Networking Technologies in Smart Healthcare: Innovations and Analytical Approaches

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
About the book
Preface
About the editors
Contributors
Chapter 1: Smart healthcare in smart city using wireless body area network and 5G
1.1 Introduction
1.2 Literature review
1.3 Smart healthcare solution using 5G network architecture
1.4 5G Smart healthcare using wireless body area network (WBAN)
1.5 Allocated frequency bands for WBAN
1.6 Low-power wide area network communication systems in WBANs
1.6.1 Description of LPWAN communication systems
1.7 Classification of 5G smart healthcare
1.8 Data transmission methodologies for smart healthcare
1.8.1 Specifications of smart healthcare
1.8.2 Purposes of smart healthcare
1.8.3 List of parameters to enhance the performance
1.8.4 Open concerns and claims of 5G smart healthcare using WBAN
1.8.5 Security challenges in WBAN
1.9 Conclusion
References
Chapter 2: Information theory-based fuzzy logic optimization of medical data analytics during the COVID-19 pandemic
2.1 Introduction
2.1.1 Motivation
2.1.2 Organization of the chapter
2.2 Related works
2.3 Proposed fuzzy logic system
2.4 Simulation results and discussion
2.5 Conclusion and future work
References
Chapter 3: Internet of things implementation and challenges during COVID-19 in healthcare industries
3.1 Introduction
3.2 IoT-based healthcare architecture
3.3 IoT technologies for the healthcare during COVID-19 pandemic
3.3.1 Implementation technologies for IoT
3.3.2 IoT technologies for healthcare during the COVID-19 outbreak
3.3.3 COVID-19 IoT processes
3.3.4 Data collection and review of reports
3.3.5 Analysis of the data received and controlling
3.3.6 Virtual management
3.4 Health monitoring
3.5 IoT stakeholders
3.6 Applications of advanced technologies in the medical field
3.6.1 Medical imaging for COVID-19 patients
3.6.2 Using clinical data to track a patient’s condition
3.6.3 Drug development, selection, and delivery
3.6.4 Modeling and prediction of virus propagation
3.6.5 COVID-19 syndromes early prediction or detection
3.6.6 Protecting healthcare workers
3.7 IoT applications during COVID-19
3.7.1 Current contact-tracing mechanisms
3.7.2 COVID-19 diagnosis using IoT
3.7.3 During COVID-19 IoT telemedicine services
3.7.4 COVID-19 prediction using IoT-enabled wearable technologies
3.7.5 Drone and unmanned aerial vehicle to fight the pandemic
3.7.6 Significant applications of IoT for COVID-19 pandemic
3.7.6.1 Treatment of COVID-19 patient
3.7.6.2 Smart hospital
3.7.6.3 COVID-19 patient data storage
3.7.6.4 Acquire information from various sources and devices
3.7.6.5 Accurate decision-making
3.7.6.6 Focus on COVID-19 patient’s condition
3.7.6.7 Warning about the COVID-19 disease
3.7.6.8 Details for the healthcare professional
3.7.6.9 Proper medication
3.7.6.10 Proper facilities
3.7.6.11 Assess level of glucose
3.7.6.12 Assist in remote areas
3.7.6.13 Assessment of an asthma attack
3.7.6.14 Reminder about medication time
3.7.6.15 Emergency case
3.7.6.16 Smart bed
3.8 Potential risks of IoT
3.8.1 Enhancing the privacy and security of AI and IoT techniques
3.9 Significant advantages of IoT in healthcare
3.10 Challenges of IoT in healthcare
3.11 Conclusion
References
Chapter 4: Internet of medical things: Reduced energy consumption and data storage
