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Reimagine the future of healthcare with a deep dive into hyperscale computing and distributed networks
In AI-Driven Smart Healthcare: Powered by Hyperscale Computing and Next Generation Networks, a team of distinguished researchers delivers an insightful and practical discussion of the healthcare applications of artificial intelligence and fog-enabled next-generation networks. The book provides practical insights and methodologies for the design, development, and deployment of these technologies throughout the healthcare industry.
Readers will explore key areas of recent advancement, including the Internet of Things, fog computing, artificial intelligence, machine learning, serverless computing, and blockchain in a way that allows them to assess the feasibility and scalability of a variety of technological healthcare solutions.
The book also includes:
Perfect for researchers, practitioners, and other healthcare stakeholders, AI-Driven Smart Healthcare will also benefit technologists, educators, hospital administrators, and other professionals with an interest in the application of the latest technologies to recurrent and significant issues in the field of healthcare.
Ajay Pratap is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (BHU), Varanasi, India.
Yashwant Singh Patel is a Postdoctoral Fellow in the Department of Computing Science in Umeå University, Sweden and an Assistant Professor with the Computer Science and Engineering Department (CSED) at Thapar Institute of Engineering & Technology, Punjab, India.
Ram Narayan Yadav is an Assistant Professor in the Electrical and Computer Science Engineering department at the Institute of Infrastructure Technology Research and Management in Ahmedabad, India.
Ali Ahmadian is a Senior Research Scientist at the Decisions Lab, Mediterranean University of Reggio Calabria, Italy and an Adjunct Professor with the Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul, Turkey.
Ashok Kumar Yadav is an Assistant Professor in the Department of Information Technology at the Rajkiya Engineering College in Azamgarh, Uttar Pradesh, India.
Notes on Contributors xv Preface xvii Acknowledgments xix List of Abbreviations xxi Introduction xxv
1 Internet of Things, Edge, Fog, and Data Analytics in Smart Healthcare: Introduction, Benefits, and Challenges 1 1.1 Introduction 1 1.2 Use of Edge and Fog Computing for Healthcare Applications 3 1.2.1 Role of Edge Computing in Resource Management 7 1.3 Data Analytics in Healthcare Applications 8 1.3.1 Components of BDA 9 1.4 BDA Applications 10 1.5 Use Case Scenarios 13 1.6 Current Challenges and Future Directions 17 1.7 Future Direction: Opening Up Health Data for Research 19 1.8 Conclusion 20
2 Hyperscale Computing Paradigm in Healthcare 29 2.1 Introduction 29 2.2 The Evolution of Computing in Healthcare 30 2.3 What Is Hyperscale Cloud? 32 2.4 Components of Hyperscale Computing 32 2.5 Challenges of Hyperscale Computing in Healthcare 37 2.6 Hyperscale Data Centers 39 2.7 Tech Giants Playing a Role in Hyperscaling 40 2.8 Public Versus Private Hyperscale Clouds in Healthcare 42 2.9 Depth and Future of Hyperscale Computing in Healthcare 42 2.10 Case Studies of Hyperscale Computing in Healthcare 45
3 Containerized Internet of Medical Things and Serverless Computing for Smart Healthcare Systems 49 3.1 Wireless Technologies Empowering Internet of Medical Things 49 3.2 IoMT and Its Role in Modern Healthcare 50 3.3 Importance of Containerization 52 3.4 Serverless Computing: Concept and Overview 54 3.5 Complementing Containerization for Healthcare Applications 55 3.6 Real-world Use Cases 57 3.7 Future Directions in Containerized IoMT and Serverless Healthcare 58 3.8 Conclusion 58
