Advances in Fog Computing and the Internet of Things for Smart Healthcare.

Format:

Book

Author/Creator:

Awotunde, Joseph Bamidele.

Language:

English

Subjects (All):

Internet of things.

Medical informatics.

Physical Description:

1 online resource (559 pages)

Edition:

1st ed.

Place of Publication:

Chantilly : Elsevier, 2025.

Summary:

Advances in Fog Computing and the Internet of Things for Smart Healthcare delves into the transformative impact of fog computing and IoT on modern healthcare systems.This comprehensive guide educates researchers and graduate students on the fundamental concepts of these technologies, illustrating their practical applications in healthcare.

Contents:

Front Cover

Front Matter

Title page

Copyright

Contents

Contributors

Biography

Preface

Acknowledgments

Chapter 1 Introductory chapter

1.1 Introduction

1.2 The digital healthcare paradigm

1.3 Key components of the digital healthcare paradigm

1.3.1 Telemedicine and telehealth

1.3.2 Wearable devices and remote monitoring

1.3.3 Artificial intelligence and machine learning

1.3.4 Electronic health records

1.3.5 Digital therapeutics and health apps

1.4 Introduction to fog computing

1.4.1 Key features of fog computing in smart healthcare systems

1.5 Fundamentals of internet of things systems in healthcare

1.5.1 The internet of things devices in smart healthcare systems

1.5.2 Benefits of internet of things in smart healthcare systems

1.6 Relevance of fog computing and internet of things in smart healthcare systems

1.6.1 Synergy between internet of things and fog computing in smart healthcare

1.7 Conclusion and future scope

References

Chapter 2 An intelligent framework to secure fog computer and IoT service applications using artificial intelligence/machine learning

2.1 Contributions of the chapter

2.2 Introduction

2.3 Overview of fog computing and internet of things

2.4 Explanation of fog computing architecture and its role in IoT

2.5 Key components and characteristics of fog computing and internet of things systems

2.6 Security implications and risks associated with fog computing and IoT

2.7 Security challenges in fog computing and internet of things

2.8 Intelligent framework design

2.9 Description of the proposed intelligent framework for securing fog computing and IoT service applications

2.10 Integration of AI and ML techniques for threat detection

2.11 Threat detection and anomaly detection

2.11.1 Threat detection.

2.11.2 Anomaly detection

2.12 Predictive analytics for security risk mitigation

2.13 Automated security responses

2.14 Evaluation and performance analysis

2.15 Case studies and use cases

2.16 Discussion and future directions

2.17 Conclusion

Chapter 3 Optimization schemes for fog computing and internet of things in smart healthcare systems

3.1 Introduction

3.2 Fog computing and internet of things in healthcare

3.2.1 Features of fog computing and internet of things in healthcare

3.2.2 Existing challenges of fog computing and internet of things in healthcare

3.3 Optimization techniques for fog computing and internet of things

3.3.1 Optimization techniques

3.3.2 Optimization models for fog computing and internet of things in smart healthcare systems

3.3.3 Optimized resource allocation model for fog computing and internet of things-driven intensive care unit monitoring systems

3.3.4 Problem description

3.3.5 Mathematical formulation

3.3.6 Benefits of fog computing and internet of things in healthcare systems

3.3.7 Real-time data processing and analysis

3.3.8 Support for remote and rural healthcare

3.3.9 Improved patient monitoring

3.3.10 Reduced bandwidth usage and cost-efficiency

3.3.11 Scalable and flexible infrastructure

3.3.12 Enhanced data privacy and security

3.3.13 Support for AI-driven diagnostics

3.3.14 Seamless collaboration

3.3.15 Integration with artificial intelligence and machine learning

3.4 Applications of fog computing and internet of things

3.4.1 Healthcare

3.4.2 Smart homes

3.4.3 Industrial internet of things

3.5 Challenges of implementing optimization schemes in fog computing and internet of things

3.5.1 Resource allocation and management

3.5.2 Scalability issues

3.5.3 Latency versus computational complexity trade-off.

3.5.4 Interoperability across diverse systems

3.5.5 Energy efficiency and sustainability

3.6 Future research directions of fog computing and internet of things

3.6.1 Emerging technologies

3.6.2 Personalized healthcare

3.6.3 6G generation and beyond

3.6.4 Sustainability

3.7 Conclusion

Chapter 4 An intelligent healthcare framework for automated disease diagnosis using ensemble deep learning in internet of things-integrated fog computing environments

