Ambulatory Patients
2.4 Challenges With Smart Health
2.4.1 Emergency Support
2.4.2 The Issue With Chronic Disease Monitoring
2.4.3 An Issue With the Tele-Medication
2.4.4 Mobility of Doctor
2.4.5 Application User Interface Issue
2.5 Security Threats
2.5.1 Identity Privacy
2.5.2 Query Privacy
2.5.3 Location of Privacy
2.5.4 Footprint Privacy and Owner Privacy
2.6 Applications of Fuzzy Set Theory in Healthcare and Medical Problems
2.7 Conclusion
References
3. Data-Driven Decision Making in IoT Healthcare Systems—COVID-19: A Case Study
Saroja S., Haseena S. and Blessa Binolin Pepsi M.
3.1 Introduction
3.1.1 Pre-Processing
3.1.2 Classification Algorithms
3.1.2.1 Dummy Classifier
3.1.2.2 Support Vector Machine (SVM)
3.1.2.3 Gradient Boosting
3.1.2.4 Random Forest
3.1.2.5 Ada Boost
3.2 Experimental Analysis
3.3 Multi-Criteria Decision Making (MCDM) Procedure
3.3.1 Simple Multi Attribute Rating Technique (SMART)
3.3.1.1 COVID-19 Disease Classification Using SMART
3.3.2 Weighted Product Model (WPM)
3.3.2.1 COVID-19 Disease Classification
Using WPM
3.3.3 Method for Order Preference by Similarity to the Ideal Solution (TOPSIS)
3.3.3.1 COVID-19 Disease Classification Using TOPSIS
3.4 Conclusion
References
4. Touch and Voice-Assisted Multilingual Communication Prototype for ICU Patients Specific to COVID-19
B. Rajesh Kanna and C.Vijayalakshmi
4.1 Introduction and Motivation
4.1.1 Existing Interaction Approaches and Technology
4.1.2 Challenges and Gaps
4.2 Proposed Prototype of Touch and Voice-Assisted Multilingual Communication
4.3 A Sample Case Study
4.4 Conclusion
References
5. Cloud-Assisted IoT System for Epidemic Disease Detection and Spread Monitoring
Himadri Nath Saha, Reek Roy and Sumanta Chakraborty
5.1 Introduction
5.2 Background & Related Works
5.3 Proposed Model
5.3.1 ThinkSpeak
5.3.2 Blood Oxygen Saturation (SpO2)
5.3.3 Blood Pressure (BP)
5.3.4 Electrocardiogram (ECG)
5.3.5 Body Temperature (BT)
5.3.6 Respiration Rate (RR)
5.3.7 Environmental Parameters
5.4 Methodology
5.5 Performance Analysis
5.6 Future Research Direction
5.7 Conclusion
References
6. Impact of Healthcare 4.0 Technologies for Future Capacity Building to Control Epidemic Diseases
Himadri Nath Saha, Sumanta Chakraborty, Sourav Paul, Rajdeep Ghosh and Dipanwita Chakraborty Bhattacharya