4 Kubernetes Enabled Resource Management Architecture for Secure Innovation in Healthcare 61 4.1 Overview of Kubernetes and Its Role in Healthcare 61 4.2 Key Features of Kubernetes 62 4.3 Key Kubernetes Concepts 63 4.4 Kubernetes Control Plane and Nodes 64 4.5 Kubernetes Resource Management 65 4.6 Benefits of Kubernetes for the Healthcare Industry 66 4.7 Use Cases of Cloud-native and Kubernetes in Healthcare 67 4.8 Kubernetes as a Solution 68 4.9 Conclusion 70
5 Exploring Artificial Intelligence (AI) and Machine Learning (ML) for Performance and Predictive Analysis of Various Diseases Using Health-related Data 73 5.1 Introduction 73 5.2 Challenges in Healthcare 75 5.3 Significance of AI and ML in Healthcare 76 5.4 Application of AI/ML in Healthcare System 78 5.5 Major Development Phases of AI/ML-based Healthcare Systems 81 5.6 Secure, Private, and Robust AI/ML-based Healthcare: Challenges 85 5.7 Use Case: Diabetes 87 5.8 Challenges and Future Directions 93 5.9 Conclusion 93
6 Algorithmic Frameworks for Cost Minimization in Criticality Aware Mobile Healthcare System 101 6.1 Introduction 101 6.2 Related Works 102 6.3 System Model 104 6.4 Problem Definition 108 6.5 Proposed Auction Mechanism 111 6.6 Analysis of Proposed Mechanism 117 6.7 Performance Study 120 6.8 Conclusion 124
7 Utility-aware Edge Computing System for Remote Health Monitoring 127 7.1 Introduction 127 7.2 Related Works 129 7.3 System Model and Problem Formulation 131 7.4 Proposed Auction Model 136 7.5 Analysis of Proposed Auction Model 142 7.6 Performance Evaluation 143 7.7 Conclusion 148
8 Fog Computing-based WBAN and IoT Framework for Prediction of Various Diseases Using Big Data Analytics 151 8.1 Introduction 151 8.2 The Role of Fog Computing in Healthcare 153 8.3 Big Data Analytics in Healthcare 153 8.4 Wireless Body Area Networks in Healthcare 155 8.5 Framework Design 156 8.6 Disease Prediction Use Case for Healthcare 158 8.7 Conclusion 163
9 Disease Spread Detection and Controlling with Fog-based Model in Wireless Body Area Networks 167 9.1 Compartmental Model 169 9.2 Simulation Tools 174 9.3 Other Environmental Factors to Control Spread of Disease 177 9.4 AI- and ML-based Health Prediction Approaches 178 9.5 Conclusion 180
10 Optimized Doctor Recommendation System Using Machine Learning Approach 185 10.1 Introduction 185 10.2 Related Works 187 10.3 System Model 190 10.4 Proposed Approach 195 10.5 Performance Study 201 10.6 Conclusion and Future Work 204
11 UAV-enabled Smart Healthcare Application for Next-generation Wireless Networks 209 11.1 Introduction 209 11.2 Related Works 211 11.3 System Model 213 11.4 Federated Deep Reinforcement Learning 223 11.5 A Direction for Performance Study 230 11.6 Conclusion 232
12 A Road Map for Personalized Medicine: Challenges and Innovations 239 12.1 Introduction 239 12.2 Use of Genome Data to Develop Personalized Medicine 243 12.3 Use of Medical Imaging Data to Develop Personalized Medicine 245 12.4 Use of AI and ML in Drug Development in Personalized Medicine 247 12.5 Use of Digital Twin for Personalized Medicine 251 12.6 Current Challenges and Future Directions 253
13 Delay-sensitive, Privacy-preserving Blockchain-enabled Fog-assisted Framework for Smart Healthcare 263 13.1 Introduction 263 13.2 Related Work 265 13.3 System Model 267 13.4 Problem Formulation 271 13.5 Proposed Solution 278 13.6 Experimental Results 286 13.7 Conclusion 294
A Research Discussion, Tools, and Use Cases 299 A.1 Artificial Intelligence in Smart Healthcare 299 A.2 Research Trends in AI for Healthcare 300 A.3 Use Cases of AI in Smart Healthcare 300 A.4 Key Tools and Frameworks for AI-driven Healthcare 309 A.5 Challenges and Limitations in AI-driven Healthcare 312 A.6 Conclusion 313
References 314 Index 319
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