4.1 Introduction

4.1.1 Problem statement

4.2 Internet of things for continuous patient data collection

4.2.1 Specific applications in chronic disease management

4.2.2 Data collection and transmission mechanisms

4.2.3 Methodologies employed by internet of things devices

4.2.4 Significance of seamless data flow

4.2.5 Challenges in internet of things deployment for healthcare

4.2.6 Common challenges

4.2.7 Strategies for overcoming challenges

4.3 Fog computing for real-time healthcare processing

4.3.1 Fog devices

4.3.2 Understanding fog computing and its importance

4.3.3 Real-time data processing at the edge

4.3.4 Advantages of fog computing in healthcare systems

4.3.5 Challenges in deploying fog nodes

4.3.6 Strategies to tackle challenges

4.4 Ensemble deep learning-based smart healthcare system

4.5 Ensemble deep learning for disease diagnosis

4.5.1 Ensemble learning

4.5.2 Deep learning models in healthcare

4.5.3 Advantages of ensemble learning in healthcare systems

4.5.4 Use cases: Applying ensemble deep learning for disease diagnosis

4.6 Data preprocessing techniques

4.6.1 Importance of data preprocessing in healthcare systems

4.6.2 Advanced data preprocessing methods

4.6.3 Impact on diagnostic accuracy

4.7 Security and privacy in smart healthcare systems.

4.7.1 Data privacy concerns in healthcare

4.7.2 Strategies for securing internet of things and fog-based healthcare systems

4.8 Scalability and resource management in healthcare systems

4.8.1 Challenges of scaling smart healthcare solutions

4.8.2 Strategies for resource management

4.9 Future directions: Integrating 6G and advanced artificial intelligence models

4.9.1 The potential of 6G networks in healthcare

4.9.2 Advanced artificial intelligence models for diagnosis

4.9.3 Opportunities for research and innovation

4.10 Implications for healthcare providers and patients

4.10.1 Transformative impact on healthcare delivery

4.10.2 Potential for personalized medicine

4.10.3 Adoption challenges and recommendations

Chapter 5 A smart internet of medical things healthcare framework using machine learning classifier in fog processing system

5.1 Introduction

5.2 Background

5.3 Suggested approach

5.3.1 Exploring sleep disorder visualizations

5.3.2 Tracking apnea in sleep

5.3.3 A new approach to IoMT-fog computation

5.4 Findings and evaluation

5.5 Conclusion

Chapter 6 Applications of fog computing, internet of things, and cloud computing in healthcare multilayer networks

6.1 Fog versus cloud computing: A multilayer network interpretation

6.2 Fog computing and healthcare

6.3 Fog computing and internet of things devices

6.4 Architecture and fog/cloud computing framework

6.5 A mathematical interpretation of fog computing multilayer networks

6.6 Healthcare monitoring

6.7 Big data

6.8 Artificial intelligence

6.9 Fog computing and internet of things copula nodes in smart healthcare multilayer networks: Scalable cost/benefit analysis.

6.10 Link between fog computing, internet of things, interoperable multilayer networks, and patient-centered smart healthcare

6.11 Conclusion

Chapter 7 A zero trust security framework for fog-enabled internet of things (IoT) environment

7.1 Introduction

7.2 Brief overview of fog computing and internet of things

7.2.1 Fog computing

7.2.2 Differences between cloud computing with and without fog nodes

7.2.3 The need for enhanced security in fog-enabled internet of things ecosystems

7.3 Introduction to the zero trust security model

7.3.1 Zero trust fundamentals

7.3.2 Implementation of zero trust network architecture and the core principles in fog computing

7.4 Principle 1: Least privilege

7.4.1 Role-based access control

7.4.2 Attribute-based access control

7.4.3 Micro-segmentation

7.4.4 Strong authentication

7.4.5 Principle 2: Micro-segmentation

7.4.6 Principle 3: Continuous monitoring and anomaly detection

7.4.7 Principle 4: Dynamic policy enforcement

7.5 Technical architecture for dynamic policy enforcement in fog computing

7.6 Challenges to zero trust implementation in fog computing

7.7 Future research and development

7.7.1 Blockchain integration for enhanced trust in fog computing

7.7.2 Load balancing in fog computing

7.7.3 Scalability in fog computing

7.7.4 Service Orchestration in Fog Computing

7.7.5 Zero trust maturity assessment tools in fog computing

Chapter 8 Sensor network and blockchain platform for the future smart healthcare system

8.1 Introduction

8.2 Leveraging the divide and conquer algorithm to deploy access point

8.2.1 First dividing step

8.2.2 2-2 Dividing step

8.3 Group number-based network

8.3.1 Initial group number setting

8.4 Data transmission path

8.5 Group number resetting

8.6 Conclusion.

References.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

0-443-33442-0

9780443334429

OCLC:

1552585354