6.1 Introduction
6.2 Background and Related Works
6.3 System Design and Architecture
6.4 Methodology
6.5 Performance Analysis
6.6 Future Research Direction
6.7 Conclusion
References
7. Security and Privacy of IoT Devices in Healthcare Systems
Himadri Nath Saha and Subhradip Debnath
7.1 Introduction
7.2 Background and Related Works
7.3 Proposed System Design and Architecture
7.3.1.1 Wireless Body Area Network
7.3.1.2 Centralized Network Coordinator
7.3.1.3 Local Server
7.3.1.4 Cloud Server
7.3.1.5 Dedicated Network Connection
7.4 Methodology
7.5 Performance Analysis
7.6 Future Research Direction
7.7 Conclusion
References
8. An IoT-Based Diet Monitoring Healthcare System for Women Suganyadevi S., Shamia D. and Balasamy K.
8.1 Introduction
8.2 Background
8.2.1 Food Consumption
8.2.2 Food Consumption Monitoring
8.2.3 Health Monitoring Methods Using Physical Methodology
8.2.3.1 Traditional Form of Self-Report
8.2.3.2 Self-Reporting Methodology Through Smart Phones
8.2.3.3 Food Frequency Questionnaire
8.2.4 Methods for Health Tracking Using Automated Approach
8.2.4.1 Pressure Process
8.2.4.2 Surveillance Video Method
8.2.4.3 Method of Doppler Sensing
8.3 Necessity of Wearable Approach?
8.4 Different Approaches for Wearable Sensing
8.4.1 Approach of Acoustics
8.4.1.1 Detection of Chewing
8.4.1.2 Detection of Swallowing
8.4.1.3 Shared Chewing/Swallowing Discovery
8.5 Description of the Methodology
8.6 Description of Various Components Used
8.6.1 Sensors
8.6.1.1 Sensors for Cardio-Vascular Monitoring
8.6.1.2 Sensors for Activity Monitoring
8.6.1.3 Sensors for Body Temperature Monitoring
8.6.1.4 Sensor for Galvanic Skin Response (GSR) Monitoring
8.6.1.5 Sensor for Monitoring the Blood Oxygen Saturation (SpO2)
8.7 Strategy of Communication for Wearable Systems
8.8 Conclusion
References
9. A Secure Framework for Protecting Clinical Data in Medical IoT Environment
Balasamy K., Krishnaraj N., Ramprasath J. and Ramprakash P.
9.1 Introduction
9.1.1 Medical IoT Background & Perspective
9.1.1.1 Medical IoT Communication Network
9.2 Medical IoT Application Domains
9.2.1 Smart Doctor
9.2.2 Smart Medical Practitioner
9.2.3 Smart Technology
9.2.4 Smart Receptionist
9.2.5 Disaster Response Systems (DRS)
9.3 Medical IoT Concerns
9.3.1 Security Concerns
9.3.2 Privacy Concerns
9.3.3 Trust Concerns
9.4 Need for Security in Medical IoT
9.5 Components for Enhancing Data Security in Medical IoT
9.5.1 Confidentiality
9.5.2 Integrity
9.5.3 Authentication
9.5.4 Non-Repudiation
9.5.5 Privacy
9.6 Vulnerabilities in Medical IoT Environment
9.6.1 Patient Privacy Protection
9.6.2 Patient Safety
9.6.3 Unauthorized Access
9.6.4 Medical IoT Security Constraints
9.7 Solutions for IoT Healthcare Cyber-Security
9.7.1 Architecture of the Smart Healthcare System
9.7.1.1 Data Perception Layer
9.7.1.2 Data Communication Layer
9.7.1.3 Data Storage Layer
9.7.1.4 Data Application Layer
9.8 Execution of Trusted Environment
9.8.1 Root of Trust Security Services
9.8.2 Chain of Trust Security Services
9.9 Patient Registration Using Medical IoT Devices
9.9.1 Encryption
9.9.2 Key Generation
9.9.3 Security by Isolation
9.9.4 Virtualization
9.10 Trusted Communication Using Block Chain
9.10.1 Record Creation Using IoT Gateways
9.10.2 Accessibility to Patient Medical History
9.10.3 Patient Enquiry With Hospital Authority
9.10.4 Block Chain Based IoT System Architecture
9.10.4.1 First Layer
9.10.4.2 Second Layer
9.10.4.3 Third Layer
9.11 Conclusion
References
10. Efficient Data Transmission and Remote Monitoring System for IoT Applications