4.1 Introduction
4.2 IoT energy consumption
4.2.1 History of the IoT
4.2.2 Application
4.3 IoT technology
4.3.1 RFID
4.3.2 WSN
4.3.3 Cloud computing
4.3.4 Middleware
4.4 Network architecture
4.4.1 Consumption
4.4.2 Storage
4.4.3 Contribution of our solution
4.5 Conclusion
Glossary
References
Chapter 5: Communication and detection of sanitation solutions during epidemics
5.1 Introduction
5.2 Types and concepts
5.2.1 Basic sanitation
5.2.2 Container-based sanitation
5.2.3 Sanitation guided by the community
5.2.4 Sanitation in the absence of water
5.2.5 Ecological sanitation
5.2.6 Sanitation in an emergency
5.2.7 Environmental sanitation
5.2.8 Sanitation, both improved and unimproved
5.2.9 Lack of sanitation
5.2.10 Onsite sanitation
5.2.11 Safely managed sanitation
5.2.12 Sustainable sanitation
5.3 Environmental considerations
5.3.1 Indicator organisms
5.3.2 Climate change
5.4 What is epidemics and how to react during epidemics?
5.5 Types of epidemics
5.5.1 Common source outbreak
5.5.2 Propagated outbreak
5.5.3 Transmission
5.5.4 Potential sanitation solutions during an emergency response
5.6 Sanitation solutions
5.6.1 Packet latrines
5.6.2 Bucket latrines or elevated toilets
5.6.3 Chemical toilets
5.6.4 Trench latrines
5.6.5 Communal or family pit latrines with short-term structure
5.6.6 Ecological sanitation (Eco-San) latrines
5.7 Sanitation recommendations for cholera-prone areas and improvised settlements
5.8 Awareness among public related to sanitation and hygiene issues
5.9 Actions to take during, before, and after a pandemic
5.10 Risk communication – a life-saving action in public health emergencies
5.11 Ten things to know and do
5.12 Conclusion & discussion
References
Chapter 6: Security and privacy on the internet of medical things: Challenges and issues
6.1 Introduction
6.2 Related work
6.3 IoMT security challenges
6.4 IoMT privacy and security issues
6.4.1 Confidentiality attacks
6.4.2 Data integrity
6.4.3 Availability attacks
6.4.4 Authentication attacks
6.4.5 Privacy attacks
6.4.6 Implementation attacks
6.4.7 Malware attacks
6.5 Taxonomy of current security methods for IoMT
6.5.1 User/device access control
6.5.2 Authentication and encryption
6.5.3 Key management
6.5.4 Intrusion detection systems (IDS)
6.5.5 Blockchain technology
6.6 Conclusion
References
Chapter 7: Impact of Industry 4.0 and Healthcare 4.0 for controlling the various challenges related to healthcare industries
7.1 Introduction
7.1.1 Industry
7.1.2 Pillars of Industry 4.0
7.2 Healthcare 4.0
7.2.1 Technology trends in Healthcare 4.0
7.3 Impact of technological developments on healthcare systems
7.4 Challenges of technology in healthcare
7.5 Digital user experience
7.6 Quality assurance
7.7 Conclusion
References
Chapter 8: A review on healthcare data privacy and security
8.1 Introduction
8.2 Related work
8.3 The regulatory aspect for preserving security in the healthcare
8.4 Known threats and risks to data security
8.4.1 Cyberattacks
8.4.2 Insider threats
8.4.3 Phishing
8.4.4 Distributed denial-of-service threats
8.4.5 Identity theft
8.4.6 Loss of data
8.4.7 Weak link in security infrastructure
8.4.8 Viruses and worms
8.4.9 Botnets
8.4.10 Ransomware
8.5 Cloud computing and healthcare
8.5.1 Cloud models in healthcare
8.5.1.1 Public cloud
8.5.1.2 Private cloud
8.5.1.3 Community cloud
8.5.1.4 Hybrid cloud
8.5.2 Opportunities in healthcare via cloud computing
8.5.2.1 Management aspect
8.5.2.2 Technology aspect
8.5.2.3 Legal aspect
8.5.2.4 Security aspect
8.5.3 Challenges in healthcare via cloud computing
8.5.3.1 Management aspect
8.5.3.2 Technology aspect
8.5.3.3 Security aspect
8.5.3.4 Legal aspect
8.5.4 Cloud computing scandals
8.5.5 Cloud security alliance
8.5.6 Problems in cloud computing
8.6 Best practices for cyber security in healthcare
8.6.1 Devising cybersecurity strategies
8.6.2 Data encryption (DE)
8.6.3 Cyber security awareness training
8.6.4 Adopting the right cybersecurity defenses
8.7 Alternatives for data protection in healthcare
8.7.1 Blockchain
8.7.1.1 Where blockchains are used in healthcare
8.7.1.2 Master patient index (MPI)
8.7.1.3 Remote patient monitoring
8.8 Conclusion
References
Chapter 9: Breast cancer detection using microwave imaging
9.1 Introduction
9.2 Property of breast tissues at microwave
9.3 Detection of breast cancer using MI
9.4 Challenges of using MI
9.5 Conclusion
References
Chapter 10: IoT implementation and challenges in healthcare industries
10.1 Introduction
10.2 Related work
10.3 Internet of things in the healthcare industry
10.3.1 IoT for patients
10.3.2 IoT for doctors
10.3.3 IoT for hospitals
10.4 IoT in India
10.5 Implementation of IoT in healthcare
10.5.1 Sensors
10.5.2 Edge gateways
10.5.3 Cloud management software
10.6 Challenges in the healthcare system imposed by IoT
10.6.1 Data security
10.6.2 Privacy
10.6.3 Integration to follow multiple protocols
10.6.3.1 Mesh protocols
10.6.3.2 Bluetooth
10.6.3.3 WiFi
10.6.3.4 LOW-POWER WIDE-AREA NETWORK (LPWAN)