Laith Farhan, Firas MaanAbdulsattar, Laith Alzubaidi, Mohammed A. Fadhel, Banu ÇalışUslu and Muthana Al-Amidie
10.1 Introduction
10.2 Network Configuration
10.2.1 Message Queuing Telemetry Transport (MQTT) Protocol
10.2.2 Embedded Database SQLite
10.2.3 Eclipse Paho Library
10.2.4 Raspberry Pi Single Board Computer
10.2.5 Custard Pi Add-On Board
10.2.6 Pressure Transmitter (Type 663)
10.3 Data Filtering and Predicting Processes
10.3.1 Filtering Process
10.3.2 Predicting Process
10.3.3 Remote Monitoring Systems
10.4 Experimental Setup
10.4.1 Implementation Using Python
10.4.1.1 Prerequisites
10.4.2 Monitoring Data
10.4.3 Experimental Results
10.4.3.1 IoT Device Results
10.4.3.2 Traditional Network Results
10.5 Conclusion
References
11. IoT in Current Times and its Prospective Advancements
T. Venkat Narayana Rao, Abhishek Duggirala, Muralidhar Kurni and Syed Tabassum Sultana
11.1 Introduction
11.1.1 Introduction to Industry 4.0
11.1.2 Introduction to IoT
11.1.3 Introduction to IIoT
11.2 How IIoT Advances Industrial Engineering in Industry 4.0 Era
11.3 IoT and its Current Applications
11.3.1 Home Automation
11.3.2 Wearables
11.3.3 Connected Cars
11.3.4 Smart Grid
11.4 Application Areas of IIoT
11.4.1 IIoT in Healthcare
11.4.2 IIoT in Mining
11.4.3 IIoT in Agriculture
11.4.4 IIoT in Aerospace
11.4.5 IIoT in Smart Cities
11.4.6 IIoT in Supply Chain Management
11.5 Challenges of Existing Systems
11.5.1 Security
11.5.2 Integration
11.5.3 Connectivity Issues
11.6 Future Advancements
11.6.1 Data Analytics in IoT
11.6.2 Edge Computing
11.6.3 Secured IoT Through Blockchain
11.6.4 A Fusion of AR and IoT
11.6.5 Accelerating IoT Through 5G
11.7 Case Study of DeWalt
11.8 Conclusion
References
12. Reliance on Artificial Intelligence, Machine Learning and Deep Learning in the Era of Industry 4.0
T. Venkat Narayana Rao, Akhila Gaddam, Muralidhar Kurni and K. Saritha
12.1 Introduction to Artificial Intelligence
12.1.1 History of AI
12.1.2 Views of AI
12.1.3 Types of AI
12.1.4 Intelligent Agents
12.2 AI and its Related Fields
12.3 What is Industry 4.0?
12.4 Industrial Revolutions
12.4.1 First Industrial Revolution (1765)
12.4.2 Second Industrial Revolution (1870)
12.4.3 Third Industrial Revolution (1969)
12.4.4 Fourth Industrial Revolution
12.5 Reasons for Shifting Towards Industry 4.0
12.6 Role of AI in Industry 4.0
12.7 Role of ML in Industry 4.0
12.8 Role of Deep Learning in Industry 4.0
12.9 Applications of AI, ML, and DL in Industry 4.0
12.10 Challenges
12.11 Top Companies That Use AI to Augment Manufacturing Processes in the Era of Industry 4.0
12.12 Conclusion
References
13. The Implementation of AI and AI-Empowered Imaging System to Fight Against COVID-19—A Review
Sanjay Chakraborty and Lopamudra Dey
13.1 Introduction
13.2 AI-Assisted Methods
13.2.1 AI-Driven Tools to Diagnose COVID-19 and Drug Discovery
13.2.2 AI-Empowered Image Processing to Diagnosis
13.3 Optimistic Treatments and Cures
13.4 Challenges and Future Research Issues
13.5 Conclusion
References
14. Implementation of Machine Learning Techniques for the Analysis of Transmission Dynamics of COVID-19
C. Vijayalakshmi and S. Bangusha Devi
14.1 Introduction
14.2 Data Analysis
14.3 Methodology
14.3.1 Linear Regression Model
14.3.2 Time Series Model
14.4 Results and Discussions
14.4.1 Model Estimation and Studying its Adequacy
14.4.2 Regression Model for Daily New Cases and New Deaths
14.5 Conclusions
References
Index
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