10.6.4 Accuracy
10.6.5 Increase in vulnerability
10.6.6 Regulation
10.6.7 Compatibility
10.6.8 Bandwidth
10.7 Advantages of IoT
10.7.1 Less risk of error
10.7.2 Cost-effective
10.8 Ambient-assisted living (AAL)
10.9 Mobile IoT
10.10 Wearable devices
10.11 Blockchain
10.12 Child health information (CHI)
10.13 Conclusion
References
Chapter 11: Cloud-based artificial intelligence in healthcare systems
11.1 Introduction
11.2 Health cloud computing opportunities and challenges
11.3 Management aspects of cloud
11.4 Technical aspects of cloud
11.5 Security aspects of cloud in healthcare
11.5.1 Legal aspects on cloud in healthcare
11.6 Benefits of cloud computing in healthcare
11.6.1 Intelligent visions
11.6.2 Anticipative service
11.6.3 Higher transparency in system
11.6.4 Complex picture
11.6.5 Working with big data
11.6.6 Accurate decision-making and treatment
11.6.7 Cost-effectiveness
11.6.8 Great flexibility
11.7 Cloud computing issues in healthcare
11.7.1 Absence of good specialists
11.7.2 Limited functionality
11.7.3 Security issues
11.8 AI application areas in healthcare
11.9 Some representative healthcare systems based on cloud
11.10 Conclusion
References
Chapter 12: Nanomedicine in healthcare: Impact and challenges for future generation
12.1 Introduction
12.2 Classification of nanomaterials
12.3 Components of nanomedicine in health service
12.3.1 Nanoparticles for diagnosis
12.3.2 Nanoparticles for therapy
12.3.3 Nanoparticles for monitoring
12.4 Role of biosensors in nanomedicine
12.4.1 Components of biosensors
12.4.2 Classification of biosensors
12.4.3 Characteristics of biosensors
12.4.4 Nanobiosensors
12.4.5 Classification of nanobiosensors
12.4.6 Applications of nanobiosensors
12.5 Nanomedicine domains
12.5.1 Drug delivery
12.5.1.1 Medical implants
12.5.1.2 Nanobionics
12.6 Challenges of nanomedicine
12.7 Conclusion and future perspectives
References
Chapter 13: IoT for healthcare system: Challenges and opportunities
13.1 Introduction
13.2 Implementation of IoT
13.2.1 IoT in manufacturing sector
13.2.2 IoT in automobile sector
13.3 Architecture of IoT
13.4 Directly implementable IoT applications in healthcare industry
13.4.1 Use of sensors
13.5 Implementation of IoT in healthcare industry
13.5.1 Implementation advantages for patients
13.5.2 Implementation advantages for doctors
13.5.3 Implementation advantages for hospitals
13.5.4 Implementation advantages for government
13.6 IoT advancements in India
13.7 Future scope of IoT advancements in healthcare
13.7.1 Hologram-based patient interaction
13.7.2 Remote robotic surgeries
13.7.3 Advantages of IoT in healthcare
13.8 Shortcomings of IoT in healthcare industry
13.9 Conclusion
References
Chapter 14: Automatic heart-rate measurement using facial video
14.1 Introduction
14.2 Technical approach
14.2.1 Introduction to Haar-like features
14.2.1.1 Rectangular Haar-like feature
14.2.2 Software used
14.2.2.1 Python (programming language)
14.2.2.2 PyCharm
14.2.2.3 OpenCV
14.3 Brief literature survey
14.4 Experimental setup
14.4.1 Methods of detection of heart rate
14.4.2 Face detection and tracking
14.4.3 Region of interest selection
14.4.4 Object tracking
14.4.5 System model for heart rate measurement
14.4.6 Implementation
14.5 Results
14.5.1 Testing environment
14.6 Conclusion
References
Chapter 15: An intelligent model for coronary heart disease diagnosis
15.1 Introduction
15.2 Related work
15.3 Problem findings
15.3.1 Objective of the proposed CNN model
15.3.2 Data acquisition
15.4 Proposed methodology
15.4.1 Design of neural network
15.4.2 Work plan
15.4.2.1 Knowledge acquisition
15.4.2.2 Image pre-processing
15.4.2.3 Pre-processing for creating model
15.4.2.4 System requirements
15.4.2.5 CPU specifications
15.4.2.6 Evaluation procedure
15.4.3 Architecture of proposed CNN model
15.4.4 Training the model
15.5 Results and discussions
15.6 Conclusion
References
Chapter 16: Wireless networks and communication-based optimization algorithms for smart healthcare devices
16.1 Introduction
16.2 Machine learning techniques
16.2.1 Supervised learning
16.2.2 Unsupervised learning
16.2.3 Reinforcement learning
16.3 Related work
16.4 Routing layer
16.5 Applications of machine learning in routing
16.6 Routing in WSN
16.7 Communication-based optimization (CBO) algorithms
16.8 Conclusion and future scope
References
Chapter 17: An IoT approach of managing smart healthcare services
17.1 Introduction
17.2 Wearable devices
17.3 Internet of healthcare things (IoHT)
17.4 Communication protocol
17.4.1 Limitations of IPv4
17.4.2 Solving the NAT barrier
17.4.3 Strong security enablers
17.4.4 Tiny stacks available
17.4.5 Enabling the extension of the internet to the web of things
17.4.6 Mobility
17.4.7 Address self-configuration
17.4.8 Fully internet compliant
17.4.9 Comparison between IPv4 and IPv6
17.5 QoS for IoHT network
17.6 Other challenges for IoHT network
17.7 Discussion and conclusion
References
Index